EVA_London_2022_SNN_tutorial.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "EVA London 2022 SNN tutorial.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "e0d4cc84253044d4b5b9910354aa9dbe": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_2da3026d7cf14952b7c08415cc9f233d",
              "IPY_MODEL_b7c91feef67e417e8e5beb7a10198aac",
              "IPY_MODEL_597b495d0a9e4ed8867dede2cc9635ce"
            ],
            "layout": "IPY_MODEL_ea904b141e984cc1960223c2df78c4a5"
          }
        },
        "2da3026d7cf14952b7c08415cc9f233d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "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_e5747048ac074c0e90e2025d42be5163",
            "placeholder": "​",
            "style": "IPY_MODEL_72d63239baad45cc97ab7f16904752db",
            "value": ""
          }
        },
        "b7c91feef67e417e8e5beb7a10198aac": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e54ebe196dcc4a59a80ffbc90ff2b0c4",
            "max": 9912422,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_5ce347a78c94405e962e062c4a272ca6",
            "value": 9912422
          }
        },
        "597b495d0a9e4ed8867dede2cc9635ce": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "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_604b47406d474196a588c11129d4a147",
            "placeholder": "​",
            "style": "IPY_MODEL_1504865cd2b54cc2923f118500793ffe",
            "value": " 9913344/? [00:00&lt;00:00, 18545287.48it/s]"
          }
        },
        "ea904b141e984cc1960223c2df78c4a5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e5747048ac074c0e90e2025d42be5163": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "72d63239baad45cc97ab7f16904752db": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "e54ebe196dcc4a59a80ffbc90ff2b0c4": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5ce347a78c94405e962e062c4a272ca6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "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": ""
          }
        },
        "604b47406d474196a588c11129d4a147": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1504865cd2b54cc2923f118500793ffe": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "7be139a084ed4b3792a2b12b094d6b7a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_e6b140dccb644f86b4b92a487b155f20",
              "IPY_MODEL_cdc752644f914b6eb66fdb3c20af3b4a",
              "IPY_MODEL_9ff00cd4cbf349969405e63911047e4b"
            ],
            "layout": "IPY_MODEL_e28f3f7a0de54b5cb3a9ab2ab0eeda20"
          }
        },
        "e6b140dccb644f86b4b92a487b155f20": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "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_006c0b929c164481bf0ad18c82871bc6",
            "placeholder": "​",
            "style": "IPY_MODEL_a2c16cd9c4c74e889e530fd73d416edd",
            "value": ""
          }
        },
        "cdc752644f914b6eb66fdb3c20af3b4a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_47feb4e571654c3082f5bc4aae6028e4",
            "max": 28881,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_e122a3d844ec4c3c98103e263b22422c",
            "value": 28881
          }
        },
        "9ff00cd4cbf349969405e63911047e4b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "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_c6957e94ff884734ab680c1495695885",
            "placeholder": "​",
            "style": "IPY_MODEL_a47da426ebd841dd95697d4088cdfa6d",
            "value": " 29696/? [00:00&lt;00:00, 5102.15it/s]"
          }
        },
        "e28f3f7a0de54b5cb3a9ab2ab0eeda20": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "006c0b929c164481bf0ad18c82871bc6": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a2c16cd9c4c74e889e530fd73d416edd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "47feb4e571654c3082f5bc4aae6028e4": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e122a3d844ec4c3c98103e263b22422c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "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": ""
          }
        },
        "c6957e94ff884734ab680c1495695885": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a47da426ebd841dd95697d4088cdfa6d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "79e2100535df4afbbf3900c05e7598ff": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_59691836a1d64668b73e816591636933",
              "IPY_MODEL_5d8d29b0e73342288ba6f9a8a0a63a75",
              "IPY_MODEL_5b23189b7c6742a4ab814040507c07fc"
            ],
            "layout": "IPY_MODEL_a91b3c55cd7249e4b3550e325b49a612"
          }
        },
        "59691836a1d64668b73e816591636933": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "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_ece9da4cfc534a3882022d34cba08a55",
            "placeholder": "​",
            "style": "IPY_MODEL_9dd6336c1b3b4ac3a8d0e4b1cf233877",
            "value": ""
          }
        },
        "5d8d29b0e73342288ba6f9a8a0a63a75": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_195e7ef8974b4296a1ecda78466dade7",
            "max": 1648877,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_f04b98daa27c4e41ad5615c70fd3ddfd",
            "value": 1648877
          }
        },
        "5b23189b7c6742a4ab814040507c07fc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "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_779b77961a734d0f8f95b6482b680e71",
            "placeholder": "​",
            "style": "IPY_MODEL_f6f3e718891f4dc59330b5a29ab70601",
            "value": " 1649664/? [00:00&lt;00:00, 1134409.75it/s]"
          }
        },
        "a91b3c55cd7249e4b3550e325b49a612": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ece9da4cfc534a3882022d34cba08a55": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9dd6336c1b3b4ac3a8d0e4b1cf233877": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "195e7ef8974b4296a1ecda78466dade7": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f04b98daa27c4e41ad5615c70fd3ddfd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "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": ""
          }
        },
        "779b77961a734d0f8f95b6482b680e71": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f6f3e718891f4dc59330b5a29ab70601": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "aff660e96ce44ff2aa7156883818b7be": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_7d7a31119d60462ba1e3f95c468e6f45",
              "IPY_MODEL_03b1ba87016f4de6b7f55d659cd8d271",
              "IPY_MODEL_3152d97891914218a0044b5711ed1dee"
            ],
            "layout": "IPY_MODEL_4f2d288fdaf0416588516ae1ea872a38"
          }
        },
        "7d7a31119d60462ba1e3f95c468e6f45": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "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_034aee3231dc4562a36c8220fd6823ed",
            "placeholder": "​",
            "style": "IPY_MODEL_337bfbe5592a4fed8be18e03387d2530",
            "value": ""
          }
        },
        "03b1ba87016f4de6b7f55d659cd8d271": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_adaf13a28cbb476d9d2dd13fafc26830",
            "max": 4542,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_84f618738a404f9da4aecdc6749eef24",
            "value": 4542
          }
        },
        "3152d97891914218a0044b5711ed1dee": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "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_b9703053b0e14471aa5941c3edce0612",
            "placeholder": "​",
            "style": "IPY_MODEL_d8d295411bd54a28bac6edc417f9d38d",
            "value": " 5120/? [00:00&lt;00:00, 56285.22it/s]"
          }
        },
        "4f2d288fdaf0416588516ae1ea872a38": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "034aee3231dc4562a36c8220fd6823ed": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "337bfbe5592a4fed8be18e03387d2530": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "adaf13a28cbb476d9d2dd13fafc26830": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "84f618738a404f9da4aecdc6749eef24": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "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": ""
          }
        },
        "b9703053b0e14471aa5941c3edce0612": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d8d295411bd54a28bac6edc417f9d38d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "5706f996092c42728234deb1d7b57f70": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_fc2d2049970243b0b53a2028b5dfd99f",
              "IPY_MODEL_52a1efd49fb7472884cfc13167d8bda7",
              "IPY_MODEL_9246ce7321c548cfb0cde6c746e827f9"
            ],
            "layout": "IPY_MODEL_d09e9a2b69454d89a7d389d51f62eb0c"
          }
        },
        "fc2d2049970243b0b53a2028b5dfd99f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "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_b14d4c39933b40a69c8b2a3164246519",
            "placeholder": "​",
            "style": "IPY_MODEL_5eff40a6098d459196a9718ab58bdaae",
            "value": ""
          }
        },
        "52a1efd49fb7472884cfc13167d8bda7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_c6f5f61e4e9c4b8cb441c517e7cb0794",
            "max": 1011893601,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_e021e073d0404949a41880f64a76fa96",
            "value": 1011893601
          }
        },
        "9246ce7321c548cfb0cde6c746e827f9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "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_fd7f2cebf88746d6a4c86e4e3b323f6f",
            "placeholder": "​",
            "style": "IPY_MODEL_0a445b7e40584ca9bdad58e91fa25b27",
            "value": " 1011894272/? [00:31&lt;00:00, 34299357.81it/s]"
          }
        },
        "d09e9a2b69454d89a7d389d51f62eb0c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b14d4c39933b40a69c8b2a3164246519": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5eff40a6098d459196a9718ab58bdaae": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "c6f5f61e4e9c4b8cb441c517e7cb0794": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e021e073d0404949a41880f64a76fa96": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "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": ""
          }
        },
        "fd7f2cebf88746d6a4c86e4e3b323f6f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0a445b7e40584ca9bdad58e91fa25b27": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Data"
      ],
      "metadata": {
        "id": "Pqi_KMryneeq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Introduction\n",
        "Light is what we see when the retina converts photons into spikes. Odors are what we smell when volatilised molecules are converted into spikes. Touch is what we feel when nerve endings turn tactile pressure into spikes. The brain trades in the global currency of the *spike*. \n",
        "\n",
        "If our end goal is to build a spiking neural network (SNN), it makes sense to use spikes at the input too. Although it is quite common to use non-spiking inputs, part of the appeal of encoding data come from the *three S's*: spikes, sparsity, and static suppression.\n",
        "\n",
        "\n",
        "\n",
        "1.   **Spikes**: (a-b) Biological neurons process and communicate via spikes, which are electrical impulses of approximately 100 mV in amplitude. (c) Many computational models of neurons simplify this voltage burst to a discrete, single-bit event: a '1' or a '0'. This is far simpler to represent in hardware than a high precision value.\n",
        "\n",
        "2.   **Sparsity**: (c) Neurons spend most of their time at rest, silencing most activations to *zero* at any given time. Not only are sparse vectors/tensors (with loads of zeros) cheap to store, but say we need to multiply sparse activations with synaptic weights. If most values are multiplied by '0', then we don't need to read many of the network parameters from memory. This means neuromorphic hardware can be extremely efficient. \n",
        "\n",
        "3.   **Static-Suppression (a.k.a, event-driven processing**: (d-e) The sensory periphery only processes information when there is new information to process. Each pixel in (e) responds to *changes* in illuminance, so most of the image is blocked out. Conventional signal processing requires all channels/pixels to adhere to a global sampling/shutter rate, which slows down how frequently sensing can take place. Event-driven processing now only contributes to sparsity and power-efficiency by blocking unchanging input, but it often allows for much faster processing speeds.\n",
        "\n",
        "\n",
        "<center>\n",
        "<img src='https://github.com/jeshraghian/snntorch/blob/master/docs/_static/img/examples/tutorial1/3s.png?raw=true' width=\"600\">\n",
        "</center>"
      ],
      "metadata": {
        "id": "SUwxbqQrllbS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conversion to spike trains"
      ],
      "metadata": {
        "id": "nvBRzIzaswC-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Istallations, imports & downloads"
      ],
      "metadata": {
        "id": "uQMDUP77oBox"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4S10AhOTnXT9",
        "outputId": "504aae73-c400-4a77-ea93-42e6d5876e0c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l\r\u001b[K     |███▌                            | 10 kB 18.6 MB/s eta 0:00:01\r\u001b[K     |███████                         | 20 kB 22.5 MB/s eta 0:00:01\r\u001b[K     |██████████▋                     | 30 kB 28.1 MB/s eta 0:00:01\r\u001b[K     |██████████████▏                 | 40 kB 33.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████▊              | 51 kB 28.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▎          | 61 kB 31.1 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▉       | 71 kB 32.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▎   | 81 kB 28.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▉| 92 kB 30.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 92 kB 355 kB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install snntorch --quiet"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torchvision import datasets, transforms\n",
        "from snntorch import utils\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Define a transformation done while converting images to tensors\n",
        "transform = transforms.Compose([\n",
        "            transforms.Resize((28,28)),\n",
        "            transforms.Grayscale(),\n",
        "            transforms.ToTensor(),\n",
        "            transforms.Normalize((0,), (1,))])\n",
        "\n",
        "# Download a dataset while applying a transformation\n",
        "mnist_train = datasets.MNIST('/data/mnist', train=True, download=True, transform=transform)\n",
        "\n",
        "# Take 1/10 subset of 60,000 images in the dataset\n",
        "mnist_train = utils.data_subset(mnist_train, 10)\n",
        "print(f\"The size of mnist_train is {len(mnist_train)}\")\n",
        "\n",
        "# Create a dataloader that will load the data in chunks of the defined batch size\n",
        "train_loader = DataLoader(mnist_train, batch_size=128, shuffle=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440,
          "referenced_widgets": [
            "e0d4cc84253044d4b5b9910354aa9dbe",
            "2da3026d7cf14952b7c08415cc9f233d",
            "b7c91feef67e417e8e5beb7a10198aac",
            "597b495d0a9e4ed8867dede2cc9635ce",
            "ea904b141e984cc1960223c2df78c4a5",
            "e5747048ac074c0e90e2025d42be5163",
            "72d63239baad45cc97ab7f16904752db",
            "e54ebe196dcc4a59a80ffbc90ff2b0c4",
            "5ce347a78c94405e962e062c4a272ca6",
            "604b47406d474196a588c11129d4a147",
            "1504865cd2b54cc2923f118500793ffe",
            "7be139a084ed4b3792a2b12b094d6b7a",
            "e6b140dccb644f86b4b92a487b155f20",
            "cdc752644f914b6eb66fdb3c20af3b4a",
            "9ff00cd4cbf349969405e63911047e4b",
            "e28f3f7a0de54b5cb3a9ab2ab0eeda20",
            "006c0b929c164481bf0ad18c82871bc6",
            "a2c16cd9c4c74e889e530fd73d416edd",
            "47feb4e571654c3082f5bc4aae6028e4",
            "e122a3d844ec4c3c98103e263b22422c",
            "c6957e94ff884734ab680c1495695885",
            "a47da426ebd841dd95697d4088cdfa6d",
            "79e2100535df4afbbf3900c05e7598ff",
            "59691836a1d64668b73e816591636933",
            "5d8d29b0e73342288ba6f9a8a0a63a75",
            "5b23189b7c6742a4ab814040507c07fc",
            "a91b3c55cd7249e4b3550e325b49a612",
            "ece9da4cfc534a3882022d34cba08a55",
            "9dd6336c1b3b4ac3a8d0e4b1cf233877",
            "195e7ef8974b4296a1ecda78466dade7",
            "f04b98daa27c4e41ad5615c70fd3ddfd",
            "779b77961a734d0f8f95b6482b680e71",
            "f6f3e718891f4dc59330b5a29ab70601",
            "aff660e96ce44ff2aa7156883818b7be",
            "7d7a31119d60462ba1e3f95c468e6f45",
            "03b1ba87016f4de6b7f55d659cd8d271",
            "3152d97891914218a0044b5711ed1dee",
            "4f2d288fdaf0416588516ae1ea872a38",
            "034aee3231dc4562a36c8220fd6823ed",
            "337bfbe5592a4fed8be18e03387d2530",
            "adaf13a28cbb476d9d2dd13fafc26830",
            "84f618738a404f9da4aecdc6749eef24",
            "b9703053b0e14471aa5941c3edce0612",
            "d8d295411bd54a28bac6edc417f9d38d"
          ]
        },
        "id": "qVVjNm1mmibY",
        "outputId": "e9d3df10-d21b-4a2f-e8f0-b82723f9646a"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to /data/mnist/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e0d4cc84253044d4b5b9910354aa9dbe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /data/mnist/MNIST/raw/train-images-idx3-ubyte.gz to /data/mnist/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to /data/mnist/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/28881 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7be139a084ed4b3792a2b12b094d6b7a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /data/mnist/MNIST/raw/train-labels-idx1-ubyte.gz to /data/mnist/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to /data/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "79e2100535df4afbbf3900c05e7598ff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /data/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz to /data/mnist/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to /data/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aff660e96ce44ff2aa7156883818b7be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /data/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz to /data/mnist/MNIST/raw\n",
            "\n",
            "The size of mnist_train is 6000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Spike Encoding\n",
        "\n",
        "Spiking Neural Networks (SNNs) are made to exploit time-varying data. And yet, MNIST is not a time-varying dataset.\n",
        "There are two options for using MNIST with an SNN:\n",
        "\n",
        "1. Repeatedly pass the same training sample to the network at each time step. This is like converting MNIST into a static, unchanging video.\n",
        "\n",
        "<center>\n",
        "<img src='https://github.com/jeshraghian/snntorch/blob/master/docs/_static/img/examples/tutorial1/1_2_1_static.png?raw=true' width=\"700\">\n",
        "</center>\n",
        "\n",
        "2. Convert the input into a spike train of sequence length `num_steps`, where each feature/pixel takes on a discrete value.\n",
        "In this case, MNIST is converted into a time-varying sequence of spikes that features a relation to the original image.\n",
        "\n",
        "<center>\n",
        "<img src='https://github.com/jeshraghian/snntorch/blob/master/docs/_static/img/examples/tutorial1/1_2_2_spikeinput.png?raw=true' width=\"700\">\n",
        "</center>\n",
        "\n",
        "We will consider 3 types of data encoding:\n",
        "1.   *Rate coding* uses input features to determine spiking **frequency**\n",
        "2.   *Latency coding* uses input features to determine spike **timing**\n",
        "3.   *Delta modulation* uses the temporal **change** of input features to generate spikes\n",
        "\n",
        "<center>\n",
        "<img src='https://github.com/jeshraghian/snntorch/blob/master/docs/_static/img/examples/tutorial1/1_2_3_spikeconv.png?raw=true' width=\"1000\">\n",
        "</center>"
      ],
      "metadata": {
        "id": "0eOE07HotT6U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Rate encoding"
      ],
      "metadata": {
        "id": "kCec325N1EnI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Iterate through minibatches\n",
        "inputs, targets  = next(iter(train_loader))\n",
        "print(inputs.shape)\n",
        "print(targets)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uH2x6iSQ5nPI",
        "outputId": "f2c25656-e4b3-4200-d7ef-40db3f49769c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([128, 1, 28, 28])\n",
            "tensor([2, 4, 7, 2, 2, 8, 9, 6, 6, 1, 7, 0, 5, 5, 2, 2, 5, 7, 5, 5, 4, 3, 7, 2,\n",
            "        0, 9, 4, 9, 7, 8, 0, 0, 6, 3, 1, 3, 6, 1, 5, 2, 3, 6, 1, 2, 0, 9, 4, 1,\n",
            "        1, 1, 3, 2, 9, 7, 6, 9, 9, 0, 2, 6, 1, 4, 3, 2, 8, 0, 7, 8, 2, 2, 6, 0,\n",
            "        8, 5, 8, 8, 2, 2, 7, 0, 5, 6, 3, 3, 4, 4, 2, 4, 6, 6, 8, 0, 4, 7, 7, 8,\n",
            "        8, 9, 7, 6, 3, 3, 0, 0, 8, 6, 2, 7, 9, 1, 5, 9, 6, 0, 4, 2, 0, 3, 2, 7,\n",
            "        8, 2, 9, 0, 9, 6, 9, 1])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(inputs[0].shape) # One image of 128 images in the batch with one color channel\n",
        "print(inputs[0][0].shape) # Image without color channel\n",
        "print(inputs[0][0][0].shape) # One row with 28 pixel values of 28 rows in the image matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dHV5m_lojSo6",
        "outputId": "99afe437-7042-4bd9-a750-e956008dec20"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 28, 28])\n",
            "torch.Size([28, 28])\n",
            "torch.Size([28])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from snntorch import spikegen\n",
        "\n",
        "spike_data = spikegen.rate(inputs, num_steps=10)\n",
        "spike_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ChVCQ0k51JUL",
        "outputId": "632883cb-2f27-49ad-d389-71251c96ed71"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([10, 128, 1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(spike_data[0].shape) # One time step of 128 images in the batch\n",
        "print(spike_data[0][0].shape) # One image of 128 images in the batch with one color channel\n",
        "print(spike_data[0][0][0].shape) # Image without color channel\n",
        "print(spike_data[0][0][0][0].shape) # One row with 28 spikes of 28 rows in the image matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KzQzBEzekflq",
        "outputId": "5e469f6e-e3f8-444c-a40b-ed17251c56ca"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([128, 1, 28, 28])\n",
            "torch.Size([1, 28, 28])\n",
            "torch.Size([28, 28])\n",
            "torch.Size([28])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# One raw from the image tensor\n",
        "inputs[0][0][15]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D7MtJGnY7D40",
        "outputId": "d5999b22-19f1-42bd-a890-60cd5211849b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.3922, 0.8392,\n",
              "        0.9725, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922, 0.9922,\n",
              "        0.9922, 0.9922, 0.8196, 0.1765, 0.0078, 0.0000, 0.0000, 0.0000, 0.0000,\n",
              "        0.0000])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Raw converted into spike train using rate coding at time step 0\n",
        "spike_data[0][0][0][15]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L8i73geD7ekJ",
        "outputId": "0f140915-d2dd-4402-996d-c1da77fa5944"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1.,\n",
              "        1., 1., 1., 0., 0., 0., 0., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import snntorch.spikeplot as splt\n",
        "from IPython.display import HTML\n",
        "\n",
        "# Take one image rate-coded in 10 frames\n",
        "spike_data_sample = spike_data[:, 0, 0]\n",
        "print(spike_data_sample.size())\n",
        "\n",
        "# Create a grid\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Create animation of spikes\n",
        "anim = splt.animator(spike_data_sample, fig, ax)\n",
        "\n",
        "# Show the video of animation\n",
        "HTML(anim.to_html5_video())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "kuiLlr7Q82RK",
        "outputId": "b13cb7c1-77bf-4c86-f56e-e79fcf2c45fa"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([10, 28, 28])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<video width=\"432\" height=\"288\" controls autoplay loop>\n",
              "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAAAaB21kYXQAAAKuBgX//6rcRem9\n",
              "5tlIt5Ys2CDZI+7veDI2NCAtIGNvcmUgMTUyIHIyODU0IGU5YTU5MDMgLSBILjI2NC9NUEVHLTQg\n",
              "QVZDIGNvZGVjIC0gQ29weWxlZnQgMjAwMy0yMDE3IC0gaHR0cDovL3d3dy52aWRlb2xhbi5vcmcv\n",
              "eDI2NC5odG1sIC0gb3B0aW9uczogY2FiYWM9MSByZWY9MyBkZWJsb2NrPTE6MDowIGFuYWx5c2U9\n",
              "MHgzOjB4MTEzIG1lPWhleCBzdWJtZT03IHBzeT0xIHBzeV9yZD0xLjAwOjAuMDAgbWl4ZWRfcmVm\n",
              "PTEgbWVfcmFuZ2U9MTYgY2hyb21hX21lPTEgdHJlbGxpcz0xIDh4OGRjdD0xIGNxbT0wIGRlYWR6\n",
              "b25lPTIxLDExIGZhc3RfcHNraXA9MSBjaHJvbWFfcXBfb2Zmc2V0PS0yIHRocmVhZHM9MyBsb29r\n",
              "YWhlYWRfdGhyZWFkcz0xIHNsaWNlZF90aHJlYWRzPTAgbnI9MCBkZWNpbWF0ZT0xIGludGVybGFj\n",
              "ZWQ9MCBibHVyYXlfY29tcGF0PTAgY29uc3RyYWluZWRfaW50cmE9MCBiZnJhbWVzPTMgYl9weXJh\n",
              "bWlkPTIgYl9hZGFwdD0xIGJfYmlhcz0wIGRpcmVjdD0xIHdlaWdodGI9MSBvcGVuX2dvcD0wIHdl\n",
              "aWdodHA9MiBrZXlpbnQ9MjUwIGtleWludF9taW49MjUgc2NlbmVjdXQ9NDAgaW50cmFfcmVmcmVz\n",
              "aD0wIHJjX2xvb2thaGVhZD00MCByYz1jcmYgbWJ0cmVlPTEgY3JmPTIzLjAgcWNvbXA9MC42MCBx\n",
              "cG1pbj0wIHFwbWF4PTY5IHFwc3RlcD00IGlwX3JhdGlvPTEuNDAgYXE9MToxLjAwAIAAAAiHZYiE\n",
              "ADf//vbw/gU2O5jQlxHN6J0zH78VuLo0N73OAAADAAA33OZE/sqTBubAAALgmC64zEyVAAGbMVNp\n",
              "U09uRzuqRrbBAfPhUpiLkvrTQEFgvEv8+7TVlTedWnKnrHUKz12QeUdzP1H4CXmvemRBDh+K/Op9\n",
              "pJAchgdkX6X4T3IG7GnL9cYk0/3V7gtmPDF110GdXNkfqPXeEdJ1SfKAXIeSADmntA0JysDvOb+l\n",
              "59KP0iLp/pldLJsuZksirJoum518DrCouv1x5ftk8wIUb2Tb7k0cC6s33AtMHhPzvKHU9EdRR7YC\n",
              "CDOu3uqbhOkxqAucqj+SBfU5Me/M3Cu1NivHrJuvxoJd2mW8mUOFtpI9bMtJxq1493Tw6GPWKKoA\n",
              "I8sq+gB8RM1f/VlQ+cOHpHEa2Vkl3LkMKgwQSpKvKdNUL40ioVx5Iny/yZ6ZdkQlg+FhpEj6KToX\n",
              "FKi75Nv2ibQKIzYptVCm7WWAn0MZWnRvLITNVlNUn1SwAzC/B5dBsOy2YsSyREIFNZv1jz2i7rwN\n",
              "KDLmpbi0ODI8w52C76QDmgRmiEDNkLOaSD4jw3NtbM+aq+0/knDfdO0G2z619H4YKf6iifETPozL\n",
              "sm2llsaTEs+40D06WQwpRtb0f6qbITxzILSEMGp9SYuMlFZNz1r94Qq0nFaTNbTWKFttUt/sMyB1\n",
              "Pt+ydtD13WdgOZL77i4Ha/q+uZRUZfHarPA2C8/ium09+KPyDNQYX5YEVBi6eQ/v1e/Xem96floB\n",
              "Gp0D0ubzNxXYgUusLWRzRZPdQ2re6ZAipxqHdNJ9z+qIuMgsJMNpnpubcxGt5/vZ/382CN/Xyvrq\n",
              "rvUT7ffHM20/8IhAgLyJ+yltjByD9oHlZiQthebuiXnxDIZJ3TdJLt0F0F9S0DFlzBawHXSthL+V\n",
              "Ng8mpHdJovfk8TZNTdIMJMZ1ms4+NU0wnPgt6d84+2PhmpchNAJgvpYFoSnMoPfgjFezMgFkkFMT\n",
              "n48WfT0MWQcxKvoNzefCoeR+VKk8mSQ+5NVdcDFsLNhEN/1nO3yhM6sAZR0aJIB++lsFFS2Sh025\n",
              "kxYML8OD5DJAR498wYCW3MqRqvZrayBjVuY3Vrz53mOsNTbessdF+AiPk9e+vsq6oblX0NV+s4iW\n",
              "aaa7/TLGomSBolUpZ1QC/WiuAyYB6wkD+iW6Tt0tDyU++f/o/U9tK8qrgNL/QBi3h+/aUxRjr8YW\n",
              "snO7sY+C3vnv86hQhkHcdAxMuicekgAOCHgAIUyzttjFOcVw8ZFzr0mdu8JskdnMoXKLOpJXka2T\n",
              "I5oafwvcE0/ZjTfwqeFGVmar5pDbotsz+w4RdwnYiGKkznZRgTkwED9XqGSE0R3qv6o9769xrnVN\n",
              "T6w7b3kUl/RlFNIcpOX6jgtijjiftooJKdeLT4SMrWub8LiacqGhsniOj86bMm8pPLrfaDkm4IXq\n",
              "fkEepq/946NQcXrWPLSp+ogDkwBqB8XT/e5mkXj9ck/jl0osqKZVO08vxIYwrp8z/vAeS8N/7X/n\n",
              "UQpmf9n6EIUuHrItYEKpG/3mpjhnKR8NtklaEyR0jfQk012GZzTq5oBjTyf/U9AHH1o2BU4VYK0w\n",
              "gqFobjWhdhTkzgwykHHcTVKNmbrfA4ISgxg8f/SlPWbf0ar1RbVvKECwoQ3U4s8LLplKXmAViulN\n",
              "+W9FOiO4HzyYBUWxpZiX6ks0er30OG/narFYkkEubVZEl8y/C1Ol6XFnZOjP3x7U5wR7kU92DCry\n",
              "8n3gxdfBFl0SUTgOOLvL6wZTyTZOau5qqqX80a7/jnwz6Q5/bp5kWgEAUVvrqo5G7pNGbELPbBnY\n",
              "K1uMOpsPHiX+oIynbFmYk15TMPXFQeEe5Og7Hj52cfup0ci1PSwFwjdJ/sm9DjntyKDXegGPiyDq\n",
              "404NBZeDd6HLJutSGWVvtbplDa59MwvRCS+PDAMlG87kdAIvyvG+WzhYMnD0g70oG/8tmF+veiAj\n",
              "3ejwKK6pigjTe2J05XyxeUNx7LnruVDmuYsF8433hEUk0f1YcBCatCqqNmo7H8LHu/Odb0N43xI0\n",
              "l3l9eKAJtn9tcTrVu5wJZPc0UggIGrwL95lidKeKpC3Pgk2w7XjdE//f/8xG5WcSLr2r8HsgIbZ2\n",
              "F0jQ3KZYrglpsO2GJtfYYcyj9l1AXm+VY411JJDvbmjEGq67WQ1ku52GdLOTKACIsf9uDm+/vsn6\n",
              "+VNNdGIHcBRDrbwzouu3IiMZjNESY5Dy/w24pglXxmxKsmHKk7deAiDegS2zvUkl3DzJqDm1nKmf\n",
              "k42nWoP+R4Uu7WcvP19WLEkAGktA4g61XjD56AAUPzHy5Ww+t0VXiKomovMCO66PTcdU0ClkFTiq\n",
              "lQGWLFK9OpY7450zsHdhSu8tehsO7cGrU22nEUf78/mLv+Vrt+yc0xjkKpQ8Cy0NxaeNwyfhnC1I\n",
              "gFgO6c5M8p4R2Lu2vUEnEblrmSb+2QiYAxapAZf2QmoEXrldZUDBTHAFos5+9wyiNSaZCAFqfALu\n",
              "iLaCiXSRSYNoy6mxTp4HO/v6IQEi8gIcq+7CWJfN+sXk3ssRFUbCSTGI597acE2tX8LrJ1jdwL71\n",
              "GUqNJ7jP25+bjBRnYHwnDa6gXXK0aMp0ANFxUMxy+HkF1g0ZHdF7jiB7bzwHy0NW3gmdaqplELqE\n",
              "S0RqoR4Lczia7sGlBuRLlmaD0adx7Ktzxkd94V/PJpytKO5M8tx6Gdzl3CFhnB0ub84u7UsI0jny\n",
              "QARny6cC+59seU4DaUHckPHOAq69xRwzz7LZGLewAB3ktwMj+Ijxf8BemGDh384UuNjPC72WMTjd\n",
              "ZUIlBPDCaLIjsilz3iMsPhP2k2IhFDeldGqVRqs5KXSTFFHow1EaLtTfzTZCvwuqYCT6GNMdOPhp\n",
              "kbR/wPz8SAAALRnZZRUAAANOQZokbEN//qeEALX/cLCJWUqoVRW9flg06OiSrihF6P7WNbdX6xiR\n",
              "KM3EEAjoemvkZykqcBk3UX5gG7YLdZy4oUJZ60EfVSua7RDpl6iX9NYgTinM9iwTIL9PJD+w9cIJ\n",
              "wSFrF1dwtSD3U/Y/fmNZxXQEIr+nx9eZwKscqmNg5iWiVODeBM3fHzo9pNi/s+KLtlC/6iwMMRGT\n",
              "eGm3Y69KnI1EWeKp+SP2ryzpkrZvuoJ2d4abBBKatj1xrJSCCP/qBoEYv0gr87ImRLfsz7/ADFr5\n",
              "54Mfr8kC1VbZOWsxWiSw7GX9KrUb+/fX/KFmeOQuAX8t80EjUD2Y5G77rLzBSCzwF/by9+ccZavH\n",
              "0mnl1IY9Z6OdLBnS2mRo3tJLHnaHNSFojj2MZozeVAzHjJvz/taxNswRbyQ6juSehpRx9GAZulDS\n",
              "Q2XlDjNRCclFeJ4onYu/6Ij7ju1WyBiQ2uU18b25dsbO0f5g7M+2BETasWC10JVtaNUadcrKuhmm\n",
              "ci1t+O1XTuff6EvXW+lMMVyiZnhgAzjRMMgBXk6f/QaTCc/W3KA/oL78U4maQy9oRNuRZ3Vz4TCO\n",
              "eVVPS5f368AN6wd14Gzxq96FW8i86Shu0xG8AlrJC5bPOqo92hYrgZtiMy5WZ0zbTKCsfgQBDNmv\n",
              "mOKFMlhvKMUiz5IXU6Q0X8RvALyevjzhcvHVwcjLypN7GM+XVANRAWTXd4FapCS8K4jkUEclVSpf\n",
              "utrwmq+1wNJY4aYeWwNUbUqF6MpdR2vyH/7K1IebpzyegR6m+S1lTqIlprcHiucyixIpmv60G4se\n",
              "UkK/CeT9Jb+3OhzaBUsaaEsKxt8HZ16hfbztQSRFisT0313L4as/WOBS8x6Sf1snzFMga950wt5C\n",
              "ZlYkm9Pwh3dVTEo9uYGG4lY8GtxNhdba07pTt/zaPrVRBnW8MCrYqv0t9WqNJz/Wa9/aAMDfmiO+\n",
              "/NJ0bqSXiwW6B/qb2zf7C3NUcoHnns8jaeXXHWnUuW1zxCJ/1CGOGYy7jGTqwwitOfiDW87oGCvk\n",
              "CtQT/zl/Ysb+cw7RJGhhvifGUh2f/CyfJGjJ5b77wBcr0s7iYeGSvXgMO8P5l9mHHJsyQXnat5hb\n",
              "4NMIiquYTsgYAAACZEGeQniF/wBsQK4sVp7jEgU23DymGBmdB4q3HbB4034C7p1kRU9NR0taPsOf\n",
              "7ZXzExsCb5UMkK053lnZPes8+4rcgBOwBdjfVBq41umTpBqMnerZx2vDc5kZ/OBpHLCKPik4MsCz\n",
              "fimV7hos4yCVV56JG53k9gw/VT2bW0ERXM24U8XrnXN5LwROy0yWnTCmMzAns0Gyf76c8iDcK8Rl\n",
              "vlTrSLtdbQG8NnKqT9D5k3hbq9rLiTcUHwD1gVs9GgPLnn44E76IWua5rvAPOA3q6287iYFDQhQV\n",
              "cMVyaroKbpY9GfKS2tCgix5phwDDf9eBIo9QjNd8wBgPSZLCYZirFXTtPEl7Vn5/RmwTFFPMm3GQ\n",
              "U1S2PJKRRH4LDaQy9HP5foC5ah6YNmyoYwAbUm24kHt+pFrPFpHirk3ZmM7zseBCyBUo4B+BW8x8\n",
              "GRaB7huwtTj4iWxz721wYydVKfbtl2q/FEL4tzWDIQyAfYxlWrxD//DRHysBAZ4wZepmxCh/jPh4\n",
              "UfqqCHGFpylgkipQ/rFSfwYQ0GNVqcK0vhJEFgwotkBvZzUjaPPorIxNZWmuN4Sih+ST1xss3Aa9\n",
              "ruoqzcL0ste7J4997eQoHpWG+DuRXfbYpJO4YZ4bAoOK53w0YQjdhcwfuDPNNlcq2eZGw0CrhC2B\n",
              "y5KZz/h+ESFefhh8u6H9WDUew6ulEGH3WQJIFo49jfEzpFK5E3DhTI2nCXhsGT4QwbeQnvSW2XOv\n",
              "oHPzM42bOvs4v++9c35R+UJoy9SbOxHshL5wDkbQllYN0qJaxJ5xRfBXPtxDXf3rNbpyPOpwOQAA\n",
              "AVYBnmF0Qr8AjvgQqg9mkhEQ/nXNCADlQCVoNiuvH+gfuH+ek3JIn1LS16qtipukQOZgrh/CdwFH\n",
              "nR6L+jrJLmW4ck1AXoKvQu4R/xd+O2T8HZQoRZTBZ+gBTdzNhYMNdCzFhotYtnglGKvDqXB0o1o6\n",
              "r18s2Zqv1qMye47RyH7OVVQTJzYrIB6ErMT6VM/O1q7snj0sSURy0uRXQm681/Re0fwJrq84CiJB\n",
              "N4TAeFTas6Srkj0G8iZnwzDS0MSFovgTRBZ4wmzYwtkuMsQWWtKd2RRjuzFAbI3Tr54wFTJKeMim\n",
              "euDRo9rtREdRNMht1tKsJXUSDWPq1V40KxWN9RqG3r+Te53w5kOoCFb4PunJyCOsbeioDNGe20qN\n",
              "iMfgiNr++Qjbi4wZG0AQoO5WHc9NvxvDCHOlzyKxTJQCc1J3qb5Tu4uFUZ3ufKiQwbNq6am1XwRL\n",
              "YygAAAFCAZ5jakK/AI7ruTnrJFF/RrdMTdjjjVC1DYrQQ6jGDXOW+JRMqBQAA6vQXrDBWC572KDt\n",
              "DHdM7JRgvV81PS642kMZQ0lCvLGjcwX5Le3lU6qwWXvdzXlKpoZy+YXOmWllABKNVZipoaNaOq9D\n",
              "R6POlooyJXH6eA/AoGOxtmvQU6x7oeaw9t8uO9zt5YW7zlCI4F/wZsMvCO6ZoreYAf/6hMNeNvGF\n",
              "a5O6dbxRg5VydB1mxghqSw719TsKSHxqZRV5gcmix0mg7Ek3lHr4BWltBbQF5ZoN+21G6OkFbntk\n",
              "/tOtlqRwP/ojkAqyZeGVVp/T1I/9qlCmT9QBnIDcEPb+Zhv7nZx+wQGmgkIsZDHWyLc0V+VbSta4\n",
              "j/vE1UrNblq47rsUmp8Lqge3I9uR4OpnXrz0YEOInauWMpcNtsi3tSeH5QAAAX5BmmhJqEFomUwI\n",
              "X//+jLACzfDrrw1ge6ALyew+81myqwfGqRaOJBqifBw8ghmkLMbKgi7fT+F0JSV/rVvFqCePTS/q\n",
              "S0zTlFGZqgJloa9gB/6IQmL+6K9U/Y2l/T/tTMIVFrzD0cLNMbTzdW6jj07vI1RFBqEru/4pzPmq\n",
              "w8BiFWEjfxKLKc3kWU/iTpZ/xIpsfyfLZFUT//B/QohC9zz2Si3hLFdx70fqeZBlAOyZpP+mgbxn\n",
              "0BSpYm4WtxRzHdmgbp2SMw8cK0DFRbzyyHMqOdfbyu7zLnSx8t3KbbnjVzk18EhDeleQCADoZ3T+\n",
              "Wd9Se3YWEDjZcC1ObatuI9LpT9jFWZJuIT0vg0AX7bWM5nlQqZE3K6pwa8+/sfAikk76tm1zGZwB\n",
              "hd8nlWluL0iZ5GXgHwmb7QFZuu6fQH/TAowIFTj+fvelPLWRXhWd2RczgGrr7sLmXiEhZaAkD2mv\n",
              "WZkLi9czs2M9o7faW0ZGlU52f/nOPwPGNjkcmZ9BAAABBEGehkURLC//AGmhRIW8Q6Xq/kNIAP+b\n",
              "VVkpzk+UgmR0/ydMXWtYPVgvtRHz9HUbTbZ1qsSHGtFCzIZZvMfSzY7V+6g9Hwl9kq5vS0VA06AZ\n",
              "S5jyAkFxvwBLZbzG7Z8Bg1axQfP6C6PxC+J44B0oRfTxudJVE2ppFh8THG9+YKAh5eLbV0SpuroZ\n",
              "M3AxJ/WDXRzh/VB/tXSQTgVfnap128Yz7zc6f0wuTRhu5KR15JpjAIpHgBmXfdwbtUog8OWnw2LT\n",
              "7RI2WgwyY9i3IZL/WZxFTwqVJB2e4SDfQ3dv1BhTIlfjCUZ3OCJkvWqLkKA0DIJgKoUhkyTqWXUl\n",
              "SBxAtk/u29DxAAAB7wGepXRCvwCTMOBP89CpxuGlVq37VKP793orMAlZrA3uM+oS/tD/CswusZjn\n",
              "+xVF9xLePe2v/UrJ73+25R/wTcXXNCfuRd74maUWJp367rFF0eys7r2TzS3LPQ1mtpPZoB/pT04+\n",
              "wrzUdAH4S48OA5a110KRv7MREHTv2YvxrFmhnLStLLDsOLeMawj4Tie7PrG712Ud6W2y6HIKjhKN\n",
              "kguBWgTiSV63PIPxqBRGqXhMRrb7QjlEZzrVRYJSIkdnREzYCDZBXl8NYThQ7i5BW/iJgVwKp/tS\n",
              "bDEcWpquQZlh3yTQ/SGcNLrj5eO28YtVbKl8dK2M6WOWEkguyT0Oa4olj3rROv+8LudfcrOavdCU\n",
              "n+hdB3lzy6tC9DtygKbvjiycH3XkV7oHzJpldEpWFc+AKXK0msnsfG6QdDkkC2ok7foCc6ZxyMtL\n",
              "9bnJH/wjfliYJdVz8/Go8kp82zPMj195AFgWv4xiPIZOPRdmXNG+9qJEWxhg6zwg3bNUmGdetANt\n",
              "H3sup0RkkwQ9IVvFzP9qXG28BOiIiYH4MDhr9BTqHB4P/8lSZ315xKLQVQwrNh3XpvR1dbP9hdEq\n",
              "8v7gj5Br7zrzv3fJOs8RmpLw9Kj3WZf76NTqnOhJuR0mKLF2ggh0/6+bJj9UwoRMkRd00wAAAJUB\n",
              "nqdqQr8AjsgX2Bn5y+1amFX17r7Xg/9k5DabbeBtZRvIT/NlAHmpTtoZ+8vO3/lEwn77VL7W0DkC\n",
              "F2u2jlFmF7Oz8r1txvIiRKDXH4Rui4Coe+shdYyn6nScfGRwDW/RVwS3ilQQbafxDGTjq1BHJMmR\n",
              "hCabZGwXiyo1A6dmiZs+QT8LYPig9AODnG7qcTsXlZ/egAAAAU5BmqlJqEFsmUwIV//+OEAKdzUG\n",
              "pgA8EaLi7ZtK7V0vomzpzN/4++m3nCKADUW3Ad5ttsJsAwU+B+k453x92OfJy6FgrVwJ7ODsq5pl\n",
              "QgVB4lNPkLDINnUFy9eMRehWKsZkj1L1szjcl+pck/qWvGAjtgvU6IOJK9PPnPZgDnp0cdQHhIA1\n",
              "T7A4SgZmeMhzGt9yUqcZGOkUsKta1JUlBDNPmG+dyvTQRFHKnwUmPDqnklukFa4r27CzfpUrOEpX\n",
              "Rlngo907GJBXXxiV1W2LUBHGbkw99s6aOymKjqth0Ayfjff24TJZyRMD3ctBSshQtIpf/GXAVU9u\n",
              "jIt76Hz5G1TYnvlmUO5D8MCri6MLaamLkOifPAxySYaSsI/2m4ktM3jE/wxbXKUXasc5VV4rFti8\n",
              "tMg99ZttlQZqO8a3hyItSKNRDaZDDUYCLbTmIE+AAAADpm1vb3YAAABsbXZoZAAAAAAAAAAAAAAA\n",
              "AAAAA+gAAAGQAAEAAAEAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAA\n",
              "QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAIAAALQdHJhawAAAFx0a2hkAAAAAwAAAAAA\n",
              "AAAAAAAAAQAAAAAAAAGQAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAQAAAAAAAAAA\n",
              "AAAAAAAAQAAAAAGwAAABIAAAAAAAJGVkdHMAAAAcZWxzdAAAAAAAAAABAAABkAAABAAAAQAAAAAC\n",
              "SG1kaWEAAAAgbWRoZAAAAAAAAAAAAAAAAAAAMgAAABQAVcQAAAAAAC1oZGxyAAAAAAAAAAB2aWRl\n",
              "AAAAAAAAAAAAAAAAVmlkZW9IYW5kbGVyAAAAAfNtaW5mAAAAFHZtaGQAAAABAAAAAAAAAAAAAAAk\n",
              "ZGluZgAAABxkcmVmAAAAAAAAAAEAAAAMdXJsIAAAAAEAAAGzc3RibAAAALNzdHNkAAAAAAAAAAEA\n",
              "AACjYXZjMQAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAGwASAASAAAAEgAAAAAAAAAAQAAAAAAAAAA\n",
              "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABj//wAAADFhdmNDAWQAFf/hABhnZAAVrNlBsJaEAAAD\n",
              "AAQAAAMAyDxYtlgBAAZo6+PLIsAAAAAcdXVpZGtoQPJfJE/FujmlG88DI/MAAAAAAAAAGHN0dHMA\n",
              "AAAAAAAAAQAAAAoAAAIAAAAAFHN0c3MAAAAAAAAAAQAAAAEAAABgY3R0cwAAAAAAAAAKAAAAAQAA\n",
              "BAAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAA\n",
              "AAAAAAEAAAIAAAAAAQAABAAAAAAcc3RzYwAAAAAAAAABAAAAAQAAAAoAAAABAAAAPHN0c3oAAAAA\n",
              "AAAAAAAAAAoAAAs9AAADUgAAAmgAAAFaAAABRgAAAYIAAAEIAAAB8wAAAJkAAAFSAAAAFHN0Y28A\n",
              "AAAAAAAAAQAAACwAAABidWR0YQAAAFptZXRhAAAAAAAAACFoZGxyAAAAAAAAAABtZGlyYXBwbAAA\n",
              "AAAAAAAAAAAAAC1pbHN0AAAAJal0b28AAAAdZGF0YQAAAAEAAAAATGF2ZjU3LjgzLjEwMA==\n",
              "\">\n",
              "  Your browser does not support the video tag.\n",
              "</video>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAACtklEQVR4nO3TMQEAIAzAMMC/52GAnx6Jgj7dM7OAnvM7AHgzJ0SZE6LMCVHmhChzQpQ5IcqcEGVOiDInRJkToswJUeaEKHNClDkhypwQZU6IMidEmROizAlR5oQoc0KUOSHKnBBlTogyJ0SZE6LMCVHmhChzQpQ5IcqcEGVOiDInRJkToswJUeaEKHNClDkhypwQZU6IMidEmROizAlR5oQoc0KUOSHKnBBlTogyJ0SZE6LMCVHmhChzQpQ5IcqcEGVOiDInRJkToswJUeaEKHNClDkhypwQZU6IMidEmROizAlR5oQoc0KUOSHKnBBlTogyJ0SZE6LMCVHmhChzQpQ5IcqcEGVOiDInRJkToswJUeaEKHNClDkhypwQZU6IMidEmROizAlR5oQoc0KUOSHKnBBlTogyJ0SZE6LMCVHmhChzQpQ5IcqcEGVOiDInRJkToswJUeaEKHNClDkhypwQZU6IMidEmROizAlR5oQoc0KUOSHKnBBlTogyJ0SZE6LMCVHmhChzQpQ5IcqcEGVOiDInRJkToswJUeaEKHNClDkhypwQZU6IMidEmROizAlR5oQoc0KUOSHKnBBlTogyJ0SZE6LMCVHmhChzQpQ5IcqcEGVOiDInRJkToswJUeaEKHNClDkhypwQZU6IMidEmROizAlR5oQoc0KUOSHKnBBlTogyJ0SZE6LMCVHmhChzQpQ5IcqcEGVOiDInRJkToswJUeaEKHNClDkhypwQZU6IMidEmROizAlR5oQoc0KUOSHKnBBlTogyJ0SZE6LMCVHmhChzQpQ5IcqcEGVOiDInRJkToswJUeaEKHNClDkhypwQZU6IMidEmROizAlR5oQoc0KUOSHKnBBlTogyJ0SZE6LMCVHmhChzQpQ5IcqcEGVOiDInRJkToswJUeaEKHNC1AVcegTL+uSnUAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reduce the rate density\n",
        "spike_data_sparse = spikegen.rate(inputs, num_steps=10, gain=0.25)\n",
        "\n",
        "# Take one image rate-coded in 10 frames\n",
        "spike_data_sample_sparse = spike_data_sparse[:, 0, 0]\n",
        "print(spike_data_sample_sparse.size())\n",
        "\n",
        "# Create a grid\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Create animation of spikes\n",
        "anim = splt.animator(spike_data_sample_sparse, fig, ax)\n",
        "\n",
        "# Show the video of animation\n",
        "HTML(anim.to_html5_video())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "XukjhKKHAehp",
        "outputId": "c9389f54-661e-458a-cd65-99590ee2fd4a"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([10, 28, 28])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<video width=\"432\" height=\"288\" controls autoplay loop>\n",
              "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAAAgsW1kYXQAAAKuBgX//6rcRem9\n",
              "5tlIt5Ys2CDZI+7veDI2NCAtIGNvcmUgMTUyIHIyODU0IGU5YTU5MDMgLSBILjI2NC9NUEVHLTQg\n",
              "QVZDIGNvZGVjIC0gQ29weWxlZnQgMjAwMy0yMDE3IC0gaHR0cDovL3d3dy52aWRlb2xhbi5vcmcv\n",
              "eDI2NC5odG1sIC0gb3B0aW9uczogY2FiYWM9MSByZWY9MyBkZWJsb2NrPTE6MDowIGFuYWx5c2U9\n",
              "MHgzOjB4MTEzIG1lPWhleCBzdWJtZT03IHBzeT0xIHBzeV9yZD0xLjAwOjAuMDAgbWl4ZWRfcmVm\n",
              "PTEgbWVfcmFuZ2U9MTYgY2hyb21hX21lPTEgdHJlbGxpcz0xIDh4OGRjdD0xIGNxbT0wIGRlYWR6\n",
              "b25lPTIxLDExIGZhc3RfcHNraXA9MSBjaHJvbWFfcXBfb2Zmc2V0PS0yIHRocmVhZHM9MyBsb29r\n",
              "YWhlYWRfdGhyZWFkcz0xIHNsaWNlZF90aHJlYWRzPTAgbnI9MCBkZWNpbWF0ZT0xIGludGVybGFj\n",
              "ZWQ9MCBibHVyYXlfY29tcGF0PTAgY29uc3RyYWluZWRfaW50cmE9MCBiZnJhbWVzPTMgYl9weXJh\n",
              "bWlkPTIgYl9hZGFwdD0xIGJfYmlhcz0wIGRpcmVjdD0xIHdlaWdodGI9MSBvcGVuX2dvcD0wIHdl\n",
              "aWdodHA9MiBrZXlpbnQ9MjUwIGtleWludF9taW49MjUgc2NlbmVjdXQ9NDAgaW50cmFfcmVmcmVz\n",
              "aD0wIHJjX2xvb2thaGVhZD00MCByYz1jcmYgbWJ0cmVlPTEgY3JmPTIzLjAgcWNvbXA9MC42MCBx\n",
              "cG1pbj0wIHFwbWF4PTY5IHFwc3RlcD00IGlwX3JhdGlvPTEuNDAgYXE9MToxLjAwAIAAAAdrZYiE\n",
              "ADf//vbw/gU2O5jQlxHN6J0zH78VuLo0N73OAAADAAA33OZE/sqTBubAAALgmC64zEyVAAGbMVNp\n",
              "U09uRzuqRrbBAfPhUpiLkvrTQEFgvEv8+7TVlTedWnKnrHUKz12QeUdzP1H4CXmvemRBDh+K/Op9\n",
              "pJAchgdkX6X4T3IG7Gl3IJO9XeYU54gn3gHzphhyHDhpCpcIUYV9TkxJWocCHp4nz98v/U57j+AE\n",
              "Qdi2+uZ9+2FlDbWwg7eKufPhvV7bcOprSfHMWxQsBQzC3cN6E5Dvb1A3ZONwUjeLdAPOdFtzqJAy\n",
              "iKWuSowlTA9t0oZ/yQVlNiLgGXzL9lFtilYxFEILsidhzzV7J3TDgWXzsDvMEDKzpD4yvIHelwmA\n",
              "XAC7W+E0md+P1n1BpTQggyk1ieq5VNYxo8pM91XWO4Grfj20QT3Djnfini9KEqUEr/JRM13Meih4\n",
              "z8S5nY2WoXt/Odz8+yy3CHNyPOYTx4f+6gE1Y7x4DMUSjchLxwTNIC8TNr+i8OL1Q0vgpyaeuKt1\n",
              "HD9fzRMgeAO+w8u95/aECiYtzAEtkIu7rVued6FWNNZfsH0meB8bEgHslBP6l3gN2JEdxMHYh4LP\n",
              "SyMxTVp96CqCqxD2rLRk7s7tld+HYGKLT3xlDUWOtyTCvHxT9jRhUHzoTCbznNocj8mR3VgmYWil\n",
              "vlSFM8ACFT+T92CU7pdyETw959r54/HE7bK6j2qa9S7GiBrdh+lxLwpeR1J5kmwbvZLtwTB4qRST\n",
              "Yn+FYXvLzzWoBei3mxoA8+Wx05a7PChl/zppn92c/xNAAZgkqYxZvG3wRVM7Uoqt1AiavAyStgrU\n",
              "MW2KDLrmySYx0ZMzwaxBxo6S23RrAWq4zPw8jlBkkwsGBKVjtXb6DGcAYy/erpP0VLF3vvS8mf3/\n",
              "U1wCrsPFD57bhIi4owLN7E1nFv224R1EDM5tmShoKk++qFZkzYrWDRrlaZqN5QxAO2rF2M5dLkXc\n",
              "C+ts1XolQJx/x3UoRGe8IRysjM+Duo6VC69OSOWys7Yzsa9Nq+RxUkoMbFv2PYCmlbLgG39L4OMh\n",
              "eJX+7G0vfkTiCHDMuhkGYa3Tad1jIFRGTwKP9sBKn6Fh+Rbq0U1dpopumeZc6kN2ZWWJT5RkEx9O\n",
              "nKgluIOqrcMsWsMqpLCm9qk0CyhAoTeQ7A8Yn00987x/7ukdlpucGyDhNKHpkQG4xOHTkKwhU/p/\n",
              "wiU9zc53A+v+z3x0SG4KHTLQx5YwePW5thXPMN0Sem3bEdR9RIwFCm6+LnJnCshTMu475cMrvwrT\n",
              "/cwQ9XqqWDdJnYQQqlUIHUjZ430YAURXrvajle/SiKBJ2yg0NpUPYuRWiIz3cTuNPjsdTbANxGF/\n",
              "CkiV07qzg+kWqk8xIxnsPqxIyWFuq7rwalLX4EOx5vJlSHkxGXrR9RH3aOxs2qlGeDT4FwuyqG/u\n",
              "lpmEINNOBkKY1tSM5GSDo6ocgd+ruwCZFvW7sUOx9/fYB7ld0ZtDwtK9o06Ya4N11gb0IBpmnr+i\n",
              "tlH3cz+7j2IRVU4S7kujxAtQhqFTIQfDBeO/IsdFiiE6QcJlkZkh2lJ32cYniTYcbBKz8LQuM4Gz\n",
              "7QgnWfTsXCZEfoRTa06i+P3SRdgDY8IcsCVMZYtG/9YrmLoh/sRCifUVzrfqRxYITrE2Xbqe4Hao\n",
              "g/+BX6AhLcr2s/ypMU2gQYMFC9c1Gb7kgxcwYmcMZIdz9ebV7SIt6GaWQepvuUcfKnOVU/YQv8iA\n",
              "g/FhukI19d3dcD51PjW1RTU7PBs5W+VB00laJLV7PTrAWjz67DH8eAKkv68u1zzOil3aZ6EFoENM\n",
              "Eek2uUFID/OYTXoeaXnJVElgul2X4YlWBmOCHRTRd9U81hQNuGEisBJqgQ0lsje4f/ds5tlDwNNF\n",
              "8gkq1aqmnwnPPVy3xahUCDbjBbIPy9VPfQpYLoPphS0ynbvCgyAEpKYra2f3Hx4SDFWp3QnGaWNA\n",
              "AVDgSOvZA4DCMX1ipZI1BTYddPNKXMIr5D+Pa6nlOjbuxEmxM9C8uFovXm+/5XHwO5zn/u5HkABs\n",
              "pFIdN6AgNlmoC2yA0KfQE7ONTiYLUOzDE8vK/mY2OKhjWE6RpYUvfACbZ3zMYBNvzSnpJdvpZIbl\n",
              "nnTjORRObm0SQv/Fc/YBD1KaY0aR1MauZdjqsUWL1+FmHZf80boW+f864lYfynRubHv150qJrDc/\n",
              "jAoNopHY7yzS1LNMA1Z9W3arDHdneIHw1a2RSaY+UCJIUj9287eOOeD1kaxADp3lzU1y+LIxccsY\n",
              "YFq7OMhTy1UuTKCy8OQETQcSZl47RUrmPXoJxErlnfg8j7ql1Ey5uoT6bzxQwcgf/M/V8JD/LFBz\n",
              "lHArkvmiUa4oQgzBQASHNIfFz3ekQ68qhog2/PPyGrhR9atBK84g01GmqErEa62fR4le/2Zroda0\n",
              "JMiitjgWj9sdQI5pnxgtlqYWDIa571u9LY8x209//4fSoXnhU7ZGQ4IzzYlT0PdrT9WjM55+dFqF\n",
              "adfiD8Th0eAAAFkvLg45AAADlUGaJGxDf/6nhACxfnDj+NVSWSlMASJXf+hUxVSnU3/XFTYjJdTd\n",
              "d1B2YPYZyv+i3yajDoJk5u3JzIpnbrYD2jLuqwSo0BqC6reodEzWdOtweGNnrl+HYObjBifMIcol\n",
              "cEP9/nNOYesO5YIx3e3fFkBha+/f9FUkvOv6YwCxsWrno1oebHOVyquEgC72wyMP5hKPN0vIR+hk\n",
              "AKB/o0tBJ6jDWDUpBWZ6zQ3vsoIdj/cPn6EeOfHATtpe8S8NPCTkC+AWg3atoj6mY8cxXMtj1sMG\n",
              "jOhBjHYbm9koJro19q1Wbb28Okjet9oglqy7Z9iruBAh5ErO+EctXN5WT6lONhGTrXxzNCB1HUD/\n",
              "b8BHSmi2DVcp6AIjmgWPjLIDWE6FNpD9MQ5kkt7ErrCd0YwxT0gGSw3f0hRZ6bJegxCm9N520gai\n",
              "3Iw7NZDquPAVtGgehwuT57Qq7H8suCP/C7SdR5QP0wmEH3v6c+kNAIyi4QhyboIVLwDdjSAFAB9Z\n",
              "4aSRwl2TiDYfv9vvbV/hsOF5WV/DuF50YuUqr6DqWTJn+jqlnYunoHA07vQnGcv+lHkKf3Z7Lv8y\n",
              "5Ib6xOJlHYAJ94rRDu0qHtNgtKzklrpDwAMQfogtI0miRny78Cv0wnE7KxBX8L5+oHv+nJszfRXF\n",
              "XaZVmQyapkQ5nRwRT0XOBKaT04m68Mxoc7zonBSeTUu82jXPF4fg0Ty7zfTUr5K+T/34GsrocOM9\n",
              "Wlj9c8RodEYPXh1J4JuCaC+r54hNvYBL9DRCrnO1W0mC7LzkJQaCPUwSVZQn2CumAL1D+I/wjUET\n",
              "bQAqvi/2Vp2kYQQ/jWJ3vgCRGb/ulIojVPkEBowrKHSpAF+5cD4SSBqV2Ka2jvb70Pzby5XIG1YY\n",
              "trLZEjpUCp8bbII30W0Eh785hHAFXDFeP86VZAr8NGfYn6JGDM2hwBjnPxFxbMQg4fuuBT9pU2Ak\n",
              "WBezNXJkP+4cowgP6hv2LdvtELfdKN+YNDBIZQ1I6P3K0yxd6HeujnnnZ5gqlh+hLLVtyElbP7/k\n",
              "RAUHYa3m7H7sORbCoRm+JZ1JL/+FhrWMTguRtZ4Y2nu9vy2GzsXGjJy8sS0rA22rfdSDSd77pDhc\n",
              "y2i+r9U4nTxf+HDx9+CbfkSzZiju6ekr3ApMHRb6umtTJQfdJEvNWxfCWWu2cu/5u1yjK9n4OUW8\n",
              "n9Y/KgoFR6NHDfND8MNH9bS7X/i9ZB9UAAACd0GeQniF/wBphHzJJ6iRLkdi+CjygA4O7RQF8VXK\n",
              "dEgyS6mw3acd78uEcw+KJMSJeHqrPIiCS9la6KZAA7aMshLs1vEaIM5F7Zr8TtjN53ywf24GeH78\n",
              "w/GLdCspJmSBr/1iF13FWjYXTSyOv21BNylVl53I1waM4LrcX92uQUO1j9MDGTeHB05tGqN/+d6G\n",
              "8ZG7JYaO9RzmnpbSk9iOou40scMD7d4U53ftzoUTJpRZD34/WxsFcp0kx/sVTGLgP7hfO2h5dONb\n",
              "Vza4h59WAPgxQyyb8S7/6MxGi76e/5wsHdX16WqcCMC/F2r/8O62lfZZDlqtQLhKgt9WQT/7c0WD\n",
              "ZSs628zXjXL8cra4rCmNTIeL5cc/fZkyE9DugxjcKyWRk2f/t7/DqYHF3HTgQ6k+VIQZvEmZV9zh\n",
              "ET4X3aCrF0oLF1Dj+3AL1ja7zvM5v2tnfmahaisb7WkkEClcBeBNA+/KuXjFx2pX82635qZD0vmU\n",
              "1/X4fo0Bo2V2ZhknZYHPhIoF1s8tn78RnpsmqFzRLupAIwHy7JHnBsMgZEpQqA7ifWnqpWcApnF+\n",
              "2kFAyqn1Di7iwrglZRMo1ThUjA5vGNU8/OxNL6GUAbGXH88TykvLS2ONpnPsD7q06TmUK0vfDRGH\n",
              "lFEWvEgT51I54aXTdU+/HJBSfaDZaMo6ly63wLzaSo9ur3rhEx2sir3XzgawcGTT7huVLu5b1Z/M\n",
              "ajPgg6YMX8SK5/ryXWyDiddcvE2h9+BAdE4AxR2F2tYOGszv2W8OeCbqh4jD0/h0IOXtZb1m+8D4\n",
              "aln1ipIezaCW1WC2tp9K70bhswEk5XMRKQmsY/b1E4EAAAIaAZ5hdEK/AJL37dbXDS+c5orBC7DV\n",
              "pCgOqZOqS4TJoLLctBfnS0nJbkKuj8AHWfL7YuYHfhR1LxubLwAPDNpx8nznqOHZWD2aQ4LE/DvP\n",
              "dtEll03SXk3D/wdelbK6/evQNJ6fQpKqK1tBaYL8sm4UZnSFcEqsuKBopvhwk8NfslQwrnR01aX/\n",
              "3Y8BvF301/GE48h7BKkFLzvFfKP/wF0guNGYT3ON6/y6QsFvINns4WTzzkFqaGq5tPFgoPaYNuyR\n",
              "1naM6Lhvu1tjGOLx6LJ/p/fa/k4qzqvll0hecPvdjNsl8TfJBjeIWfEwz1emF7qdaqypK7M1rITy\n",
              "38QqUlBDA4llSLx8/L0KDikyShgEifPt+zLbIP/gusbTE8qiqBZayPKZSC3HqbsL2nYci0N2gfND\n",
              "GRmaGXjdixHiI/dx/o+Ky8QcxeSlP2OeETG/oUqDC1O+g3PHGCQD0dfes9wzU6BLkKax+wboiMwm\n",
              "t8tXGGl8vPS5mgSG9DisFnlYdGBmPpGrm0MgtBkgVoptkwyCQmQKa66U0t9RjfCrdNal7GxEDwzZ\n",
              "69Ws15sm0njXsUspe15Srq3sRNnX+Le+b8lY4cJJu2rEBs5H1VQo626wulXMcGnBD2lbg0N57Ko+\n",
              "DgRH4s39/ihYRavwiR+/lLkdzWsivz9UXnJ/x/a+uAptCqrXPIpF7lr/vwvqKzsTSDXBiPdGcLGv\n",
              "a9v/wAAAArcBnmNqQr8Aksmu3eBQD6dIqrN5I8zGjlyQPXQ05IlGpQN6VsKcbqsSEeO9c4zmNnhd\n",
              "orIc0OPpaYcxbGpFU6IdfgKCpI/tvLE2/7rVjgS0M5FXYKP5mef6aoPiA9l9+r4bj9iXlJy3tfgm\n",
              "/7DWL+1CIM3u4YReLuxsXdNaiuNBZBvx2UMlbX9TK/8rK959YhUHU2mdNvLhJc6mWz7+9N0zUs6F\n",
              "ycDfdeiT7ccPG6EMAN77T9PXHnkGrRypmVcWxVkN4ZdUrgJ0r7UJxhiZ2xvQeZ4AtZIgpg2dA75w\n",
              "yLhbSsDszzqAuty/8HWjt7bHkJpIzSoen8Q83tDc1MVZen+BYPmMdF+mtJUMzAvgjJ4ny+JS0ghe\n",
              "4QuCkb24Pl+GklkL6afJZAiPFX3aP4JtPPlT0/pS0YQTjaDnXLhaYbt0DyLTt8DFNazuWOPWbhNH\n",
              "5cESADqpWUp9CB+EAdRSBmlAeztuBA7x1NBh69cXrhJQ/x6AIAHXt0EL7TR9cHn3wDe5AjA3q6kA\n",
              "3K+pmvqtr/BRt69fIrQf9eB6xDILxyfoYujgsU9Mso3QJGS6QCwhn7EYBZZhe4UKLl4SvRjkgHw+\n",
              "vfv3bkCc51IvkFd5zA272OoGYNNUeimxYGWOwROfVDFSuxynvOC5QwjPvv1eaCM9sgPVUoMxpVPB\n",
              "nvByIcGy+bfGduCZ9/HxaolQXbeJLkWDZUnkxWYMVFzAKBC16vUZdKWUxDTRdfS9rRy+Vv4QKGQw\n",
              "dGItRpPwtzmuYz4L890bye9bmjMxyv0NtbjfLl5hMIbmsqbSs3hrMmBaE7JUvh6AW1ajaphoscu/\n",
              "vl7PPrUYVkBKt5T4YPOG83sMtdjTWCZLE9DwOB3wcd+LRL/NHGeXnyaKeIyvVy2lpXQzptxaxw3P\n",
              "MLBzoRq3AVyFSxDJU/TS0MMfWwAAAchBmmhJqEFomUwIX//+jLACqfDqOpJzmdFAAU/3L3NrXCr7\n",
              "S7+MapX3pW9N2glW7CTol7xgGpXCJtVR1NKA7P8aQtUMFu92504jA/PXqxBELYg0ezA0JJlLzW8w\n",
              "x4l20mQi+Qyhz4NUUEsS9Ar+FLLWRpMVfcaoh5aAoYXApx7DkqaWneNoOHpGW2SWvR1Oj2u9WwG+\n",
              "YlZLMGDGvvGFh8b+PcT2o/9bPUH28Ja4WQE9BXL3VrjKt2v7WKhMpt+ukm4InzSUrj+aTR2JqfzQ\n",
              "6QJe14Vd+Hw1pOQfcngg13VXoBYVR0OLtbnmt+5L5Q3zoydoTYnBgoC5YEiDhqwxdsorKfnXs/yp\n",
              "bkIK7rDUeXq33uGmU6216QRvJD3punVSmwdRMjTiALh/hy1iCyoXF4jOfPT1M4Bzrowgd1I+CZqi\n",
              "Bu3W/H73x9r0+QQl+nJIei/Tn2Wor+G88x2uSo1Z5fWkswSPXpvwfN/l6IJRh1DQvmPOBIhayUN3\n",
              "V2xrbBsBz3eIK/RWragB2NAjr5Z6uTBieDpVPHS7UX2J2cbVYfcCcwP28N/CO5vsREPHKZl0NRpY\n",
              "gsOai0+/++k5U9Aef7PnqGDkTYCWPC8AAAIxQZ6GRREsL/8AZuegWpy6ns5YkrH9QxLvFBidpZft\n",
              "Vs2rCefAAdaHk/nbh+Q1vKYQMSx7BGyOARwR7p6f23ghV/2ZEPqW0MBf9q12W9p7zltjjTa63tZe\n",
              "MDv500S+xpTbuPP69HwP+Jbo5xD7ul4I5K5+ZnC7ecEB6dY0Vi/XYBhB6mXfPhjA98m11dzXvy8q\n",
              "F4w/PekRhdtAz92n8MYKu3bU5pcnYFuVwrRdFP9nU0AIXOF9IDrisrc1uiLKTSX4qj0cD0Pd3JSB\n",
              "9LnrFv8Bt+x1976q6LWlvyyx6lpyTBOqjFhfhc9vjgJYS0+m8Z9LB7uPzbMTyIo5SG0smAjikhqs\n",
              "ZR6MmNJce2Jfxwqte+/4KMzT8CrZ0H6DdLnQEEORDcWwPMrbE+GXDHTjFAp+BTj0QmGQMOZV7i+D\n",
              "vaC9aIv20tVM0F53Xiv1zPBXgGzUXi6elBEQ3W9q+grdNSxB4zVCLVFT/x1TIm0+mvoqkD5wvKnE\n",
              "6VDHOScSZ6irm2NsksJ126Bf75Ly4AwkJfAgxgwsNKz+V1ebahJb4r0Xe3Am1czPl/lXvIYyI2vX\n",
              "dTKipyfOjZuUOXVZkScC/ac41VDGY2mty0SV9zcNXNqC+T9fe5HhnFgJrN5esS+UeaQOuiocG0AU\n",
              "ih8DlM6kBrepklbxiuSzToJMVf1bmRUPNWFTatdvUBdqoaMLbn8hCTiGCWK30B4uBQKjQBVa+pwY\n",
              "+qoOP5hZDf5q3eUeGq+/LtmBAAACLAGepXRCvwCK9+R/9OyiwAcp8vroQr3LDoFvkg+Fdr+71/px\n",
              "gIl928VHPNKITaXdiYEomtKXeJEU1QORlgD7WDKjYwdpnZbQLBm/9hktDKyyz7cr3rTuu5+nt7q9\n",
              "YPZz1SlJBL4ACZLs28E2zmqKQqToFAB8lqcZCbNk0R4f9IgGd3MDxV9eHdpcF+b7RwfhHkDfBJWH\n",
              "O7njgE97Biy3a47VeUn1hlAGtoGVpjhCcI++K15Uz8Pt68nxa7mp2sSso915uCMqe4kfPlVEhsC9\n",
              "Z5ID4ApQjrbZEC9c/9zU4M2FttrqXzj1Ks1qylCa4EHkSCghHoYuBfTdiIrDPAMhL8CVzu8Csv5S\n",
              "XZEbl4vAD/glIB7GleQg33zEvR8Gtdk64fsD+DKf7rM47ZlmKSXwzZfqf5B8hXTDKFMAfRcXe/Sy\n",
              "8cRhsiPcyPSCp08K+J9E9x4011NqtC2qxunH25e+AC+kNZKwT2wRIuzDM6R+MRO4Pcwxj5C6cKXo\n",
              "hUNRTn1L+YD28jgRzHDbkeIrt3wMGVNLfxK8JPyhYAMXF2LvS4bnnD94ELR3Jt4J/URPbkjK1TDT\n",
              "Kk7j9VKesNFwaLysKg4xHuQlbTE8EIXUzrVEXlc0FFMyx7gkOoRGRPygq8YJc9JVZvVjEzgy1+1I\n",
              "scGEk7wFWlR0GTtY2H4y5s3Z0/E6HYKi+v9ztG2fN/6ob2T2kWLoK9emsv5o6rFYeLKTc/+L1whY\n",
              "vOnPpnhuYY8AAAJBAZ6nakK/AIrIT1gDGABUFDufP8kV9LHiHIKERDb7w+5YN9Wx2WlrdHivJokL\n",
              "BTh0MB1A1n377Dea1FSfmtw6rMVV1m/w/cQFZONWlr0wikPusaU8+e0DejFhs4fkpFtsZzzGD/bw\n",
              "Wosu5swxmq3+oBa19Bd3sa//H2Y2Kc3mGlTmTotPC+uQofgZ2mPLoD5w9jYvzPhThE0XV5D5YaeR\n",
              "76olZ/DppSYMHQ84xWzOBVhaQpuF7TottJsL01GK3ynhnkKWbxAyHcVhhaHjAApBpQI1Utpr1JnM\n",
              "Qm+0WRi6qAgqDohOMc6QdbUGkGbuLGSmYQrf+/Mg1RtIqtc32aQ7Ww9ze30XoAKtyK0P/nv/Zxhb\n",
              "20kw/N4V6Q2RyUVPjc1NG1+hwsnMsMFtLHxkBLadA+BqbPWHevgzu2nzgB/kgXx/mCEHm91vKCsQ\n",
              "3l+6WLnDm39Gm2iUwyjq+2a22AocZeaC9mV7PN84jZ+4D+/fH4AwyINKWLHcq5J14WT/RAJ3IZRf\n",
              "wNQZUMSC4odp16IcoH1IBqWR6K/ft99spPNfCN/xf2ihGDrTbqZChLvso/baxBuE+6W4UbGPn1g+\n",
              "SWKIkpRAxL9EfysX6dp4W1ZVbJHF2hqy3OdeQnpZD38VgkjZXSTvqxuaX7A/0UhPH4n7dblxdvhm\n",
              "ts2mhBUPlSovapZjkrH/eRpswM4r86A8zRlAe91/1i6NPUjnVQ3r1bpXHnkw2pd8XdPDXdqIYdjp\n",
              "qNPSRyJog7n3ax4as7VwY+yryAAAAyFBmqlJqEFsmUwIV//+OEAKhu3wNfo8AH4qr6wfiqyQzETS\n",
              "e2Pf+vCIbhGKn3GCU3JXZZ2+7HM7a2ABw0VCWBUwYqeAL4GCOpJmXmYKllRo1fhuzMT+3zO+9sc/\n",
              "2Ln2vNFa7K2wiPoIORJ0j+NH42/9vSo4dZplgHACYQsUaUKcI3DpOwbKn44jGcsKXik1/py8bLp4\n",
              "b5FnnnsnffWBkJivdI7lLb4rIBOn7TjoKZdz98TdRDUXKgKjCwx86AuKfkrtt4rDmz7KhvVPH9MU\n",
              "7yaR3q3IhUoNL58m5cvOOpk2cW5UNGNcT8essdDYBAk4XLAWAJ9Netgb+H1KyQts6FPnT4dxkuCq\n",
              "kDXHvq5pnU2Xgq7XJo+nRdE8a6Gf/hodrxD8kfFoV0sduvtzS2wliQapLUV+dBhLyEBwRSQrqUfg\n",
              "bYDIf4Rz/fTYLBiOddaZjZFahrFdv+VEWyx2ocJKPu/1kzc/cOYHMh2Xmuetvvodt4+UrQTRYjaO\n",
              "zKtHAHygSrior3zvrJfBWq7BoKn2kp5ycmbfkSmsTiBnLsFmToV1wylwCYegA70miLR6iYypvOSF\n",
              "LdmDq+D89tElj0ifYuZpeycHxJpD1YG9TxMSDj5UZOvOVaLgG19sne3KMFSw6LpISAVv6wQbBu9r\n",
              "EQuTQ7RAqxCcyTUuehGzgHZLzDq6oGA07I86AtWLKz65Z/hzvHZ8x1l8WS7sY0Il7gmmEea4kNK8\n",
              "ZuI9aR+6TOjmYS/8QReIIxqqJKVCpx4c9pg+PvwYdhrHJKFRjXmElZiQ0/8uXqEqBxbfXkgrGuse\n",
              "n+2uUudWCNflxIP/X+D+UkBeUWlUGlldazShy7ZG5xOSeXl5kYhVv4Ph1cUsmUqN+OVjy9dndvzk\n",
              "fz16MkLSTuR83A73Q3hiaO3avJwVLrf1RqJ8LSz1jFS5ziHk0CVT/KcUb6R+57mbi7E5IWp6cnnV\n",
              "kOKxsYLLEJ919VZsC6gRz9V+/2SXmn14NnP2XCusXI35b/Akft9e8RHFpiv9xuErAqvP2REwNE3a\n",
              "e00zmFmhXLmqd6Lor+LM1xrXIlHSbE64qmAAAAOmbW9vdgAAAGxtdmhkAAAAAAAAAAAAAAAAAAAD\n",
              "6AAAAZAAAQAAAQAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAABAAAAA\n",
              "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgAAAtB0cmFrAAAAXHRraGQAAAADAAAAAAAAAAAA\n",
              "AAABAAAAAAAAAZAAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAA\n",
              "AABAAAAAAbAAAAEgAAAAAAAkZWR0cwAAABxlbHN0AAAAAAAAAAEAAAGQAAAEAAABAAAAAAJIbWRp\n",
              "YQAAACBtZGhkAAAAAAAAAAAAAAAAAAAyAAAAFABVxAAAAAAALWhkbHIAAAAAAAAAAHZpZGUAAAAA\n",
              "AAAAAAAAAABWaWRlb0hhbmRsZXIAAAAB821pbmYAAAAUdm1oZAAAAAEAAAAAAAAAAAAAACRkaW5m\n",
              "AAAAHGRyZWYAAAAAAAAAAQAAAAx1cmwgAAAAAQAAAbNzdGJsAAAAs3N0c2QAAAAAAAAAAQAAAKNh\n",
              "dmMxAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAbABIABIAAAASAAAAAAAAAABAAAAAAAAAAAAAAAA\n",
              "AAAAAAAAAAAAAAAAAAAAAAAAAAAAGP//AAAAMWF2Y0MBZAAV/+EAGGdkABWs2UGwloQAAAMABAAA\n",
              "AwDIPFi2WAEABmjr48siwAAAABx1dWlka2hA8l8kT8W6OaUbzwMj8wAAAAAAAAAYc3R0cwAAAAAA\n",
              "AAABAAAACgAAAgAAAAAUc3RzcwAAAAAAAAABAAAAAQAAAGBjdHRzAAAAAAAAAAoAAAABAAAEAAAA\n",
              "AAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAA\n",
              "AQAAAgAAAAABAAAEAAAAABxzdHNjAAAAAAAAAAEAAAABAAAACgAAAAEAAAA8c3RzegAAAAAAAAAA\n",
              "AAAACgAACiEAAAOZAAACewAAAh4AAAK7AAABzAAAAjUAAAIwAAACRQAAAyUAAAAUc3RjbwAAAAAA\n",
              "AAABAAAALAAAAGJ1ZHRhAAAAWm1ldGEAAAAAAAAAIWhkbHIAAAAAAAAAAG1kaXJhcHBsAAAAAAAA\n",
              "AAAAAAAALWlsc3QAAAAlqXRvbwAAAB1kYXRhAAAAAQAAAABMYXZmNTcuODMuMTAw\n",
              "\">\n",
              "  Your browser does not support the video tag.\n",
              "</video>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a raster plot of spikes - requires reshaping to 2-dimensional tensor, thus we multiply 1x28x28 to receive 784 vectors with spikes for 10 frames \n",
        "\n",
        "# Reshape\n",
        "spike_data_sample_raster = spike_data_sample_sparse.reshape((10, -1))\n",
        "print(spike_data_sample_raster.shape)\n",
        "\n",
        "# Define plot to show\n",
        "fig = plt.figure(facecolor=\"w\", figsize=(20, 10))\n",
        "ax = fig.add_subplot(111)\n",
        "splt.raster(spike_data_sample_raster, ax, s=10, c=\"black\")\n",
        "\n",
        "# Create labels and show\n",
        "plt.title(\"Input Layer\")\n",
        "plt.xlabel(\"Time step\")\n",
        "plt.ylabel(\"Neuron Number\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "id": "Gv9tuQTaBwel",
        "outputId": "1641169d-4f81-429f-88f3-366f18b63a40"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([10, 784])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find one single neuron from 784 that spikes and index into it\n",
        "\n",
        "idx = 585\n",
        "neuron = spike_data_sample_raster[:,idx]\n",
        "\n",
        "print(neuron.shape)\n",
        "print(neuron)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "epArvKUhh74E",
        "outputId": "f85809b9-9bb1-45ec-8c60-29e00cf7e925"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([10])\n",
            "tensor([0., 0., 1., 0., 1., 0., 0., 0., 0., 1.])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize a spiking neuron\n",
        "\n",
        "fig = plt.figure(facecolor=\"w\", figsize=(8, 1))\n",
        "ax = fig.add_subplot(111)\n",
        "\n",
        "splt.raster(neuron, ax, s=100, c=\"black\", marker=\"|\")\n",
        "\n",
        "plt.title(\"Input Neuron\")\n",
        "plt.xlabel(\"Time step\")\n",
        "plt.yticks([])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "_x55MZDJaxtT",
        "outputId": "36aecc0f-b457-455e-da70-2ea4e8aada8c"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Latency encoding\n",
        "\n",
        "Some of the arguments include:\n",
        "\n",
        "* `tau`: speed of firing - a higher ``tau`` will induce slower firing.\n",
        "* `threshold`: the membrane potential firing threshold - all values below the threshold are clipped and assigned to the final time step."
      ],
      "metadata": {
        "id": "TO-0DHWY1KJ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spike_data = spikegen.latency(inputs, num_steps=100, tau=5, threshold=0.01)\n",
        "spike_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tl0DT19A1NLk",
        "outputId": "b2372d9c-5ee3-4032-d52f-d6202041aa9c"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([100, 128, 1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "spike_data_sample = spike_data[:, 0, 0]\n",
        "print(spike_data_sample.size())\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "anim = splt.animator(spike_data_sample, fig, ax)\n",
        "\n",
        "HTML(anim.to_html5_video())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "nlzmq2W23xcI",
        "outputId": "cbaab693-2daa-42b5-bf68-9c35b60d5d97"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([100, 28, 28])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<video width=\"432\" height=\"288\" controls autoplay loop>\n",
              "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAAAdvW1kYXQAAAKuBgX//6rcRem9\n",
              "5tlIt5Ys2CDZI+7veDI2NCAtIGNvcmUgMTUyIHIyODU0IGU5YTU5MDMgLSBILjI2NC9NUEVHLTQg\n",
              "QVZDIGNvZGVjIC0gQ29weWxlZnQgMjAwMy0yMDE3IC0gaHR0cDovL3d3dy52aWRlb2xhbi5vcmcv\n",
              "eDI2NC5odG1sIC0gb3B0aW9uczogY2FiYWM9MSByZWY9MyBkZWJsb2NrPTE6MDowIGFuYWx5c2U9\n",
              "MHgzOjB4MTEzIG1lPWhleCBzdWJtZT03IHBzeT0xIHBzeV9yZD0xLjAwOjAuMDAgbWl4ZWRfcmVm\n",
              "PTEgbWVfcmFuZ2U9MTYgY2hyb21hX21lPTEgdHJlbGxpcz0xIDh4OGRjdD0xIGNxbT0wIGRlYWR6\n",
              "b25lPTIxLDExIGZhc3RfcHNraXA9MSBjaHJvbWFfcXBfb2Zmc2V0PS0yIHRocmVhZHM9MyBsb29r\n",
              "YWhlYWRfdGhyZWFkcz0xIHNsaWNlZF90aHJlYWRzPTAgbnI9MCBkZWNpbWF0ZT0xIGludGVybGFj\n",
              "ZWQ9MCBibHVyYXlfY29tcGF0PTAgY29uc3RyYWluZWRfaW50cmE9MCBiZnJhbWVzPTMgYl9weXJh\n",
              "bWlkPTIgYl9hZGFwdD0xIGJfYmlhcz0wIGRpcmVjdD0xIHdlaWdodGI9MSBvcGVuX2dvcD0wIHdl\n",
              "aWdodHA9MiBrZXlpbnQ9MjUwIGtleWludF9taW49MjUgc2NlbmVjdXQ9NDAgaW50cmFfcmVmcmVz\n",
              "aD0wIHJjX2xvb2thaGVhZD00MCByYz1jcmYgbWJ0cmVlPTEgY3JmPTIzLjAgcWNvbXA9MC42MCBx\n",
              "cG1pbj0wIHFwbWF4PTY5IHFwc3RlcD00IGlwX3JhdGlvPTEuNDAgYXE9MToxLjAwAIAAAAZfZYiE\n",
              "ADv//vdOvwKbRZdqA5JXCvbKpCZZuVJrAfKmAAADAAARsG58lw15q9IkAACtobhz+EtXgAGbPfnA\n",
              "6vBq2y4KD1VFSYr8QiGU1RZ6il6SV2xj+fxPL8Qg2QVIKd1lIO+ebaj7Zv4WsMdmfuuUAqmGmgt7\n",
              "2qfAnam+BtJ+fYoQA9tZoVhX7v3hsPbYsyMm3HgDts+PZfA450/77h+qcv4tHMfO0JJ086eZwkzO\n",
              "ITgaEOSIKRHtgqOjS7OCz278baBUJJioTdV0TcCWQItUN8jW+11acWCQgOKUMiWUtYFuQEBL7g2o\n",
              "JWFdU71ynliQrNteCmHol1AE7G5WtyPuU0u4pwL1emtcptiZnGxxYP8bx68NdPdyK3e+TKRBypl/\n",
              "Jfq5Y/BMvcdkSEcdZ0OHnJpL8PAUm7MxjSEwjOmiKsruRc9IiVJOMYDvp2cIhginpnKy3D/S/0Di\n",
              "8n/oTDJkt3/Kf380L+fJ7pZi0QX6/RFwgcy2g85DAZonhIGcP20uxe929LUTF5109Jm9j7wO/Q7i\n",
              "G25KtxHTklFi3Zyt02hWfyeYdrW1/wfRDeV2CIy+JIpLHpdOAhPCtLi7i9TzIoOK/3qsLJnQy6iz\n",
              "qjeJw1vcJSCeDL+kAFWfBTgMQ3J7x/xz9ahSrtPBSMZno7xi2P+az4lrKtXrc+4SZBsYq2e6MQ7r\n",
              "hFx0Yxrr80PLtFRBYBiqfth3uGGlmoMbmeGVHKq7t/914ar08gM4DfHg8ZTT5XZG337pYlFaOn4S\n",
              "CD1r3o9vuVUWj0N+4ACcmgFPouOu8/u6Q9wB+ZC8a8xA3tfvBcMtp8EiUDrPZcIO1/SuUfb6K+od\n",
              "Vg1Iij7+uDQUH3WzEqxRFAYP8xems97agHVRUnNLwCeqK2PUW4/z/teUsd1GQWtWbJSHHz7GoCPq\n",
              "UlwjtJ138bHw4SzrSi8sBAJBwRdPRTjqVQkJmV032K9NviV82Hmj6KHuGJXh1/LciavSYRdPrr3R\n",
              "991n+PRKyoRo1G2LlrJtbP1sb5tKPzIwFK245YfUrwTZ1klRpe5VHyo6l/IVpUeXaLHg+t19razT\n",
              "ImtVsBwTpZvuBxddcR6IATySOmPPzEzW8VUw9fI/7SlN8OZznpVVIkBqx80kwuXpU/0BjSysqRur\n",
              "oc+8WND9zzsXvm/X0qvkQTWY2glW/6Alkyri0ge4q5QMXTo3jNvNKM7f0b0BKeDT41nXeqeAOfDC\n",
              "qvWhyrQAOXg937TP1zkCPCv1+WYx8eFIX9PR2Tu3Tuv3sE+g79JKznf9e94uiJmzZsM3fOLbSD5S\n",
              "3LOiAbGV4X6f1F1wIxMViumIF3dwb1TlIS8WK3/VZNLZjQUahER67PCwEILdN0h967iDAS4H48Ek\n",
              "j2lSTUw3iifvyAR2wnx6tDGuVZ6daiGvAo31ZpoBLdG4uHDg82hRemBrgTrkCDZuSNBTeERnWPsV\n",
              "gOFiDIisjpzQPiL0ZENkkzlH5oiOjlgvrTRH7OUlqpx1kczmQK5xbGwhq5ilHrBN/A6X70c/R2Dx\n",
              "vq4HogatGIJNHiYeY3M5rHFwzGSq3hOfD1Fd2i5pruVw4PA1WU25X42oxWlPdhIGdzoj8jPGBAr+\n",
              "K3Ao9Uue6fQpvANYp2Q/FIpFKD4Wz2xQ5Se4ZSHJrAbo+mdcpRn9CzH1w730I6naKvJkQ8AkePzd\n",
              "idCABRdYdPv1pxZd2qlamp9+QR5MuNSb5WFSjx+PfrA8oikYCukPLIJLw3eWtX//k9QwyiWs68BH\n",
              "y5qLIMZn3496hE1yyiUVHUQQ8OkblDHYPVMdw2MrnEx/8RKlP9omp3HyVTQM7u2V5v1GALZYnHaV\n",
              "iduY3c6fQxPrfPCBzm6rxwMRQK67zutQOxWyIAe+ApSwcb75NQCGKl0Q96TggMIMTO86ucTPtdUG\n",
              "6G9pc3FhbgE2cPIBWUJyCakk7w16mSooeM+FnIIleGSWsYwynaCFXM4G/gTWGSguuvzADDgkvzi6\n",
              "6Rq7tPysDRGDftA6mHBDIEbydIhvObPLPR/W48CIaK9ZrsNX9J7MBHMqf095UHSOkAyqSB+Yp+c+\n",
              "CVyu4h+DecTLAG3guFuzy41bbj1pDz5xKUdBfyhgXOAb0fO19/IfDNIfGEYFk69YZXh//QFaF8vC\n",
              "nPTfz7s4M+vho7Fw0XvY4kMzYnVuN2kC0AABn6IqOOEAAAF0QZoibEO//qmWAJAwj0AIdw6a6ANz\n",
              "1w/pTsUDPAEcrRDBmhKb2f12isft0dl7feYV/y82gyInoE8/eIXtJh9i5/kuMTW7DfXQorh4tZsf\n",
              "dipRzEA89jKaH5vYlV0B9n3csbk7kJYOGFhk83v5O/L++jTFr0CGrsWYN8o5X+wKEQHeKZYUNtB7\n",
              "M2kiASj/gy/sVKLLIlA9jTYTM8OvsTWm85+vmUIXSN0JQhJKUjfA7rMFRS9CkhVTDXp7+/7uyuHl\n",
              "/nPb7TSJM1lPR3ZkkOgDZhXAWWUZaDgkyTtE2cszOV7WgHlP5tEhGzRxonEWOWWgnkQ7rRU7VrTs\n",
              "glK0URi3TSF8Eg3wcrE10Ab73SCDiHc2oSGwnN10o8fMs25L5b1aCeYnLfINaDfvEjHUlZm2UC9A\n",
              "yM6pL+3NRpBKcIKlxGBYfLGT75Kjes3eNV/e0e6cAbiqkrKPglbIm5CyHbmQwmhHsMZ+8KUyYJFj\n",
              "b+h1ecDrOv0gAAAB2QGeQXkK/wCW+FKffGgqg/HKYUkMXA2LcIATLoVOYaTVkhmImmuSNSUucPqq\n",
              "aa/FIjmZQjhm/e8L4/DBQMQH18DuK/k8V0BkTt4vgraH7GXJkEsnKkK1b3f1WvzfaAASkFdv3Yqk\n",
              "abCTqvNEHydX5IJWavjLo0upFvcQKaczKfdThK6zaBg5m7P+/pIOqQtnE0Th5DUE9cU5+MbLmO28\n",
              "AIaDHh/GtrAEcU1za0jv1H7gKbYd1Yfl8+wDG/B9m/HIPiEKRBKEUJvX7NWbAtBaX4TLEa44nqON\n",
              "XZnMNcM3DseLyGwXOTTT/iRxTMK7yDwlUf7rf7RH+ytQNU0C5sTP+SxnN8ZYMqWjz2M9DFvuw+ri\n",
              "DQT7B+I7LHR2yMrPgtrh6xTcuLteUs1ergyfq0GiU2dy0V+uevnHkITBi3eivFEA2vaRdFZcOh8P\n",
              "7HUPGhk6Y7frCEhrT6lSlJlsSO81x15H2mfZuydgiQoopP6MtIbyuYwNu5fqyijH1XvwrHV4uD3g\n",
              "tCF2QiqrXZSDOkSqDUFNH87Y3NBdSd5wM1IsfVdOWI6E/QHoMMlGQI702epb6Oq7Apsfvd8/vgR/\n",
              "XR06kWV14ObTAYYw1wpesKh8prYibQGAV5i1JEnBAAAALkGaRjwhkymEO//+qZYAW/4+hl9mZz23\n",
              "b2QOsSrCs/Fv9TTLuycNV3RMH6CU4YAAAADCQZ5kalPC/wBuoiqJy17eDkXSU739hINCA+PzF/17\n",
              "G+GmRDgAiD0tE+HSD0t389psIjBr0L6K0FCKmhlHWzarsJZjTS6PQJysadVR6ucpWVIi+AUleHif\n",
              "muksiAdF37qSr7sMzoBpp0R6cpoXqF4E89lt5PGrePcsjuGWaD24OUTIhddaCmJ8SSp0IvMOamo+\n",
              "XARivkPrTFV0xg1XhBIXXdQXE54W+Lt8MxOJcfmQtBowbNShBmh/MgeYVz4g+xlKulkAAAChAZ6D\n",
              "dEK/AJa6FAnL+w1Sp18L1FOb1jv6cizCuGnz2ZxlEDqdw8tML+5nABzevzjdxwM0qvBfcSlQcij3\n",
              "ZXq3x7rTl8e9gYeI6nlQA10/F1DQZnRIb9P8hFil79AL6WGDdNPSlnDgocCzeB1aqkZb+7XQXWUr\n",
              "S/9oqTqV6dRm7Phirhk6Uw2xWt38Y7KlOv9r2eTU7G5BYr4A2fbrvB3Z/38AAABDAZ6FakK/AJbs\n",
              "ALNDCdFfoGCgAOtAJWdFVRWQ+7qYtIQLdoK3g7EQX4eA1Xcw8zWmzbRZCGBzkRY7BFtXdeAlCPv4\n",
              "OQAAABVBmopJqEFomUwId//+qZYAAAMA8IEAAAAUQZ6oRREsL/8AbqI3PJyrYikwm0AAAAAPAZ7H\n",
              "dEK/AJa6GbFUpYAQAAAAvAGeyWpCvwCW6/3lI7TEZaOgBMnn/ZQeNv+2Bf7hK190ce8HCHwHIOzD\n",
              "6aE9G/rTt+YO495fvPoms0zNoZE65ETQCiZRgIc/jwy8fgcdjhvAlFhBCUvWlFK2TOn+u77z+/v2\n",
              "IZ8KA+i5JkVe0vABQ/sGqkEYlu8hfl9juVQEOYmNcpc2VpAVd+M0glZMIgjBDVEov96GgfYHGoGd\n",
              "RKqt0l0AFST8QDrj4cje9qx6BAR5ilY5QVDMm6TlkTkPAAAAFUGazkmoQWyZTAh3//6plgAAAwDw\n",
              "gAAAABRBnuxFFSwv/wBuojc8nKtiKTCbQAAAAA8Bnwt0Qr8AlroZsVSlgBEAAAAPAZ8NakK/AJbr\n",
              "+7FcahsxAAAAFUGbEkmoQWyZTAh3//6plgAAAwDwgQAAABRBnzBFFSwv/wBuojc8nKtiKTCbQAAA\n",
              "AA8Bn090Qr8AlroZsVSlgBAAAAAPAZ9RakK/AJbr+7FcahsxAAAAFUGbVkmoQWyZTAh3//6plgAA\n",
              "AwDwgAAAABRBn3RFFSwv/wBuojc8nKtiKTCbQAAAAA8Bn5N0Qr8AlroZsVSlgBEAAAAPAZ+VakK/\n",
              "AJbr+7FcahswAAAAFUGbmkmoQWyZTAh3//6plgAAAwDwgQAAABRBn7hFFSwv/wBuojc8nKtiKTCb\n",
              "QQAAAA8Bn9d0Qr8AlroZsVSlgBAAAAAPAZ/ZakK/AJbr+7FcahsxAAAAFUGb3kmoQWyZTAh3//6p\n",
              "lgAAAwDwgAAAABRBn/xFFSwv/wBuojc8nKtiKTCbQQAAAA8Bnht0Qr8AlroZsVSlgBEAAAAPAZ4d\n",
              "akK/AJbr+7FcahswAAAAFUGaAkmoQWyZTAh3//6plgAAAwDwgAAAABRBniBFFSwv/wBuojc8nKti\n",
              "KTCbQQAAAA8Bnl90Qr8AlroZsVSlgBAAAAAPAZ5BakK/AJbr+7FcahsxAAAAFUGaRkmoQWyZTAh3\n",
              "//6plgAAAwDwgAAAABRBnmRFFSwv/wBuojc8nKtiKTCbQQAAAA8BnoN0Qr8AlroZsVSlgBEAAAAP\n",
              "AZ6FakK/AJbr+7FcahsxAAAAFUGaikmoQWyZTAh3//6plgAAAwDwgQAAABRBnqhFFSwv/wBuojc8\n",
              "nKtiKTCbQAAAAA8Bnsd0Qr8AlroZsVSlgBAAAAAPAZ7JakK/AJbr+7FcahsxAAAAFUGazkmoQWyZ\n",
              "TAh3//6plgAAAwDwgAAAABRBnuxFFSwv/wBuojc8nKtiKTCbQAAAAA8Bnwt0Qr8AlroZsVSlgBEA\n",
              "AAAPAZ8NakK/AJbr+7FcahsxAAAAFUGbEkmoQWyZTAh3//6plgAAAwDwgQAAABRBnzBFFSwv/wBu\n",
              "ojc8nKtiKTCbQAAAAA8Bn090Qr8AlroZsVSlgBAAAAAPAZ9RakK/AJbr+7FcahsxAAAAIUGbVkmo\n",
              "QWyZTAh3//6plgACQGelAH1TU3hulv8ZWxcXqgAAABRBn3RFFSwv/wBuojc8nKtiKTCbQAAAAA8B\n",
              "n5N0Qr8AlroZsVSlgBEAAAAPAZ+VakK/AJbr+7FcahswAAAAFEGbmUmoQWyZTAhv//6nhAAAAwHd\n",
              "AAAAD0Gft0UVLCv/AAB/K9JraQAAAA0Bn9hqQr8AAH8ZTJXgAAAGE0Gb2kmoQWyZTAh3//6plgDc\n",
              "Xx9HB/96gAOcW16XkrC+lxjz/5b/MEeuleGuPzCl7Mfdxt5iPv8AAAMAGz+Hwa7FFu4XFMcmJpZ5\n",
              "n/+aGfAiDA9b8UnW1ITmEW9vjdhzpMHXMWk96yiLTi/HbYuuOJoqOeCR/KyIQDc9Gfzpn47Sy8zo\n",
              "gvWsDma+E/on37h+7aS4i7xfnPZbQQ8ECOKPoPcZLvgq4a85nh+f7wIJKYHgt7jBMv8AJtU14MTr\n",
              "uELXtmCf1qfoPKu9U3GEluVNzb3LePOdj7tKQ2l1gDusDtPGCZS3bf4ugMJIMHTAyeWN27AfPMBX\n",
              "CYbHvLsJ6rjmiLXuowus0W5MfyiLrMKr7OSIi1vbknGLNTN6nSw8sJeyeaSuxRNutVkvJFKpec0P\n",
              "mZ5K9xEX6AfRv5z/cz8+iBbqb5F00490G/lvacKUFo5fzfvaRmB1Ovmhi9wV0W60mFXOx2kAc6tj\n",
              "0WB1eRtMPOo9SdlTKHw5dNQG0c5r1jRPJ++mPOJ5826uW+bimrEvFa3FjhLPVYkuDz3t9LAvYVTZ\n",
              "ejvoEs/qnjCmDg8c/pmzshBlVoofK+gy87wpeBs8crt2Q1jEJwCecW8kPDjmhvIcU4A5E3g9o5Jz\n",
              "pEvnFoGc6Sficlmgefp79VUvbTukzc4feLXwCjwob2aHwTtq0T96q58gHZ+y54Jv6HDrpfT8H9l4\n",
              "UgMkY6AZIO7J/KOrSIUTsrE22V0uZ3/o1xx+64G9J2fvFwtp9V8GcoIBH+h9UbJdUuSVIr33pdK4\n",
              "RA0YS/RzQRwXG1ysh7j4CII5QbuDSvW6HhpYnGPq7k5HbrvQq0Gy3NaTgP5t9xL+C17H7420IsRu\n",
              "ZmF5l9a22uKhewmvk7Ux4NmH2vZ9zhz6x95mbasG3q9Bj0BoSZdvlygOVDfazbOKxHguhckx9DuO\n",
              "FM/yXE89qmmmkHH0nYhSXAG/ZseHAJe8euP4AwUAsgIdgVOZI2yQYBJ4B4z+2ufr9vREiFRRauNo\n",
              "xB1ajHhEDpva3F3HRJTDBkLOE2j2WIQORjHPHV6L2jNHEH24UDmo9eW1Ifx0fBn0r0H1FOjXyB+B\n",
              "Xw7VvapEhbStYmn8mC98qoup2rUbB/3xklDWOzL/SuuCpc+l57SXgiT/qun1hkDHx9svLtPVasGc\n",
              "TKIIsdADklTiYObkiJsA4Rd6oHHi2TIsf7m2B6qXqAuFR8dQWRjC2sCN1GVcsetm4qbntGwIk8rJ\n",
              "QajFBqqdU88YUg7bHBAxCQGHyngGrbjniZ3npQjR6ryFw0eJyhxpKmu0RkG5X+F9hIuCK3OjjPzH\n",
              "hT2NY4huOWuYTQaKcMuNvSegK5ZNHhuP+kQjxs/N14T8aN3ZGFxH3IsFfcUQUzStpK0LeOQMiTWt\n",
              "bB4CySxLLuSZ4uH3PM2JFPHIb8D/3KHhGv/5/Iwethjv8Q1uy4YJI5CceFortAJDCJ0+NZ5FVrQJ\n",
              "d7wablq5rouFBlzX7pUZmJVW8xwbp0qX7JVlaMmqFGhQFX1YQDbOlRtNRtm6R9D+efIHPCEWQ57/\n",
              "s9nva2CACJ3u7Rt9n0YIjcuE6kSI5vmikDFTNrjlmCsqxMAnyVMvpEJJRfTnTu0QAz0SK/zAZSUI\n",
              "+luPruQsP25Fup6utLBbtXpjO6X4nOtsYXHgBvlU9TJameOAzdyLeXks88WNkvhTk8lvc986f/yh\n",
              "LiMoWTyrdO2DBdGth6SKFgvqK/JxRAzHkyDjlkET3JzjARBq5nEgYCuCorHESg9FzKayC8olenoR\n",
              "hgoS5VBfeYlRw4QlGsIUzLuee9ujKjqIKfXw+tKDWfWDNy5CkSFR1I9NlRUQndq055DewXPd5PCZ\n",
              "GPCdwWEJlqVkR6MCJNFAxmRYFOPxM1ReRX49d8lcq0s/L0EEL76qSLtG4eQOOf+3aWeuq6r6BVXz\n",
              "87VJq1Na5YbUArFVqpjfbFnQ/ZuAx1PMtLgXUEn/o6xW7EzGSm0B+1giviChtNrzKFzdqLikuSEH\n",
              "Jzi74WX9QUSjV4q1h2zUroDhRk75c3Q/BH/LYlF0EC3kQd7v9J/dCQdOLeQPnfIB/CB87kP6puEA\n",
              "AABkQZv+SeEKUmUwId/+qZYA2kp1gAG7uGxi6u56ZCK29YH3G4vvSDeRS4CJjINtWB+nCLxXpoqp\n",
              "ril0dtFdwC0xqgACa32gC+aKYBprOs+LoYLkVFpyJz9ZMd1M1/Ze2T2jVdzOgAAAAChBnhxFNEwv\n",
              "/wIdzoLU40sTsKWaPw6uIETbXT4HSyODUQ5I7D3nRerpAAAARwGeO3RCvwLNMaNzB9aG0PC1MAAF\n",
              "IsmnGDHHN6n8/WnqbWvQEnxLYQQp4i9HlLWCm9iizO6yQ+Tax+mvCHHUv0DK3KHYIlE7AAAAEQGe\n",
              "PWpCvwLOwLUIBF4lgFBAAAAAK0GaIkmoQWiZTAh3//6plgBoPbQgndizMvqzzVwj0RzfmRXSlGFV\n",
              "HSyfPBgAAAATQZ5ARREsL/8CHc6FKPJXrpYBQQAAABEBnn90Qr8CzTGjcwf4EwCBgAAAABEBnmFq\n",
              "Qr8CzsC1CAReJYBQQQAAABVBmmZJqEFsmUwId//+qZYAAAMA8IAAAAATQZ6ERRUsL/8CHc6FKPJX\n",
              "rpYBQQAAABEBnqN0Qr8CzTGjcwf4EwCBgQAAABEBnqVqQr8CzsC1CAReJYBQQQAAABVBmqpJqEFs\n",
              "mUwId//+qZYAAAMA8IEAAAATQZ7IRRUsL/8CHc6FKPJXrpYBQQAAABEBnud0Qr8CzTGjcwf4EwCB\n",
              "gAAAABEBnulqQr8CzsC1CAReJYBQQQAAABVBmu5JqEFsmUwId//+qZYAAAMA8IAAAAATQZ8MRRUs\n",
              "L/8CHc6FKPJXrpYBQQAAABEBnyt0Qr8CzTGjcwf4EwCBgQAAABEBny1qQr8CzsC1CAReJYBQQQAA\n",
              "ABVBmzJJqEFsmUwId//+qZYAAAMA8IEAAAATQZ9QRRUsL/8CHc6FKPJXrpYBQQAAABEBn290Qr8C\n",
              "zTGjcwf4EwCBgAAAABEBn3FqQr8CzsC1CAReJYBQQQAAABVBm3ZJqEFsmUwId//+qZYAAAMA8IAA\n",
              "AAATQZ+URRUsL/8CHc6FKPJXrpYBQQAAABEBn7N0Qr8CzTGjcwf4EwCBgQAAABEBn7VqQr8CzsC1\n",
              "CAReJYBQQAAAABRBm7pJqEFsmUwIb//+p4QAAAMB3QAAABNBn9hFFSwv/wIdzoUo8leulgFBAAAA\n",
              "EQGf93RCvwLNMaNzB/gTAIGAAAAAEQGf+WpCvwLOwLUIBF4lgFBBAAAAFEGb/kmoQWyZTAhv//6n\n",
              "hAAAAwHdAAAAE0GeHEUVLC//Ah3OhSjyV66WAUEAAAARAZ47dEK/As0xo3MH+BMAgYEAAAARAZ49\n",
              "akK/As7AtQgEXiWAUEAAAAATQZoiSahBbJlMCF///oywAAAHVAAAABNBnkBFFSwv/wIdzoUo8leu\n",
              "lgFBAAAAEQGef3RCvwLNMaNzB/gTAIGAAAAAEQGeYWpCvwLOwLUIBF4lgFBBAAAAE0GaY0moQWyZ\n",
              "TAhX//44QAAAHHAAAAfWbW9vdgAAAGxtdmhkAAAAAAAAAAAAAAAAAAAD6AAAD6AAAQAAAQAAAAAA\n",
              "AAAAAAAAAAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAA\n",
              "AAAAAAAAAAAAAAAAAgAABwB0cmFrAAAAXHRraGQAAAADAAAAAAAAAAAAAAABAAAAAAAAD6AAAAAA\n",
              "AAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAABAAAAAAbAAAAEgAAAA\n",
              "AAAkZWR0cwAAABxlbHN0AAAAAAAAAAEAAA+gAAAEAAABAAAAAAZ4bWRpYQAAACBtZGhkAAAAAAAA\n",
              "AAAAAAAAAAAyAAAAyABVxAAAAAAALWhkbHIAAAAAAAAAAHZpZGUAAAAAAAAAAAAAAABWaWRlb0hh\n",
              "bmRsZXIAAAAGI21pbmYAAAAUdm1oZAAAAAEAAAAAAAAAAAAAACRkaW5mAAAAHGRyZWYAAAAAAAAA\n",
              "AQAAAAx1cmwgAAAAAQAABeNzdGJsAAAAs3N0c2QAAAAAAAAAAQAAAKNhdmMxAAAAAAAAAAEAAAAA\n",
              "AAAAAAAAAAAAAAAAAbABIABIAAAASAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
              "AAAAAAAAGP//AAAAMWF2Y0MBZAAV/+EAGGdkABWs2UGwloQAAAMABAAAAwDIPFi2WAEABmjr48si\n",
              "wAAAABx1dWlka2hA8l8kT8W6OaUbzwMj8wAAAAAAAAAYc3R0cwAAAAAAAAABAAAAZAAAAgAAAAAU\n",
              "c3RzcwAAAAAAAAABAAAAAQAAAyhjdHRzAAAAAAAAAGMAAAABAAAEAAAAAAEAAAYAAAAAAQAAAgAA\n",
              "AAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAA\n",
              "AAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAA\n",
              "AQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAAB\n",
              "AAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEA\n",
              "AAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAA\n",
              "AgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAA\n",
              "AAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQA\n",
              "AAAAAQAAAAAAAAABAAACAAAAAAEAAAgAAAAAAgAAAgAAAAABAAAEAAAAAAEAAAoAAAAAAQAABAAA\n",
              "AAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAA\n",
              "AAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAA\n",
              "AQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAAB\n",
              "AAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEA\n",
              "AAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAA\n",
              "BAAAAAABAAAAAAAAAAEAAAIAAAAAAQAABAAAAAAcc3RzYwAAAAAAAAABAAAAAQAAAGQAAAABAAAB\n",
              "pHN0c3oAAAAAAAAAAAAAAGQAAAkVAAABeAAAAd0AAAAyAAAAxgAAAKUAAABHAAAAGQAAABgAAAAT\n",
              "AAAAwAAAABkAAAAYAAAAEwAAABMAAAAZAAAAGAAAABMAAAATAAAAGQAAABgAAAATAAAAEwAAABkA\n",
              "AAAYAAAAEwAAABMAAAAZAAAAGAAAABMAAAATAAAAGQAAABgAAAATAAAAEwAAABkAAAAYAAAAEwAA\n",
              "ABMAAAAZAAAAGAAAABMAAAATAAAAGQAAABgAAAATAAAAEwAAABkAAAAYAAAAEwAAABMAAAAlAAAA\n",
              "GAAAABMAAAATAAAAGAAAABMAAAARAAAGFwAAAGgAAAAsAAAASwAAABUAAAAvAAAAFwAAABUAAAAV\n",
              "AAAAGQAAABcAAAAVAAAAFQAAABkAAAAXAAAAFQAAABUAAAAZAAAAFwAAABUAAAAVAAAAGQAAABcA\n",
              "AAAVAAAAFQAAABkAAAAXAAAAFQAAABUAAAAYAAAAFwAAABUAAAAVAAAAGAAAABcAAAAVAAAAFQAA\n",
              "ABcAAAAXAAAAFQAAABUAAAAXAAAAFHN0Y28AAAAAAAAAAQAAACwAAABidWR0YQAAAFptZXRhAAAA\n",
              "AAAAACFoZGxyAAAAAAAAAABtZGlyYXBwbAAAAAAAAAAAAAAAAC1pbHN0AAAAJal0b28AAAAdZGF0\n",
              "YQAAAAEAAAAATGF2ZjU3LjgzLjEwMA==\n",
              "\">\n",
              "  Your browser does not support the video tag.\n",
              "</video>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Raster plot\n",
        "fig = plt.figure(facecolor=\"w\", figsize=(10, 5))\n",
        "ax = fig.add_subplot(111)\n",
        "splt.raster(spike_data[:, 0].reshape((100, -1)), ax, s=10, c=\"black\")\n",
        "\n",
        "plt.title(\"Input Layer\")\n",
        "plt.xlabel(\"Time step\")\n",
        "plt.ylabel(\"Neuron Number\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "plZjXOz41yrK",
        "outputId": "626269ee-7509-4ee9-f2c4-0fbed8f47f1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add normalization, linerialization and clipping to smooth the plot\n",
        "\n",
        "spike_data_smooth = spikegen.latency(inputs, num_steps=100, tau=5, threshold=0.01, \n",
        "                              clip=True, normalize=True, linear=True)\n",
        "\n",
        "fig = plt.figure(facecolor=\"w\", figsize=(10, 5))\n",
        "ax = fig.add_subplot(111)\n",
        "splt.raster(spike_data_smooth[:, 0].reshape((10, -1)), ax, s=10, c=\"black\")\n",
        "\n",
        "plt.title(\"Input Layer\")\n",
        "plt.xlabel(\"Time step\")\n",
        "plt.ylabel(\"Neuron Number\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "GxWlO3co3FiH",
        "outputId": "8c41684d-4132-4e1f-be51-23c4f07fbe23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "spike_data_sample_smooth = spike_data_smooth[:, 0, 0]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "anim = splt.animator(spike_data_sample_smooth, fig, ax)\n",
        "\n",
        "HTML(anim.to_html5_video())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "yYwOAptU4Rpv",
        "outputId": "546da0af-f05a-41a7-baa8-bbfabb44cec0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<video width=\"432\" height=\"288\" controls autoplay loop>\n",
              "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAAAxV21kYXQAAAKuBgX//6rcRem9\n",
              "5tlIt5Ys2CDZI+7veDI2NCAtIGNvcmUgMTUyIHIyODU0IGU5YTU5MDMgLSBILjI2NC9NUEVHLTQg\n",
              "QVZDIGNvZGVjIC0gQ29weWxlZnQgMjAwMy0yMDE3IC0gaHR0cDovL3d3dy52aWRlb2xhbi5vcmcv\n",
              "eDI2NC5odG1sIC0gb3B0aW9uczogY2FiYWM9MSByZWY9MyBkZWJsb2NrPTE6MDowIGFuYWx5c2U9\n",
              "MHgzOjB4MTEzIG1lPWhleCBzdWJtZT03IHBzeT0xIHBzeV9yZD0xLjAwOjAuMDAgbWl4ZWRfcmVm\n",
              "PTEgbWVfcmFuZ2U9MTYgY2hyb21hX21lPTEgdHJlbGxpcz0xIDh4OGRjdD0xIGNxbT0wIGRlYWR6\n",
              "b25lPTIxLDExIGZhc3RfcHNraXA9MSBjaHJvbWFfcXBfb2Zmc2V0PS0yIHRocmVhZHM9MyBsb29r\n",
              "YWhlYWRfdGhyZWFkcz0xIHNsaWNlZF90aHJlYWRzPTAgbnI9MCBkZWNpbWF0ZT0xIGludGVybGFj\n",
              "ZWQ9MCBibHVyYXlfY29tcGF0PTAgY29uc3RyYWluZWRfaW50cmE9MCBiZnJhbWVzPTMgYl9weXJh\n",
              "bWlkPTIgYl9hZGFwdD0xIGJfYmlhcz0wIGRpcmVjdD0xIHdlaWdodGI9MSBvcGVuX2dvcD0wIHdl\n",
              "aWdodHA9MiBrZXlpbnQ9MjUwIGtleWludF9taW49MjUgc2NlbmVjdXQ9NDAgaW50cmFfcmVmcmVz\n",
              "aD0wIHJjX2xvb2thaGVhZD00MCByYz1jcmYgbWJ0cmVlPTEgY3JmPTIzLjAgcWNvbXA9MC42MCBx\n",
              "cG1pbj0wIHFwbWF4PTY5IHFwc3RlcD00IGlwX3JhdGlvPTEuNDAgYXE9MToxLjAwAIAAAAE+ZYiE\n",
              "ADf//vbw/gU2O5jQlxHN6J0zH78VuLo0N73OAAADAAA33OZE/sqTBubAAALgmC7L4SFUAAfhAscm\n",
              "5bJZnZTB5Nap1flGt/pbkllXXwqb4LGeJf597CTJWLOk3rMJKPLvPSymyf/5DuZ5WFtW880g/NzZ\n",
              "hp6duQxvWV+brHrti04QiKeF0/R1EYdsqsd8RAo29+2kVhuVfDD1osafX0OJHgKDSAH1rAYGxvtM\n",
              "ttU/MLnLxVIocPNd7HCRWxLgpU4wkSF65Q4ksGQUtdVIgHr3oHZMwZVuQF3qb/CguhWk2YFcIYSL\n",
              "qboxNOrcF97/UWg2JDZ8TUZkQWeSLPJ14n+y6jV/JOzeHHg005dKeQ2dbOaPqY55oznbwD1B74Q/\n",
              "0eyUQRugSox//c3/4/4vS//r3QjXmEAACsFjNZjxAAAEgkGaIWxDv/6plgDaY+KgAtMK/BqgWxM0\n",
              "VEUeNVbg0w6u4vgg0g9fGmHRPbXNerPiINv7K/NJ4nsb+jAcZcGpoxJuoHUFOg+x/9Mt5H4bXAxo\n",
              "BSiP6CpaNz01jBS/6q+9BBmhxTx2T6b526fndntdiYWalSGIJ/S84P8s73bm3OnyNbrx0Y1M0bXt\n",
              "cpc8pLWy98BKon9/6jXMNJH8m28GyR09iktzQ1Q3EtHUfmA/6jqr1zl3UATy71tobGtsygwifZ0W\n",
              "R2S30hermoPLxPd2nZ5GKDCbGAoWSQQUP0KT3YdWZsQaX0IpmXsF6tEEl5+BJ5FifNUTw1B5txx3\n",
              "8qvltfoaKKfPeeRiChkEPY2SZIz2j5QBYK4rMJ1ibC5C7h35U/Ix4mVeIBr5kvjOMdg3ZCIV/j2O\n",
              "8QWjNxkGIL/EQWv0SdFPHMC4PHlh+wd/XPsevAV5nXSQkyF8xRTLunadm2GCsEZCQ5R3LqaSVr6Z\n",
              "v44+ORkmEx6HeovlxeKs0zEywa0943VtxizDuez++RVtyYF+aQ4hHqho2Z/uokw6FvT+9XaB5Mx/\n",
              "jikk4KVpAE9z8mTArzZ/h/gSnc0K5lWOpBwROUYKcNuanAp0dUWLQrU+FEjWUgWjGxQ3/LDsCldm\n",
              "yvy6ySE6mpjcF4wrB/Ug/1f3Q6p9N3N+lvFf4LWDpTcjs++hAT/Ykx2KspScDoxOAlu9LachGEaj\n",
              "qGnSGSUF5Aa+N15dxG5DjRJiohzIPhljBbX7xT4FEMoXRXzBrS6elB7J9kOJFqUxnqap/g5/03bc\n",
              "h0IaAlkiQGV2sz9203SIk0qs99EfS3obR8qRJMFPLsZ5PtY6fipT8SsvsWLx449g5OGP/UpoHgsT\n",
              "8nyuTQ71l2zaposWDSgtzAT3c6FdnpnXYKsDKwJ99PT2DB7JGVF0Nt4Aiouu+N3QP6eE85ZLL7DE\n",
              "+k5JC2UgyTaWSjV5FztAEkdv87g3Is+FaOlBwOyQR3Cqc6l1zkJDPdAF7z2FzQLI/U0ZAPabt9Hk\n",
              "Msu3DY35lfouUVCnV1lsnfhWD3bBND2vzwv35N6byPrxpo0pL9ogzOvJ4flJb+7R09+MKCZnEA+K\n",
              "i+gajdUoHOZ7ISWTgTnJXBwyN6Q2R6mFRiMIxa066MUPJLNP8JicJoqjx/rhAPwzzLIk05jvDMrv\n",
              "YNhDGRwFGEf6I/e7zk8JVdNrRi4A9VVj6jmI6iTmyBwoGMmJyu6Q1JMF4HMmQ5Ocas6qdZXDTdhA\n",
              "cO2Cbj6Y9DVh5gBwsVx/inQRPVzr0LiEIujMpvdaT1kdMOKOLbFOlUAC2qXLIAFRLsubZSblKj7l\n",
              "Rw9HPnxxMr22UTEE96M7cvc4dHUNOafMze/HHhCs53dgRX+cg0/t8cEDNurmFfC1jxKAJpNNkbm4\n",
              "FHVQELGNBXF0eRMNhxVxEEoQUtmFZnepay55jUvi/n6fQd1ximXtLkgubgDvYpWRk0wOrFespF88\n",
              "i/HRueTtlefa9a+yoYsOcV6nP6ZaT/KzrmThE1EuUbCYfHYxWynRWxHcQ8CLXvEXAAABZEGaRTwh\n",
              "kymEO//+qZYAK78A2brq8u+J1W0DU+yRGaKAAHd9X8iX3ymS7tzoE9/XCyxEWbD0c28hhqSfnFqj\n",
              "IhzR9G134hzlU3iwx2pACaCpM3CmoAAtYV/Tf9lzVxpOLjRE2uwH2Eenj2op5fFy4cMu4JfvlCi3\n",
              "OJBv6hPtEAVy3ZGfIB31Amp3jqVR35EeiwTxonb+7OQaM44xMxwf4wcb2E/JKNFyz/SegJngfcP1\n",
              "sSt+eATQQp96IS5OyLwBK1uCRd06hVFoDYposl/qUOjOb3X0bOFOfcTVv0ATO8Sx+VZOZEDvO4k5\n",
              "wYRjG0YYNz6+ohHUbZcwW8+hod1N55oU5t5/6dSMaKSTrakvFhwW3wNjWtmKjRMeP+1dGmsq6jCY\n",
              "Xot4TC10RzLfbYBTLb4dJRmkNQxD+A7+Cn0Lpu7LWTzu9+gLg1ecwB/SmXPYrt507RCe8vRBnaPc\n",
              "fuTAMq5CPKrBAAAAikGeY2pTwv8CLBq2AD1N4C+lo9tQBIUgAwxyEcX5WGZNPcUgn56H7vciygju\n",
              "mxay9kGRQ371WscwvpwORUoTBnaSz2DeIPxJ9VqUfObiAg6d4W1njSoVKKaBmD2Ss/s3J5AoVI0f\n",
              "WpuGOZbapp4Yy3uYoaNorb1xnKsWhwiUMEcSkd+8xwM4eZVasAAAAKwBnoJ0Qr8C7TCbgVyLNa+B\n",
              "LkdHK4Z704Mv2lZdggOBqBvRoatDYBUOaxlb7ZLmGAKcPmQNIWy6NwfumQmfcdUujktehOO5+ExS\n",
              "Jt4SUkFwcTUia4m5lJDN3JbBawJ1QpcSwxZ43HXx1LfU3N0HhuGNRb+grihAi+ljzkp2kwO8EFqU\n",
              "c7Pv37s8moCtv5uGndMgFSVRQWcWfxQRxswSkVegPLhFQ4R525umxpQNAAAAvwGehGpCvwLuv6t+\n",
              "pVLY/XbDT8Eaflr1EtDNTDkdpZ9jAsNw+dx+4JwORaACdu6oc7ap6JvXlgx24n0DoMsC9+OZ++rU\n",
              "vMF+hiVPhW6idwkRRoeyaIN40genXUDk79yl/Ua3WVP00lYnM9H4s9wV8Q9GRSD3F9GdaCwsfj51\n",
              "7o7ZW3VA20PeAJDpZr2NtVBAaI/i5Q7MA/WWWfkmFvtNrRTbiOhig//8iMDaDV3t4JqRP4glhhFL\n",
              "juZQf1mgCvfdAAAAuEGah0moQWiZTBTw7/6plgAWb4Bs3a2M0LSALOfCNMwXABUmhMfNkfE42cqr\n",
              "Adp94IGiEOa0SPH3ghJsahCmA0PcRHzO8B2vYP6VNTvnyb+egDktKzOiRomZw8qE/wPS59qr/LGr\n",
              "TJAMVgB6S2xdytGfShaRfkYMhcC9JKeiSDUAqvvqrlgpAsD67uxJOIMKPTiR96T9t7+V57oU4UdE\n",
              "aCZKS0YVpuDTL5xzz8PY0lx2g74KYaCYWzkAAACCAZ6makK/Au+TIDrL6aQhJxECpOYAL1Cx8zKc\n",
              "p9k9EZ22HgNZIdrM1p2TaZ+8hRKW3xtjKo6rlvfYceABA6rp7Xtv0iVSG9JgI6sdu8Sw0dQH1S7H\n",
              "R4ywQHQLYnPlXwwU1KYnvao6eEQYNo2LaJQQkr1A0dqpkgCgAA53HcNU/G0lfwAAACVBmqtJ4QpS\n",
              "ZTAh3/6plgAEx+R2sGKWQXa1mhNT/GTfS7A4sG/wAAAAXUGeyUU0TC//AjmLl3Xp18FHzAAD8Def\n",
              "kLDIN0fRJsTARelsI9FFuJT3VriNwPx1vdrhSokc+HqbACR+wsTlzcH3FDMDqt61pfeB2OtBtawY\n",
              "Fymtbd8XnylX1mgf4AAAAJABnuh0Qr8C7TCTib3xorWM9ABysf/4afEV4hg0Zb38iFaJ0FSMVj4n\n",
              "NJHbZBrgNu/idBxxLI5zvo6RUEEqZI61X9Duh12ZjX0RIYCbURmcUs75iunU7HIW24/Hh9aPXd2v\n",
              "w68BuN2+Zef4DRlRuaPt2AM8WMZDTwxwnZAONPfuuISFYHNXmsNxCC8jIQ/kaVsAAABUAZ7qakK/\n",
              "Au6/nvDQX+XPXiU7qPbjeO+dcbgAO0O5KzoqqKyHzbd7sIFu0FbwfBlxg/IZtfdAvSfc7TJzx0G/\n",
              "A9kcpW2qv/07UiDIQ72P6ZOvr1nQAAAASUGa70moQWiZTAh3//6plgALbxllABRcCASj7Tn/54hp\n",
              "yj5niFzS8+Dq+3EuQDO4+bjPltNeVGAFBYUremkAADpjY0zzaVgOIOAAAAAgQZ8NRREsL/8COc5X\n",
              "UtxpIhCgV/lIvu03+e/rBe3VnwkAAAAPAZ8sdEK/Au0wjX7r13qRAAAAPAGfLmpCvwLuv6DMvENX\n",
              "VVoKDN4r8mAzVnkM85dgAXxr5xu44IwarwcbP6Jnvq6k5nm0SK3ep4eEvnufgQAAABtBmzNJqEFs\n",
              "mUwId//+qZYAC3/AGM5zbO/KDBgAAACgQZ9RRRUsL/8COc5Xu7Zc9Yr7eACG+FCWqKjEwBWlDRpQ\n",
              "rDd8Zhf/VlaYlf82YmCmaoBt4M5xn9/lr1Wpi/8anHeAyr///B3dtYL6vV8f+KzRz6dVTVrkOcmu\n",
              "Yq4oQId4+j8u3ZUZduUIBqolGLujjv8hJC62xmz9dmzLmZ2pI9YEmxyAhT83YE6HlqqhglLOre/l\n",
              "9XFz0h92DBLXXYgD8gAAAE4Bn3B0Qr8C7TCaNLWqPwYgctrrOPJYNHVeU8AB1egvWGCuETHdmdth\n",
              "4DWSHazNadk2mfvIUSlsf52aG7YI6znZguJII/zpmmiwashLGLEAAABvAZ9yakK/Au6/qjABduHs\n",
              "Dy4Gxb9ACZPP+yg8bf9sHGW4m190ceq2A4QSzX5Ub1PbUksPcUPNrDTmPbq1TT1NY2vDgS5+tpUD\n",
              "edc1ykxAYHNEezFQxCQJlHSktXV11n8dSYg45lo0xB11tHeFsPmAAAAARkGbd0moQWyZTAh3//6p\n",
              "lgAEx5X+OqSOUADv///g7ojYUmTGxb6fpIR+WI0NgxsBC8VpCqwvVHdl/uwuaUGLvJcfHtxekfAA\n",
              "AADKQZ+VRRUsL/8COc5XxmXCBZbwuc2BWJtd/Axx12h7hvPWjZSarWHChCUcCteGGNIt90UqNbzf\n",
              "l2xSYKdVcioZolXndwa6nexGbWNVlTk6N7VOKv5fyRkXKLFNcTjdkmLmrPv02LRTS4BlpxWzN2Ew\n",
              "NIHG5BxYeWywheP2TI0IT3/uz4QuuC7JqdyEriuFWT9JtsVQ8jFRJ2QXWEpDRJDM6kj+4jlU6tz5\n",
              "9aXnyiF3MJVxYngA4kw0mhA1ccqOjQ/RrKhbODfcrxgfkQAAAGkBn7R0Qr8C7TCT6lk1m7BEALD5\n",
              "0djPajycrGKNnDiZZcXyxO8VgeDr/gbma6QEiU0LshJcROnPmCBRCGHp1MXtRwgm/RdEOb+qf/Tx\n",
              "JvQDndb82kCVpJtA8L8NnN/hwE0zsNeSE/0pVpIAAACIAZ+2akK/Au6/o+f96DTXLOLhjxMr4AL2\n",
              "N//DTUiZQPn7veQOf1PqRKFzgb6SBYhBVYVU8FxRcSyLhZaZhmZx/eSUjOljWsj/6cTMfc8jhbM9\n",
              "S02P+0/w9FdFZMCXu60HA/X4T0a7iLEdRHVYCThfps/Fo0zt9dZi4oMWJx5I0bZRq2y0/C8WFQAA\n",
              "AMxBm7tJqEFsmUwId//+qZYACu8LfI0tfOAAk6+/nDEzoqSZwJfLYTrlqF3zm/Gsp0qfS4yTy1zv\n",
              "4qY7Oohi7CJ7AiaEP8VK4q7O08DvR0xDFwb5MK0+Jl2IIP+mgW/58G+sKHLI5haeEHNp7aoL5fF+\n",
              "pLcjNaJ9xZitrAfn3apzD6ozuagoPCrI2RUwTglxWuOoAzZUYqTlOCGDEfZQ51YDNLtP4+/sY6tK\n",
              "+pPfmsUWkFv+g48wEj5UKrjCQ66GWUAw27VCQekiJ5PLT+EAAADWQZ/ZRRUsL/8COc5Y5N++Cj5g\n",
              "ABsYeT8rEla/avDJMGyBcXhNvCeap9xglXbgrc0K/hMmLWwAOGipAzbmdW2LfSzZpoDNyfQOoAg6\n",
              "O4u/t8zvvbMX1nWX0CiUnuWdibJUn/8h7luJ+5zCcR3LAXini/uxHm90c/pycBwc6X6wbo33FA4f\n",
              "kaiVFAnO913nOt9B/lX1VkQydQbfDsv2Cm7exfh8qC5WNdKEyr55uDw95DGW/SIbXFROAvQ8IBZF\n",
              "0jMFHh3jYWmQ8eSlAL0AJU7+UUBk/SrLqAAAACQBn/h0Qr8C7TCbhHY+dUUtjoi1TRNXcCHbmSi4\n",
              "NoOzHmvjezEAAABnAZ/6akK/Au6/q36C/y5s3/3tKs1wqPKy+CTt2zFL0EzwaDtw8N25vXY/lj5Q\n",
              "AdodyVnRVUVkPm273YQLdoK3g+DLjB+Qza+6Bek+52mWpahYCCSFfEBnM0Z2NyNwrrXB9DTSek9D\n",
              "KAAAAVdBm/9JqEFsmUwId//+qZYAFU/ukWr1OzheuImy9vcbmKUOSHEzPGbbodbzAeX3f22HjcAD\n",
              "zW63D78b4S7lzz07mI2N41QaN9oJ4+lXP+o3W7RErcqnH/mR0xp0KsDvPe5s9eUmJPRhTGjoLcSD\n",
              "vjgh2r650Qso69/4E00reys1uICzxeYG8oT6F0uFE6/nv920M67zTEDuI8L/ahW3yNt42xop6U30\n",
              "32o5O2y2Mutxs9L6WodEP44Oj5b6ClrqlS4cJ/dLGfmyQ9NKcBa10OtaGftJ5qsja46ztZJIEpnL\n",
              "VhqXNs71E+NdTwRGog1g6lpV9LHdyPekLnUAa6B3NHTf0AduaQGitSnmBus4R9sjofqxGDOvq8uR\n",
              "+SVJhIRx5Q3NNRf2Kzsyr8rRh32osrdt658Zlu+EwUQq0oScsokmlbUZuq5PTj/qScbs2L6+4pI0\n",
              "XkY2OiQJAAABAkGeHUUVLC//AjnOV825EAxjEK9C/x+NI79Ar4PGhfACavUoTI2b7JRfhPzW3sqY\n",
              "UFOiFF9mDrxGrQjJWfTOYUSUgu+/BGJHJamZoqGHuY5/WLxTuri1FHFm8qj4n47lpijojgusNO5G\n",
              "CpxbCcCpMWrYOVRu0UoTe+sIwuAZapKq4qjooCLD2/r6tZ/HDSmD6ScSANpRcFDraWh4LqEB9Y4q\n",
              "JubCWztTeFDMix+Malcroz47fSKECucCWi0OZErs0imi3ND8/8RlHfq0R5DVlpYVQUmRNCFvqSHi\n",
              "iDv6CTNaAeXfofLltZRosaFXrcPOQARiU7OhgWAiXt2UdJST3WPYwQAAACkBnjx0Qr8C7TCQn1+D\n",
              "OOQ1QMN2xQZ+k8bSyT8+PcR/vYnV1e8q7kn+7gAAAI4Bnj5qQr8C7r+kFXrZ/bTuJs2ciQAjZdE5\n",
              "l7In8ETocA5YMjLUoVNlwx25PU2BTHjQmf8YvbE1jKRITTvEOnVTpmhLHynn++EVq2UfhULzKEXR\n",
              "+5rUbI0OMDJ3uLkQXM4p5idMWQiga3doLtH9/G4t2aW0O4G/pmHpvPxzgr3zEf2Y1frCNfQW+IXc\n",
              "xTZ6AAAAlEGaIkmoQWyZTAh3//6plgAVT4Bs3a2M0LibDndLXrIkLvIjlxwGH/45hn7xHsAGje/i\n",
              "oKTm1zXf3w+nD5oC3ciGZtD7q+7Yxfx8sFQAShPg1/qRU40yF0LVytsBQLK+JGZyHhoj3qzl38YE\n",
              "4SPGf1LONRJUQylv5drY3ZVgdti2c7uhO7br4KlXEGEMBm2yErjxNLkAAAAkQZ5ARRUsK/8C75Mg\n",
              "Gsvq+s5AHATEifmAajeOPP+nT3DhfF2LAAAAFAGeYWpCvwLuv53tr8FhJc3dd7vBAAAAYEGaZkmo\n",
              "QWyZTAh3//6plgABODXygAdpFvH6Mo2JNAg4bMEtgQCR/7oj0yWU2wxI+skOjAT+vWsj2sxWlrBr\n",
              "GQUk9G+3v+l2WMIofesBoncpI4hmdoXP8ahlrc6G7W+VmAAAAElBnoRFFSwv/wI5zlbjvycsQx+C\n",
              "WX4JxXeAEJd6/S4eNv1IXGW4GZhxvj1Q6ZSApWZZw0pDNvVAcB28ywS1Wsmkb9/+0Q3MTCLhAAAA\n",
              "FwGeo3RCvwLtMI3tV8V86UN00lmZ4LsvAAAAFwGepWpCvwLuv53tr+OWXQ190pH5u2pdAAAAHEGa\n",
              "qkmoQWyZTAh3//6plgABOfkdaC6ILfeMB38AAABUQZ7IRRUsL/8COc5W478nK9ZgIl+DyrGVABEF\n",
              "/T9Lh424NgeTnXFfy/LFEF/hb8noVeovyb73lwzS3w2cMEyaiBIOsveevxVfGpQN9rWP5wkl97K2\n",
              "AAAAYwGe53RCvwLtMI3tV9OEd2K3ADtRgbgnPw2gARB50diZADe21mmWOgf7nHyEWVUJ+GRMCvzI\n",
              "SqqCmiXdrhpe8spyJRp7fAHb8h7J/1zuqf/NZa8YGdzM+LJa5Gox9oW/yjMEFAAAABIBnulqQr8C\n",
              "7r+d7a/RFJptXmEAAAAVQZruSahBbJlMCHf//qmWAAADAPCAAAAAN0GfDEUVLC//AjnOVzsbLnrF\n",
              "fbwAOtj//DkhbzZq9EC8if/Ig4wDl17c8GcXG/ul0zgqzqVzrPAAAAB6AZ8rdEK/Au0wkE0qXRWt\n",
              "dMRnZUACcPP+yg8bf9sC/3CVr7o494OCWuEeyAuVG9T21gWefYOt1Wtt9B7eCNiaMe9Uqa4ntMyc\n",
              "k+xxeyB0es1z2BdsjaXD7s6ieRk+1EHMI++AMRplUwf8uv/zp3bBiiBPhfmbb3OU+bEAAAASAZ8t\n",
              "akK/Au6/oEv5LUdoMBbRAAAAYkGbMkmoQWyZTAhv//6nhAACf7++E5PL4ALVIRnKDYtHmA5s/Rjf\n",
              "Ly2UEAsKkMUNKeggpPxF6rylG60TvnMoeVXyx4BvJwQd/RQFvwUa421R//KKrvqw2TmhYC2tBfKN\n",
              "KMvJAAAAHUGfUEUVLC//AjnOVuO/JzdDHtqDvklrqKk1Sz3sAAAAWwGfb3RCvwLtMI3tV9Q3G4Jz\n",
              "1HoAW8dyVoz3YDVGkYSXNi9wfvPsDZbcGHrP0JZ+h8nzExrRJYVatMUC8UEUOIW2DJEGJg/7+Ihq\n",
              "uoesnt7d/yo8C6wtvxtGWoAAAAC1AZ9xakK/Au6/ne2v5JZdCK0FACMjSoeux8qKkQyQteB0lnQH\n",
              "LE77fdVfwFIJ0BN5bDs7zAu2IovP6XqNUs2+7gUqOv+Z8KAbkrADK/ZCejInN8Uh2V4tIvbzudmr\n",
              "QOcZLAJYWxIkTpoCPM4yeTudn+GHrY2EEUv8ml7jOwQHUDymgQKV0jFByHntg6sSzL+lRcVL5lnb\n",
              "0kwLM4b1ChikTWEl13+03Nmlp8YpZpVRlQkG43bWgQAAAHtBm3NJqEFsmUwId//+qZYAAUL3lwCe\n",
              "ogiiwAZd6cZDG6Fo4IE4LOcPBdYhroqCMjf3fG+BS1AA9kG6y795mYeP5lFVwX0Jdp/7ZBFA5O0w\n",
              "QQTEiz/F7WgigZkS8nHA+ae4Z60gME+TRGGa1O0kzjeCwQFYJswEKF6FVVgAAAAbQZuXSeEKUmUw\n",
              "Id/+qZYAAKX8A2btbGaFozHEAAAAiUGftUU0TC//AjmLl1n3LzQMWESclOHqv4PGiEABzh5EVatS\n",
              "xyj5/HFXEwwitV3T31OnILV97Sv5B5y75bHvH6RPOZHiQrFvC73vOUsEstiP2tQcO9e8KnYQ4448\n",
              "9Eda5sWCChkqGVxYGTcFbaFZJgaDUWdNfl85LqO7VzLdciy3oj24YrlD76WDAAAAGAGf1HRCvwLt\n",
              "MI3tV8ISO62IRAkE+WASgAAAAOABn9ZqQr8C7r+qYXXGjZVFYAL11R2GlPsLj+X910e6gejM+eai\n",
              "f90YcgRb3Gdj9BICQuWHx6GNUim9X5RhDnvD5OcujVx0RgDYsW7NSdS00Bj6J+Ixxr9iXcX+xAQo\n",
              "QZqVS2FlX/rtQJpDFD/g8clQ0UXCKMCmqxm5tLOspicTdP4YKt0ERtzYKgoqmC3LjsTu7R673edr\n",
              "0xkHV2ZQ+g4NLum63HIb1+vZU9LsdKqXV9egmNj252p26W+69Et3imK2flty+DuiQwO89J2UOSOF\n",
              "8xCUk/8X+Pxqqgy8cAfD2wAAALBBm9tJqEFomUwId//+qZYAChcLfBBr7gAosz73uRabKhvicFdO\n",
              "xXP7oUetFLl64jzp2MS/RPbM0Te+0W3xKBt+Ox00+zIht/3CLITJPT+q88GQdCM00QiTiq0w64ek\n",
              "mlkXndD/TV548/6+WBaewMu+A+5LIMP2ubR+qRESMIMj2+HsbokDGLd151cJyRaYf3oSa/xdiOyj\n",
              "Hgcivj8PWjnhHgPZslU8VesSmEHunw1PIQAAACNBn/lFESwv/wI5zlc/ErIRCEKyLfX0XYOkxIK+\n",
              "P+JHc3udWwAAAGIBnhh0Qr8C7TCN7Vez0DtwgAWtoATV5ipzDSaskMxEzBt3EdQVseSyCF4JOWTC\n",
              "EkRRL1gXZYZXvXQSm/lcP5An3WbRlhgJuW8vVghCNEcfEHTiJM1s+HGXwdu1uyoY1jgSywAAAMsB\n",
              "nhpqQr8C7r+gYNuNms8f7Lp8Uh+5VeCZ2zRwAt3yU+0I+HuChBgnsrqQPQKH+PIzoLgMOFBhwig4\n",
              "pgTZvw+Ew4uEumShsILgAAR9iBfvkydrjvp/ZqYemC7Bhw1jLIclueB3futxHAu77pqkbdcPoFnl\n",
              "gQ+D8dv1PmxEE09v4miLY6UsQX09XYEZgg9WaaIAH5nTPs+77bD6VItmVfM0rBm7E4/GkS1Et11e\n",
              "xjZgFatx3CR+2e3NFgBzw/XaB/4qCMquVBvQcfiBgAAAACZBmh9JqEFsmUwId//+qZYAChfANm7W\n",
              "xmhaN0RD3hnkp7STdFYOmQAAACNBnj1FFSwv/wI5zlc+9u9QVSsi319z+jpTd/2gzKNL1qb5iwAA\n",
              "AB0Bnlx0Qr8C7TCQX5wn/Qiv2XT4pIc542FtIB6aOAAAAA8Bnl5qQr8C7r+d7a++3HAAAACQQZpD\n",
              "SahBbJlMCHf//qmWACyb6FDr8CtETDBRNvi8rahzJm7dDVOYDzQWcxSc2ua9WfQea3XOBp+jTTxg\n",
              "xgaGhbd+l7K1vTgV78mp44Ki+cKvH4w5PwU1bJsYCLf13i5v/8O3lRmH/kcnM3/003Oe2b3/EZY4\n",
              "x51schi8cn1BdAX66kK1oV3+O2Po57Ko48K/AAAAJkGeYUUVLC//AjnOWPtxpIgfom4RBdD/ZdKH\n",
              "7mFvixqhs8IFkFq2AAAADwGegHRCvwLtMI3tV67ccQAAAJUBnoJqQr8C7r+r2vEKJVxozBXmADk9\n",
              "BesMFcImO7M7bD0FvkOyiOmdk2mfvIT9nwCP/oKv3DSvbknT2t6tfQTmUkGs0Idb3a70/6o88A5B\n",
              "yoUtK+NmsJEraO9dAmhjA6hSlduunca99r/SzsqynE3SL8umU5P+Ps1CY3V4caOqjh/Pgk91/goo\n",
              "174t98f8cpjdxzLmgAAAACVBmodJqEFsmUwId//+qZYALN8A2btbGaE7XJodqATYyEzXrG9BAAAA\n",
              "JkGepUUVLC//AjnOWPrHnAAVZxL1Bj9l0ofuYW+LGr+rVTImaLaBAAAAcQGexHRCvwLtMJvVains\n",
              "2S7QNxiXxozDDuAA6vQXrDBXET3zhd5a2H8EWR3ZRHTOybTMUfFiFC6jT6G/PljvzKdm1SI+ARJO\n",
              "FIIPUedgvFgr440LLkMsKBWZMG4SSIT6x29Qe9FmKL/Zm3UOiz2IVgPnAAAAWwGexmpCvwLuv53t\n",
              "r8QWIvB++kBAC3juStHH0PXTXchBg9SFpjB4UT8Pycm2wqyrEmHHwEPtwjyJu0pAFQFzLIaqvXiM\n",
              "dQ2qTieLI2C4NR6MBxh3aT5rimjgStUAAADnQZrLSahBbJlMCHf//qmWAALfwt8Jqz8AFSfDSuLU\n",
              "KqTNnNrvbYg2WmuITsZJhdk/txX+0fInronhZISrrdn8Sp9P1z+T+dVqxOBNaHLcZQfjzJihxR9k\n",
              "YxDb0e90+fXjrpZlVfRP5CDXbg+pCusDVufs5sp6a7CSG/1ekZnSThR7FHG3Fhq8jIOlA3JUHpJr\n",
              "R9lhcDSn6ti/WaHIKP6ROzkfm7HPYlofAhVGgJtlYF5EIV/ida/m9fjDncUC+I7rCMjd2H/fLWNa\n",
              "jfF5dTIpJWfMUACFL8KA6uuksKjlybkHza0qROcwAAAAKkGe6UUVLC//AjnOVuS3HyuY20j+2qm0\n",
              "SIG3Qc5OtQApHb32aZy63t7CGwAAAGMBnwh0Qr8C7TCbXIXGjMAEgAOs+X2xcwO/CpiFTiiFtkpB\n",
              "4tuE6NNRopXgw8WXAkYTr2v3vJGxrxV8o4kC8ynO7gKAJbKPk0ix4JAtHEilqLZ20fHXCBGCgqYt\n",
              "n/4rU+zhy8EAAAAgAZ8KakK/Au6/nfMvEcMdhjGz1zb8SqQXlUnYR+UA2WAAAADcQZsPSahBbJlM\n",
              "CHf//qmWAALxwoYgbZ/5gwAR9PEPHSnXQNDrhqXXiI9lqk0s374sqk0aofaCN/wVVkJUgFnjC6+D\n",
              "KAO87mstSlW7zQQtHeUgJUxY2QyuvBVm8jVoLI8BzSdkGNOaGFVwPwxi5aTy+MIC7oqJle4pNQwq\n",
              "PR/+RQPmvQAaodo7nzuGuDkyAf0XfH+YWx/BK5XqScnddYvIh0xompUfWcFRPbj8jygkJZ1x+fA5\n",
              "Nzmxu+3UsGi6TewgclZLo3iGhI6cXppOTJDEp8tsi6K/FQzQWU26WAAAAHRBny1FFSwv/wI5zlbm\n",
              "txox8fk/P60Eh0UYjiMAphj3ltVAkMo2iyGRTIVnR/Q8Ln1bYeBByRT33j3zxODf+/CACox0Xuz/\n",
              "ZIX5Sp2PyBHPhFuZTWZ2pPx4k0Y+ss5CYLEdCObVL7MMSmFms9/BbOXqHQbOXwAAAJABn0x0Qr8C\n",
              "7TCOrG98DilzAB+BYvyIbiO2zink2j0MTD2YABiOrolqPcY6bBBthwC3s0sCxh5zdRmBj1YuGVqt\n",
              "02YMi0LRYTgVNe7wsatTH5u84xSmDsRK88TjL4DC3JImeW5nxfgHfSU+QSRgRdNeGcr+8msreziK\n",
              "6bl/lVFeAFAap/XAT39MKCYWFaictiEAAAEwAZ9OakK/Au6/oIC9caMwASAA6uu+icTOftneDmgD\n",
              "CVcLkaI6b7nyZb+9rI6wuqP4F1M4aRW86Cr8uSKmnWDiD6DceOsu4QJKiIJwADz+ZN8/bF886aCY\n",
              "LmKmMloPdMP4oD90sAbsXfyGKRraIoPZk0IFYuWljmlmy2v35xF4/81V/mIuigx1XtiakUwSWc5M\n",
              "2EM5LswRWYz0y9dgdrSIXQtgjrkLlHLkLN8Ci49iRIwB6LLr09Wl4l4MHvS2nSqX/oJvuBhMOPj9\n",
              "CheUhH7s78HixD/TJl9OzbLbooZWgGl1oODz1I8pTZ2669Au6AOkr28USQc0pgvZf2S3uccccyPT\n",
              "67OR1XGOETqd2zaIBPHjjoJPOrQ6OyWA1pZTU7SyBlps95uheg/XDFmzKSZ9IQAAAHJBm1NJqEFs\n",
              "mUwIb//+p4QABdfeFCELefL52m6uLtzgzVnpBOlCKQAROuS7p6d2N9S3Ll60swi8fT15Tqjz514Y\n",
              "7f90KSBhkgQtAZnG2L0/Chns1zoG5Vjg1YMVZjimLiA+dc1h1NwTepKvdYA41TlOhKAAAAAtQZ9x\n",
              "RRUsL/8COc5W5r8YyzxhIdFGGTmvKhiFAcrLa0lytnYdhpzvf0VT7QvgAAAAHwGfkHRCvwLtMI39\n",
              "1qLeUZCoifCsu9PcEY0xNIZu4aEAAABJAZ+SakK/Au6/nuxeuNGdUuCwAXqFj5mU5ERNU6Ts05O8\n",
              "vL/tQxacj9wcsvIytNvoLb3MJEdM2baLKuQehanCVU56IlM/hu4+0AAAAHNBm5RJqEFsmUwId//+\n",
              "qZYAACzfANm2kc96y2YARB8NK4svBs2jcyVyLgQcELZRTROQB92xmgR71QfAu/dxCZbDzgn8lXL9\n",
              "WWKS2HOrILLj/Qj4sabtUfKgkWyYdKTla1rwDYm0DDY/TUSbN0JrNLZqOZ9zAAAAZkGbuEnhClJl\n",
              "MCG//qeEAAse/vhHYvHABzlyXdVtBqjTrl31/eVJhwKEo2V2onwW3jpEzoiDAV45/l771MD+rWQa\n",
              "ppRihEQWlaavwyITxGBztj0KbOSp0kg7JvfhphWenqT7TfAJnQAAAGtBn9ZFNEwv/wI5i5ivUcI+\n",
              "f3B2Z1nY53aXr9JUMKu8P7s/2TOcetsZsbaOdRWTvvhfl4nP+7ONYjYsx1jpTnA/o3GV1sJYif/g\n",
              "7ptFhPpOJ/rwmLe3VTD0xtcbk53Rhhgy1aScGneU8ZAaEAAAACgBn/V0Qr8C7TCaOdCSp18L1E2B\n",
              "G2Rp/2lMMbwZLfWwYykVoaw3QFJBAAAAdgGf92pCvwLuv6o50JKnXwvUTSYs683/aUwxvBjwzXuv\n",
              "GusioQ1Y1m14JwOJUAD+d1Q521T0TevLBjtxPoHQZdonuX84zpaPxGPhAK5mAnEups3YJb7oDUTc\n",
              "1+XX6CY7yaQfL/HTKrD5alQR8RSorCAlXWs8Sk8AAABqQZv8SahBaJlMCG///qeEAAsfvCW8EC8P\n",
              "3deCwfhOTzSADtLTfdnxHWTgBQciAfeuJ7AyHrCYPPyueCBbqR/o68tCqbaIZ5GHBf1VHCmnkqgr\n",
              "y8whYtXXwsDXyo/cON94FaZB2MSWklI9wAAAAGpBnhpFESwv/wI5zlfmZcEDTYRfq8KRfgpAmlKM\n",
              "A/t6jYkQSIPcYBphQEczDrgYM5PFOmUyHa1LzbTr6W0lm8HwAGzviHerDz0wfy7Kl1pc+qXhn4Hv\n",
              "SvidxOetHT5m2vA93lKoNvJ+QBEvAAAAgAGeOXRCvwLtMJSaQq/J5iyuNGYOdwAXqFj5qUtF1VBT\n",
              "b+Cw/7m64c5n/x1THrd4NWDkbtCFycjbsJyOpfdSwa8rMzNxQcYyfabsaXjgfpizYJ0EOzqbZz00\n",
              "mVHpbAB5//KqC8i64abb21xYlRMMSzb9NB4hhyXsAkGE5Q4UAMqAAAAAQgGeO2pCvwLuv6SXUqbf\n",
              "Qvq40Zg53ABeoWPmpS0XVUFNv4LD/ubrhzmf/HVMet3g1WKjVIE2ZiXbUiM9T2tWu7ehxwAAALtB\n",
              "mj1JqEFsmUwIb//+p4QAATUuqIAHaPJDw42ipGFI9EfNaiDsu9ynqxL+6I9KBdXI847ErANWvYtw\n",
              "Th/FzM+dgYewJwr85mOCBSVmvNmZE2ijBKicvQg+5buo+RbBEn77EFyKX4Kc2vJ0a9sRa5n4eort\n",
              "6nRbWno+VmoOK1nW2zDTrlMlTNAmIh+85OYeFPfg3DKejc1WPGgthh6nyHTQ6SnyQkhQhKzfcDY+\n",
              "XBQAIGSW2TLSOrty7mqBAAAAm0GaQUnhClJlMCGf/p4QACbcefA0jQAJ29eC0YBeQapKQwOQeD05\n",
              "dzE/c7oKClvU6qWcvLZn2DZTMwQ/i2cQzgmeGAZlnUIoUUFto3e0VlSLEyzXWM5odone4I7/E+EO\n",
              "O0v72ocyp/R930MhFdFcq0OKDU1TalY7MwNa/WFJHvoZYn/R25C0PSRRG6SVNu/F+21rCShPcDe4\n",
              "2kKwAAAAJkGef0U0TC//AjmLl3SeOdeUz6sKeBnpynAcwa38GWvQ9Ed2TetgAAAAoAGennRCvwLt\n",
              "MI7skLi3uRhwAlhzp9oR8PmOO4XUKUUUKvAQ0CWeEJLXafAZviQE1ZjQZm9rFpUDQOKEz/JWi+2k\n",
              "lRGa7ChA0GP+OiUWN4R8MRQGY7nxqGHDovI2Kpkb0+DBy3coeTSQk+/vnVz2B+/0Lx6Rva5x8+ik\n",
              "Fjylvm3rFWAfmsqSADs+tr8yxEMR3mRHEC5g1lMcDo88kimkCLEAAAAZAZ6AakK/Au6/nuZhJzrf\n",
              "x9r89bEXvkoccAAAAB9BmoNJqEFomUwU8K/+OEAAl3wQ4nZDxGCL2HMWDWf9AAAAhwGeompCvwLv\n",
              "kx8KkdwNgnMAH4BX8mQQt/2wcZbibX3Rx6rYMGd+q9JO0LRv61+QO5+g/S2+PeZwQrK01Er44jvM\n",
              "jNSciXwArPLLkdft9C+M+GctNEi7tVRe3w9FfnYNmw8t5beMQ/Y103Timc3/BFhNetrGt7wbHOsA\n",
              "yhNL+Y5l9+iiGuwP8AAAB85tb292AAAAbG12aGQAAAAAAAAAAAAAAAAAAAPoAAAPoAABAAABAAAA\n",
              "AAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAA\n",
              "AAAAAAAAAAAAAAAAAAACAAAG+HRyYWsAAABcdGtoZAAAAAMAAAAAAAAAAAAAAAEAAAAAAAAPoAAA\n",
              "AAAAAAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAEAAAAABsAAAASAA\n",
              "AAAAACRlZHRzAAAAHGVsc3QAAAAAAAAAAQAAD6AAAAQAAAEAAAAABnBtZGlhAAAAIG1kaGQAAAAA\n",
              "AAAAAAAAAAAAADIAAADIAFXEAAAAAAAtaGRscgAAAAAAAAAAdmlkZQAAAAAAAAAAAAAAAFZpZGVv\n",
              "SGFuZGxlcgAAAAYbbWluZgAAABR2bWhkAAAAAQAAAAAAAAAAAAAAJGRpbmYAAAAcZHJlZgAAAAAA\n",
              "AAABAAAADHVybCAAAAABAAAF23N0YmwAAACzc3RzZAAAAAAAAAABAAAAo2F2YzEAAAAAAAAAAQAA\n",
              "AAAAAAAAAAAAAAAAAAABsAEgAEgAAABIAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
              "AAAAAAAAAAAY//8AAAAxYXZjQwFkABX/4QAYZ2QAFazZQbCWhAAAAwAEAAADAMg8WLZYAQAGaOvj\n",
              "yyLAAAAAHHV1aWRraEDyXyRPxbo5pRvPAyPzAAAAAAAAABhzdHRzAAAAAAAAAAEAAABkAAACAAAA\n",
              "ABRzdHNzAAAAAAAAAAEAAAABAAADIGN0dHMAAAAAAAAAYgAAAAIAAAQAAAAAAQAACgAAAAABAAAE\n",
              "AAAAAAEAAAAAAAAAAQAAAgAAAAABAAAGAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAA\n",
              "AAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAA\n",
              "AAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAA\n",
              "AAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAA\n",
              "AQAACAAAAAACAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAAB\n",
              "AAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEA\n",
              "AAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAABAAAAAABAAAKAAAAAAEAAAQAAAAAAQAA\n",
              "AAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAE\n",
              "AAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoA\n",
              "AAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAA\n",
              "AAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAA\n",
              "AAEAAAIAAAAAAQAABAAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAA\n",
              "AQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAABAAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAAB\n",
              "AAACAAAAAAEAAAYAAAAAAQAAAgAAAAAcc3RzYwAAAAAAAAABAAAAAQAAAGQAAAABAAABpHN0c3oA\n",
              "AAAAAAAAAAAAAGQAAAP0AAAEhgAAAWgAAACOAAAAsAAAAMMAAAC8AAAAhgAAACkAAABhAAAAlAAA\n",
              "AFgAAABNAAAAJAAAABMAAABAAAAAHwAAAKQAAABSAAAAcwAAAEoAAADOAAAAbQAAAIwAAADQAAAA\n",
              "2gAAACgAAABrAAABWwAAAQYAAAAtAAAAkgAAAJgAAAAoAAAAGAAAAGQAAABNAAAAGwAAABsAAAAg\n",
              "AAAAWAAAAGcAAAAWAAAAGQAAADsAAAB+AAAAFgAAAGYAAAAhAAAAXwAAALkAAAB/AAAAHwAAAI0A\n",
              "AAAcAAAA5AAAALQAAAAnAAAAZgAAAM8AAAAqAAAAJwAAACEAAAATAAAAlAAAACoAAAATAAAAmQAA\n",
              "ACkAAAAqAAAAdQAAAF8AAADrAAAALgAAAGcAAAAkAAAA4AAAAHgAAACUAAABNAAAAHYAAAAxAAAA\n",
              "IwAAAE0AAAB3AAAAagAAAG8AAAAsAAAAegAAAG4AAABuAAAAhAAAAEYAAAC/AAAAnwAAACoAAACk\n",
              "AAAAHQAAACMAAACLAAAAFHN0Y28AAAAAAAAAAQAAACwAAABidWR0YQAAAFptZXRhAAAAAAAAACFo\n",
              "ZGxyAAAAAAAAAABtZGlyYXBwbAAAAAAAAAAAAAAAAC1pbHN0AAAAJal0b28AAAAdZGF0YQAAAAEA\n",
              "AAAATGF2ZjU3LjgzLjEwMA==\n",
              "\">\n",
              "  Your browser does not support the video tag.\n",
              "</video>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Delta modulation\n",
        "Delta modulation is based on event-driven spiking. The `snntorch.delta` function accepts a time-series tensor as input. It takes the difference between each subsequent feature across all time steps. By default, if the difference is both positive and greater than the threshold, a spike is generated:\n",
        "\n"
      ],
      "metadata": {
        "id": "Vb6F9kjBqqSF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a tensor with some fake time-series data\n",
        "data = torch.Tensor([0, 1, 0, 2, 8, -20, 20, -5, 0, 1, 0])\n",
        "\n",
        "# Plot the tensor\n",
        "plt.plot(data)\n",
        "\n",
        "plt.title(\"Some fake time-series data\")\n",
        "plt.xlabel(\"Time step\")\n",
        "plt.ylabel(\"Voltage (mV)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "qY6rsY7HqyeV",
        "outputId": "cba17eb2-273f-45b6-9228-e7263ce7434a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert data\n",
        "spike_data = spikegen.delta(data, threshold=4)\n",
        "\n",
        "# Create fig, ax\n",
        "fig = plt.figure(facecolor=\"w\", figsize=(8, 1))\n",
        "ax = fig.add_subplot(111)\n",
        "\n",
        "# Raster plot of delta converted data\n",
        "splt.raster(spike_data, ax, c=\"black\")\n",
        "\n",
        "plt.title(\"Input Neuron\")\n",
        "plt.xlabel(\"Time step\")\n",
        "plt.yticks([])\n",
        "plt.xlim(0, len(data))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "adVfuWKwAHV2",
        "outputId": "949c59e6-f885-41cc-f190-7d8ebc54e3b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are three time steps where the difference between $data[T]$ and $data[T+1]$ is greater than or equal to $V_{thr}=4$. This means there are three *on-spikes*. \n",
        "\n",
        "The large dip to $-20$ has not been captured above. If negative swings have importance in your data, you can enable the optional argument `off_spike=True`."
      ],
      "metadata": {
        "id": "WDoTNPLPBPY_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert data\n",
        "spike_data = spikegen.delta(data, threshold=4, off_spike=True)\n",
        "\n",
        "# Create fig, ax\n",
        "fig = plt.figure(facecolor=\"w\", figsize=(8, 1))\n",
        "ax = fig.add_subplot(111)\n",
        "\n",
        "# Raster plot of delta converted data\n",
        "splt.raster(spike_data, ax, c=\"black\")\n",
        "\n",
        "plt.title(\"Input Neuron\")\n",
        "plt.xlabel(\"Time step\")\n",
        "plt.yticks([])\n",
        "plt.xlim(0, len(data))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "r4FdNjvzCnwp",
        "outputId": "5ff57aaf-c765-4df6-ca21-57097eb44a89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's convert an image using delta modulation. Here is what documention says:\n",
        "* `data` (torch.Tensor) – Data tensor for a single batch of shape `[num_steps x batch x input_size]`\n",
        "\n",
        "* `threshold` – Input features with a change greater than the thresold across one timestep will generate a spike, defaults to 0.1"
      ],
      "metadata": {
        "id": "Uq1Smy4vpE5F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download test set without transform applied\n",
        "mnist_test = datasets.MNIST('/data/mnist', train=False, download=True)\n",
        "\n",
        "print(len(mnist_test)) # test set length\n",
        "print(len(mnist_test[0])) # One image with a label - a tuple of the length 2\n",
        "print(mnist_test[0][1]) # Label of the first image\n",
        "mnist_test[0][0] # First image itself"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "fSUxnJT6EkJC",
        "outputId": "b4e3f381-6774-41e4-ebc6-8c366da6b10b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10000\n",
            "2\n",
            "7\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=28x28 at 0x7F99B81BAF50>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAxUlEQVR4nGNgGDaAEUKFpD77sfTFHeyS9xQYGBg+X4UKPuk6w8DAwMDAAuGm6l/TMnSweCzLwPDntSTDozPIOhkYGBgYBA3PmDIw/Lh1XShnGi5nBP+9KIRLTuzl/2AokwlDMlv0/U1cGq1//rPDJcfQ+m83Ky45zrM/rHBqrPu3Daec9+8PlrjkhO/+W4ZLjvn0v9vKuCTV/v3zxSUn/+BfMSMuydZ//0xwydl+QpdEClsbHoa7X1AkWZA5F53f4TIWEwAAaRE8kJuHrgAAAAAASUVORK5CYII=\n"
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "\n",
        "rotated_image = []\n",
        "image_tensors = []\n",
        "reshaped = []\n",
        "for i in range(5):\n",
        "    image = mnist_test[0][0].rotate(10*i)\n",
        "    rotated_image.append(image)\n",
        "    image_tensor = transform(image)\n",
        "    image_tensors.append(image_tensor)\n",
        "    image_reshaped = image_tensor.reshape(-1)\n",
        "    reshaped.append(image_reshaped)\n",
        "\n",
        "print(image_tensors[4].shape)\n",
        "print(reshaped[4].shape)\n",
        "rotated_image[4]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "CmqirLQmIDep",
        "outputId": "a03750fb-6f29-4650-8ae8-c66f60997ae0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 28, 28])\n",
            "torch.Size([784])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=28x28 at 0x7F99B81A1210>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAzUlEQVR4nGNgGORgVQ9uuWP//mXhkjr29N+/i0I45H5Zi/4NDoYLMCHJMVh4Mti9WXMTLsICpVXuHJOWdWU4KsbwH8PY6TYf//3jY2BgYGj8Z8eKKjf/37/Fj50YGBgYGKz/7UaV2/nvXxGM3frvB6rkPYQcA8PZbdtQZZ2R2HUfLNFdhAC/fy/DLen977YyLjlh4X+3ces8/e8fI05JtX/FJri1PvhnApVlwpCTX8pw6gtOnZ/+/cMepQwMDAy2/86fxylZWfkPt4OIBACwMUZZQ6RCRQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the tensor\n",
        "plt.plot(reshaped[0])\n",
        "\n",
        "plt.title(\"Image data\")\n",
        "plt.xlabel(\"Neurons\")\n",
        "plt.ylabel(\"Pixel values\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "yHbk9M51VHBG",
        "outputId": "f2e5eb7e-2687-4eac-eea1-f0bd78405566"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO2dfbxcV1X3f2vm3rzQpI1tY2mbtikQkYK21FjeFHkEpEWhKigt+lAV7IOKigo+RQShyqOCvIlVBHkXCuVNQi2UAgWhQNuk0Je0FNI3mkJJWtqkSZObe2fW88c5e+bMmXPOXmtn7zsnmfX9fO7nzszZZ+91Zs7Za6+XvTcxMwzDMIzppTNpAQzDMIzJYorAMAxjyjFFYBiGMeWYIjAMw5hyTBEYhmFMOaYIDMMwphxTBIYxQYiIiegRk5bDmG5MERgHBUR0OxE9bdJypIKI1uZKY2bSshgHH6YIDMMwphxTBMZBBxH9DhFdQURvJqL7iehWInpi/vmdRLSNiM4plP9lIvomEe3Mj7+mVN8LiOgOIrqXiF5VtD6IqENE5xHRLfnxi4jo8AbZXk5EPyCi7xPR75WONcnxP/n/+4loFxE9gYgeTkRfzNu9h4g+SESr9vf7M6YPUwTGwcrjAFwH4AgAHwLwYQA/C+ARAH4bwL8Q0Yq87G4ALwCwCsAvA/gDIvpVACCikwD8K4DfAnA0gMMAHFto548B/CqAXwBwDID7AFxQJRARnQ7gZQCeDmAdgLIrq1YOAE/O/69i5hXM/HUABODv83YfBeA4AK+RfDmGUcQUgXGwchszv4eZewA+gqyTPJ+Z55j5cwD2IVMKYOYvMfP1zNxn5usAXIisYweA5wL4NDN/lZn3AXg1gOICXS8G8Epm3srMc8g64ufW+PJ/E8B7mPkGZt6NUqftkWMMZt7CzJfl17QdwJuayhtGHRZ4Mg5Wflh4vQcAmLn82QoAIKLHAfgHAI8BsATAUgAfzcsdA+BOdxIzP0hE9xbqOQHAJ4moX/isB+AoAHeVZDoGwKbC+zuKBz1yjEFERwF4K4CfB7AS2cDuvrryhlGHWQSGkbmONgA4jpkPA/B2ZG4XAPgBgDWuIBEtR+ZuctwJ4AxmXlX4W8bMZSXg6jqu8P54hRxVywT/v/zzn2LmQ5G5vKiinGE0YorAMLLR9I+YeS8RnQbg+YVjHwPwrDzYvASZO6fY2b4dwOuI6AQAIKLVRHRmTTsXAfgdIjqJiB4C4G8UcmwH0AfwsFL5XQB2ENGxAF4uv2TDGGKKwDCAPwRwPhE9gCwGcJE7wMybkQWEP4xsRL8LwDYAc3mRtyIbxX8uP/8byALVYzDzZwC8BcAXAWzJ/0vleBDA6wBckWdCPR7AawGcCmAHgP8G8InA6zemHLKNaQxDTp5pdD+Adcx826TlMYwYmEVgGB6I6FlE9BAiOgTAPwG4HsDtk5XKMOJhisAw/JwJ4Pv53zoAZ7GZ0sZBhLmGDMMwphyzCAzDMKacA25C2ZFHHslr166dtBiGYRgHFJs2bbqHmVdXHTvgFMHatWuxcePGSYthGIZxQEFEd9QdM9eQYRjGlGOKwDAMY8oxRWAYhjHlmCIwDMOYckwRGIZhTDnJFAERvTvfEvCGmuNERP9MRFuI6DoiOjWVLIZhGEY9KS2C9wI4veH4Gcim668DcC6Af0soi2EYhlFDMkXAzP8D4EcNRc4E8H7O+AaAVUR0dCp5DEPCPbvm8Nkb7p60GIaxqEwyRnAsClsAAtiK0U3BBxDRuUS0kYg2bt++fVGEM8K45Pof4Lf/40r8zacqPYKVfODrt2P9312GF71vI1743qvxwStr572MnffFb//QW07DC951FV78n5uwe27BW/Ytn/8Orvmef2fIXp/x2k9vxq3bd3nLfu2We/DvX75FJKuUPft6eMUnrsP9D+6LWu9/fuMOXHaj//vf9sBe/NUnr8e+hb637Dv/51a84hPXQbIG2tu+8F1susP//d9w1w686H1X4+a7H/CW/cp3t+P3378Rd+/Y21iu32e87KPX4j1X+Fci3zufff8/2h33+4/JAREsZuZ3MPN6Zl6/enXlDGmjJXz62u/jq1vuwfu+fgfme/4HHwBe9anNuGfXPnz+ph/iC9/ehld+UqZEXvWpzfi99/pnmX/9lnux9rz/xj275rxlb70n66xJsOHjWz7/Xfz6v37NW+7arffjPVfcjvM+fr237PPfeSX+/jPf9jeu4KOb7sSFV92JN1/2naj1/vV/3YDff7//+3/thhvxoSu/hy/c5Fcar7vkJlx41Z3Yva/nLfvGy76D5/yb//u/+Lof4PM3bcMXBIOGt37+u7jsxh/i2q33N5b70YP78LFNW/HaT9/orfNT37oLF151J17/2bi/a0wmqQjuwuj+rWswvtm3cYCx0B+O5PbM+x/mxeBdX70VAHCNYPS40Iu/Gu+2ndno8tDls9HrluAG133BpX3v3gexZZvfclG1n2+3LGl/cE7EVZFdXSTYzvmBvQv5Ob469XL0NF/AIjNJRbABwAvy7KHHA9jBzD+YoDxGBIo3+96WKIJOPryXPIcLCR7WbQ9klsiPH7o0et2xefIbLsfT3vTlqHW6Driv6D19JTWKwpWUWHkPzvtdgtr2SXH/TYpki84R0YUAngLgSCLaimyj7lkAYOa3A7gEwDOR7d36IIDfTSWLsXgUO9K9+2SuodQ4RaDqPCI+tNt25opg5WQUgesA2du9phLAtR+PkNG1QA9gz+Ceba5f07prd2Lfv4BkioCZz/YcZwB/lKp9YzL0+sPOvy2uoU5u96pcExHbd5bRQ5Z0I9YK/MrbvoLv378X17zq6Y3lJB1gSgYdYURFnMJyA4a/VUzXUIcSaMLIHHDLUBvtpuhjT6kIQkzznqojavFTm3PDXTtV5Sd1SR2JT6ZMREWg+S2l96xmdD8ciLT3njogsoaMA4den9HtZA/+HkHmRyj7hBlJANANcA2lQBKsTNNwfv2TaX3omlJZZM2FewFBfYk+ci4nf4xC0S7aHyMwRWBEZaHPWLE0MzRTBoslOemOXC9NLFg5rHMyPcHQNSM/R/P9Sol5/fO5CzLE2JDgdQ0p6joAPEOmCIy49AqKQGJmh47SdYogH5Ep+rYU6YOTYthZKlwkEa25FN+/G7nPdPyaIMVvpblvh1lr7b1pTBEYUSlaBJLOJDS3WuMaCokR+PrMEKkn5RoKaXf3Plkapaz9DM135ivrYgRB8QdR+56soYBg8aRdk02YIjCi0uv3sTzPjpF01lVBP8EgL8g1lGKSkuqcCTsHNCI/GFERIMH372IEXYlFEFB/zD7b6SqNRbTYmCIworLQZyyZyW4ryWi/ahmKma7/tpwLcA3FDFa2d2w3Tkiw1peV01dYcs4iUVkEHmFdjKCrsAhidu46iyA/p8V3jSkCIyq9PmNprggkPtGqJR0kfl+VRRAyjyBBjEDjook5eg6Z0OT10avSdwcCyNv3HHeDjG5X8Z3Km/cnCyhqOxBmFpsiMKKy0GMs6cotgqqRp8TcdxaBZEAYNI/AezytayhmpxGUvikM1orad3VGVERuACGxCNIEi4uvmxsImVC32JgiMKLSU7qGFiocpxKLwFkbko4gxTyC1M90XIsgfkBVM6FLs9aTFHdvdSQBpRydIpS7Bn33eYrrj40pAiMqxRhBqGuo2/HfliE+Wo1fO8XoTVNlik5D5xqJN6ErbGJxghhBRB998f7wWZrudjaLwJgaev1+wTXkL18ZLNaM8gRlQny0MWeWSussEjXnPIFrqMqSq20+YEKf78saxAhEWUMJlHqFLHVYjMCYOrQWQdVDJHq4E0/o8c8sTftUJ9ADUWXWzf9wE8ritb+gSB91xFSExeNeRZD/twllxtSgjRHMV2UNCTJBNI9UN8GiX0EWgco1FDFGkGCNA02MwDWvWijOc1xlEQRZb/50gbIsdVCqdTAiYorAiIp2HkHRxfDUn/xxALrZohLLICRYl2IegS5rKH76qIYUWUOac3ztD2IECosgJkX5pArOLAJjauj1GbOdDohkN37RIvi7X3sMnvHoo0Sdu2r1x5C1XryugbQPdduDxSFZQypFIAxW6yaUxXQNDvG5vFy7NrPYmAqYebAMdZdI9OAXO+cuEZbNdqNO/AECs4YitDt2TsT0RQ3DeQQxYwT6YHHMzWQGaw0lsgg0MQLfdbmjZhEYU0FxRchOh0QTuIqd8/IlXRBkHabGzeLcBzFH2amf6SQTyhTn+F1DivYH58QbkQ9jBHI5/J17mMXivS4e+ddKTBEY0XAjo243swgkI3BX5E2/eTJWLpsFEck6+cSuIW/R5IogZoxAP2r2ta5LHw1xDcnaV8WTfMcLBTRlfdfl7mebR2BMBYNRGhG6HRKNGt3Dceyq5QAgtgiG5/vLDNP3FPV6g8X6h1rTEaRwI8SsUtOpu2vRWQSyshI1IK0r1GLyuobywzaPwJgKBss+dAgdYbDYPRwDXy9JXUNyQtaDT7HoXGwFJyXMNRQvWOwUQIoN52O6u0bLygcCXovAuYbMIjCmgeJtnlkE8mDxUA/ED/4lmUcQraZq0lgEEYPlCvHcbRB1Ql+IIvZuNjM8Htc1lGEWgTF1dKXB4ryM8yMTyTqssPRR+Tn+jiDANaQpG9UiSLF5vcI11Ne7hvytx1Nq2nJlfArO3StmERhTQfE+7wiDxe4c574hyB5ITUcQtsSEbJSnYWIziwPOiTki7w86wgBBPCRztymuXzrnwCwCY6qgQbA4wDUkjBFoCFt9dP+O7y8xUx2HJyUr3Ihqr2jXesTvX1p2NGtIHiOQ7m9sO5QZ00F+nxOyUbjMNZT9H1oEsvRRTUeQZB5B8o1pmstqrmUYLI6oCOXNB3WEMTePH6Rv7meboe27lm1msTEVFB+kbkc6j8DFCDD4n2q0HTUAmzhryPfVhUzOinr5irqcrElG8ZpBg9eXL2+/eFhqvdjMYmOqIHLBYn9Z5+LoFIPFgjZS++hjjohD8FsE+mBpxKWWVCTJgEpUVnqOKsMooP3FxhSBEY3RYLHMJ192DQEkm0cQ0LnEXPQspFPVuVOaS6eYnDV6Trw6QxSBpiP21jVwTcUjxDVmFoExVRD2Yx4BAdETHfPqQoKWtXUm9g15XUNBnetkOiLnG9d13rKysmIy15TKNTRSVhZYtqwhYyoo3ufaYDEV00clFoFKrgMja6WI1zWUOEYQM33W3QeTHpHHReEamnaLgIhOJ6KbiWgLEZ1Xcfx4IrqciL5JRNcR0TNTymOkxXUeLn1UNo9g3CKI/bi450+TTx/T7yvNWiniyzAJcXPF9KvHVCoh7WsuRpq1FJISWn5dXa+s3CRJpgiIqAvgAgBnADgJwNlEdFKp2F8DuIiZHwvgLAD/mkoeY/EYBos1rqFC+mhskyDgFO+IOIHfvYhv9KhxDaXIGtKQNGtIdKskcA35my3UlQ+QFOcsNiktgtMAbGHmW5l5H4APAzizVIYBHJq/PgzA9xPKYyRmzDUkiRH0h+UBTdaQZkQcn9SuIe85E25fNydCf47vAtPl/IfU2eKhvpCUiuBYAHcW3m/NPyvyGgC/TURbAVwC4I+rKiKic4loIxFt3L59ewpZjYi4YLFs9VHnThqeG/vBTeEaCpIj4oSysC5V706KIcCkl9Tm0n9fOVHZYvqo1NJosUkw6WDx2QDey8xrADwTwAeIaEwmZn4HM69n5vWrV69edCENGcUHQrpV5aCTHsQIZK6hkJmlGlIsMTGpPP7homeakyIKMJAjflmR9Sh2M8l9Q6mTFRablIrgLgDHFd6vyT8r8kIAFwEAM38dwDIARyaUyVgMiNDpyNLlyjECoC2GdkzXhP6K/CtayuuSjog1hGTtRA1Wq+oSxgg0dRZ1hqJsW0mpCK4GsI6ITiSiJciCwRtKZb4H4KkAQESPQqYIzPdzgFJeYuLeXXP47A0/wOc2341dcwuV54ytNSRcfjS1jz5q+6X/svrjKSJXdFL7EUg7Yk39IdZNzLjSSIaRUNYWe4bSKQJmXgDwEgCXArgJWXbQZiI6n4ienRf7CwC/T0TXArgQwO9wmxftNpop3PCHH7IUt2zfjRf/5zU49wOb8P6v3155StXGNLJgcVpSLDGhyxoKaKCu3QSusbYgciOK66p+7as0xZLli81MysqZ+RJkQeDiZ68uvL4RwJNSymAsPkTAG5770/jDpzwcAPArb/sqdtdYBMW5B+7c2GOBFHnso8FCHshfXVbdvHcORsjoOq5rKCRrJ16wOsg1F9HfFNO6awNJFYExXRRv92WzXTzq6CwzeKZDtfvVDl1D2X/xxjSJHy6NRcA8DHZXl+WR/xJ8FkGIvz11gL22Lh79rzkn9PhI2dJ/WftyRSzVL02DhUkz6awh4yCkvO/wbLeDhZqlSMcmlCVYhnrSMYLhSZr6E3TEAeeEHm8T8gykgpWnKRszsj0hTBEY0ah7ILodwkKves2E8WCxcGMajVyKssNz5E93iqwRr0WgCvzG74lSZO2MnJPEIorvbsvqjxjYnxCmCIzolC3g2W69a2gQI8jvRPGicwEPrcYy1y1HIAsWarqDqOmjA9dMvKwZDUOLJKLyUi2xIStbdvdJy/rbz/631zFkisCIiHt4yzf8TKfeNeQekk5hanGq8dOkRqTDOjUxgoiuoZBzvIposjEaTbmQGIm/XXmQoP32gCkCIyKNrqHaYLGLEWTvSawJJuwa0QQLA5qP2s8GzCyO6fZO0hEHFPb/TpoYgVyWAyGeYorAiE61a0gaI4jfcQe5hhR+X/+Dru+IfctzhGXNTKhHCghWe6tUXX+IayjeMH9gKbfYN2SKwPCyd76HbQ/s9ZarezayYHGzRaBddC6oI4zpGgrx0ctPiUqSDKfEFxOS6eOrK65FIh8ImEVgHBT83nuvxmmv+4K4fGX6aI1FsJib18dEkzUSVL/3uD5YOrF5BAEWkbdOVbB4VA5fOU2donrl1U4MUwSGl6/dcq+oXN3D2WwRZP/VG9NocB1hzCoDRoS6jiaBayjRiDjFOTE7V7lrKGyU759H4JIo2usbMkVgRGPwQJTu95luRx4slloEB5JryI2IJzQ2DErfTOAa002oi1cujWsoTdlJYYrAECPdJ7c87pnt+IPF2s3rNQRl7Uz48U2RtaNqf1IZPsJzYmZAVRX0u5EUGUYHgCYwRWCIma+ZHeyj2yHM184j4IE1AECcWhHWUacZEadxDfmO62MEMV0TYco1HipFKA48y8/RZBgdCIvOmSIwxNS5d8qUF9ea7XZqrYk+88imNO5VXB95gmClyt994KGxSKQdYYjy8pZTlEq1xISsdUsfNQ4SejWjekfjhLKGtYZGFAE11xVCitFr6qwhnwQpRsSj50S0nvTNqyZ/eetKYr2Ya8iYUuZr/PxlxmIE3XrXUJ95ZKTk3BcpfORRs3Y07Q9cQ2k7r9q6EkxoKl6LdDmM1MrL167/ngr8fRIowsXGFIHhxfnw61JAHXUdzkyn3jXEtRZB/Mcn6laFxWChtyNwWUNyogZLU1hEmrIJsnaGdQvG7sJ5FKq4z8hrixEYU0A31wS+YHGta6hLtdZEvz8aLB7ECDwyTWp0Paiz5vXikTZGETWwHdC+1zWWImto5Jx47R8ImCIwvDhFIA8Wj76f7VBDsHgRYgQBweKoywYkyBrSEFaXvCOchGsoxI2TapKaLTFhTAUznew2qQv4Ouru927DMtRjMQJyMYL4TmJdjXLniHw/As0oPuaIOH6MQEVA1pC4akUZTUpuXDeS+/7bmzZkisDw4lw3dQHfMuV89W6nfjIaM6PTGX9AJj3KUnUEaUWpbl9TNiRG4D1HHiwOc814jksjwJAroFB3U8x4zqQwRWB4menmFoEna6juges0bD9Z5xqKiXREGFKnqKwwWDl6jk6e2Gg6t5AJW96yCeIOMb/SSe32lgpTBIYX11GLLQIaf18XXuhzOVicu4ai+ohd1k68hzckwyQgrCBq31tXgpnFRcTbagbI7C0nqkverrZ9SVl3uL2OIVMEhoDcIAiOEQBU+zD2edR3OggWx/SRJ3CNaLYqTEHMBeRCzlG5Rgbps/G+qBQzy1XKXfHzT3rdKgmmCAwvg2Bx4K5ZWefeECOoSh9N8exEHJGGuDliTmjTEOZGkf/WsUTVbRUZoghjfqdpFfFi41UERPRwIlqav34KEf0JEa1KL5rRFnI9IF50rpwd0bSiaK/P1emjnjZCAntxfcTF+qVKY0IjYueaSOWbEI6eU1hxsgllwroUv5TOeshdcy32DUksgo8D6BHRIwC8A8BxAD6UVCqjVTiLwL8MdfXxpj0GelxedM7FCCbTaQ7O8R4vdBoJRnwxs6aCNpGJGKMR15nI3RfiGtK1H8+NOSkkiqDPzAsAfg3A25j55QCOTiuW0Sb06aPl9/W7jjEPJ6wBCotAJIkr6zqCiOZ8gJtnUh1CCkU4WjZORzgad4g/EEg1WzrNzOrFRaII5onobADnALg4/2w2nUhG23AWQegSE40WQWmJCV9dw+MBI1JVWXmMQKozYo7iUy+DrVmyWZ41FeDPi1BM2qpGEYUMKg5019DvAngCgNcx821EdCKAD6QVy2gThyztAgB2zS2Iyo+lj6IhRlCaUEZSk0BBatdNkhmzEd0YSGCRaBRhSYyG4xofvV6pRO3ca15Xl22/TeBVBMx8I4D/C+Ca/P1tzPyPksqJ6HQiupmIthDReTVlfpOIbiSizURksYcWsnJZZgDu3DPfWM7d7uV8daJ611C/z+hWbUyjGJFL0XWevsPxO6JUBI3INfV7rYfFV5QjZaUxAk39qmCxp64WIMkaehaAbwH4bP7+FCLaIDivC+ACAGcAOAnA2UR0UqnMOgCvAPAkZn40gJeqr8BIzvLZzCLYudejCBpu+CbXUGWMIKpFwI0yVJ7jrVNRVtgR1dW/vySfRyGtU3FcnjUUp0yTLNXH9dZDqgl9MZC4hl4D4DQA9wMAM38LwMME550GYAsz38rM+wB8GMCZpTK/D+ACZr4vr3ubUG5jEXE3/c49ga4hQu2TVbtVpV8oMSmsh5BOS9W+73iCEXEo8VxDxTrjWYRc+i9p31unQtaBa67FLiJRsJiZd5Q+kySUHwvgzsL7rflnRX4CwE8Q0RVE9A0iOr2qIiI6l4g2EtHG7du3C5o2YuJueq9FUJc+Cqp9BPo8nKcAFFYf9U7oChiRRnRhhAQLJ9UNBGXNRJwwNYn02tGyAeFixYRCcbC8vXpApAg2E9HzAXSJaB0RvQ3A1yK1PwNgHYCnADgbwDurJqsx8zuYeT0zr1+9enWkpg0pbvrADk+MwFE2gDtUvx5NrxwjiB8rDuqIdVlDPgUz+l/UfkRFGBIjiDt6l43JQ+ZmSK4ppCOO6hpssQJwSBTBHwN4NIA5ABcC2AmZL/8uZJPPHGvyz4psBbCBmeeZ+TYA30GmGIxWkd3J0vTRKtdQ3cPQL2cNlerytSVB2gGqlk3gmteRSOXGiEWKCWWj9cdrfzgQkClsUZ0qRZzWNRcDSdbQg8z8Smb+2XxU/kpm3iuo+2oA64joRCJaAuAsAOUg838hswZAREcicxXdqroCIznOIgjtnKlhGeqyReC0SJIFyjR+f1+dIR1B6sCGr/1UykN4X6hG2Yp5DD4CHEM6i0goa4v1AGZ8BYjoclRcAzP/YtN5zLxARC8BcCmALoB3M/NmIjofwEZm3pAf+yUiuhFAD8DLmfnegOswEuJudOmWhGXnkHetoQqLQNNpS0m1+qW4g4vauesLR+zblVlT6RDdkgl64pjfZRvwKgIALyu8XgbgOQBE6SPMfAmAS0qfvbrwmgH8ef5ntBSpj7W2o22YWRy8xESKEWFxQlPEmbUhaPdbaNoGMUhpBgTDo9YpPC5KH5XWqYh7aH70YYyovSrBqwiYeVPpoyuI6KpE8hgtpK8cUI3PLK7XBD1mzI5YBLKNaTRIg7U615Ci/ZBzvP2QTlGoBfDWqSnrXGOKOiOOBEICzzHTh9ucNuqQuIYOL7ztAPgZAIclk8hoHcNF08L8ttlaQ/UxguplqD1tBXTFcf3ehU5DnDUU0TVVqr9pHRtpsLSufm/7CTo6eYDfX0666GC6eQTyeieFxDW0CdmlEDKX0G0AXphSKKNdaNMfx1cfrT+3z6WZxaU2F5NQd9MkJpRpygaJF9gpNlUVc+SssbKSzCwecSPK6mqxZ0jkGjpxMQQx2ot7QKXB4rGNaTyrj4bMIyhnbTT6yINGxPFGjwOLRHOGwkWddUqC64/qGipaRL7CsnKauIvmmsKyljxl/c0W6nK/f3s1Qa0iIKJfbzqRmT8RXxyjjYQ8SEWa9iMYzxqSziwebVfkGlGMSP0djLwjDJpQFnGczyGKyOuaK7xOMNSdxISy0Al3Mee8TIomi+BZDccYgCmCKcFZAsGuIV/W0Mg8guHnUvxdptTfXP3aX3ayT7pcEYV1dPvLsCOWKxd/nfJ7MmyBPIUi9Nbl5FCLsWjUKgJm/t3FFMRoL9KOpH6tofqHINuPYLSsSqiBXP4zYz6HIT78dK4hRcWxUCnNkI7YV6esXEidksIhqcYt1gOiYDGI6JeRLTOxzH3GzOenEspoF9obeXyJiaG7p+zL749lDenTR9O4ZmR1SuqVZl2p2hfXVPRRK85RlJXL4Tsu71yldRbLTMpya3NswCHZj+DtAJ6HbM0hAvAbAE5ILJfRIqTBYne4chlqVD+0vbqsIZWPurFo0GOYYvXTVEi/q9D0SF+bSb5/8XFJjCDANZig7KRdiE1IFp17IjO/AMB9zPxaZNtW/kRasYw2sb9ZJ4MAcMWx2qwhjWtEnOEjLad0I/jKCuvUNJA6WBl1gbaBRRkxRqC4J4PcSJqBgLDi9qoBmSLYk/9/kIiOATAP4Oh0IhltQxosdofHt6rMj1dUwIzSnsWjdUkQZ5goRrneujT1BmgCvyLSyKp3DWkIGXH7jsuzhhRlYv7+KkXYZhWQIYkRXJzvEfAGZPsWM4B3JpXKaBXuNva7hqqPN+06ls0sHr7vkDB9NKAj1KDyZ7d6rFdURPE6Op1rLr5zSDpbWOCMCYwAACAASURBVFomK1f9urr9cVm8ZVt8m0gmlP1t/vLjRHQxgGUVO5YZBzHqAc1+xAgGbSqa88ondA2M1hPvqR0aBJPqiMfP8bbvOx7kI9/PRivKaiyCZKvPCi2dFusBUbD4OiL6KyJ6ODPPmRKYPqTLUNcdpYY9BkKzhkJGZDHz6MOyhsTNi1MSZXXJy4YgHnEncL2IygW4mfw6q2gRysq22UUkiRE8C9kaQxcR0dVE9DIiOj6xXEaL0HoW6jL6NVlDXtdAgD/Zh25AmvahjiuLXhF5g9UpOnVV5+rqFriGhHKo1g9qkSKOgWSHsjuY+fXM/DMAng/gp5EtPGdMCYNgsafcMH20OlhcRe3qowkCq9JysrLy9qvO2V9U6ZsD14QmriKrU1K26hxvnVIrS9RugCJUkMI1t9hIJ5SdgGwuwfOQ7ST2lymFMtqFu9H9i85VH2/aY4C5tDFNQ6ppXUvSpQui5tGPdFoy6yXqKLpFI9KYGT76xuO1q3I3KpIF9jf9ejGQ7EdwJYBZABcB+A1mtj2Fpwxt+mPVWkPZ6eMVlLOG0lgE+npi5ryHoHMN+Y6HxCg0bco7TWmd4s5VUq9QEaebm9FiDZAjsQhewMw3J5fEaC3OEpDOLC7TtMdAttZQwMxihT93UM53XNVRhgQL5fV726+RpbKscEQassSDigSKSLcxjb+klNHv31M2wCJcbCQxAlMCBgD5Y1KOCQzmBlSU7QfOLNYIpuk0BucoRsQp0gejZg0p2pVXqohRDP5r3C1CMSRlUug0RYzkQHANSbKGjCkn1sziKotifB5ByKJzcVwTUDzcKXrX0FG4uCMSlhOVDWlf5W6KU+dIWWG57LXPNSj3I7XZEnCYIjC8yIPFsnqG7xnMCNuzWOPP1QhZkK25TnlXKHfNCASraN9//U6Ry+Mek06PlH7/qSbp7W9dVWXbbBHYDmWGl6FrxVMuP163DHX5Sevn70cUgbCtKvm85YQdprYueb1pOmIvAXXprAdppy2vU4rMItBXHNUiUre++NgOZYaXoWsozAVTFwB+YO88AKBb3JimadJBsa2REXGcod6k/e4hKYkSYeSKUqMI9UozZgBalTVU8aqpTpUAgvPabAk4bIcyw4/ioQMa0kdLFZxy/mUAUJk1pMlQ8o/I4rsR+iztXuSugX7xmhSdu++6tDGerGy870pcj8LdVTjJi9SlqclE6yuu+aBYYoKIjiKidxHRZ/L3JxHRC9OLZrSFkI6kSNPqowD2O2tInLUTsXMNiVFIFVZsQvLovXUqzpNe/2iwXth5C8ol+f0DLNL2qgFZsPi9AC4FcEz+/jsAXppKIKN9uBtYHCxu2Kqyim7AfgSahzYFaVxD8vpV6+KEiyQixcxa6ZwHUYyg9D8GoRlWbUWiCI5k5osA9AGAmReQLTNhTAna9LvajWlqzhsNFsv2IxhtWCaXtHORlB3xEQud9MnmMXjrklp0k/V7q1z0inPE2cOhrrmISmtSSBTBbiI6Avm1E9HjAdhS1FOENFhchy8TqGIagTJrxVNWGCMIcXfIyrr2PeUSdRRi15TCNaO5fgivv06W5nIiVSAqq1r9NGCc0ub5BJIlJv4CwAYADyeiKwCsRraBvTElyH2sWYGxxJ+G/QiA8qJzurZEZQOev6gdgbRTG+mI5JpI7KOe0JB00GzMBACF311TVkoqpTEpJDuUbSKiXwDwSGTP6c2oX3LeOIgRDvzGIM/x0T2LfaHlqmZlZaNO6NK4kQLaVylCX7sBXWBUN1oCNOs3icVTfP8aRSzUgxNFkjX0JQBrmHkzM98A4BQAV0sqJ6LTiehmItpCROc1lHsOETERrZcKbiwe0kXnHOVRQtNaQ0Apayj/H7fTHP0vQTULV+hGUfQtUdHGeGLLIr/+onKJJ4E8RlL/rrmssH3FOYuNxDX09wA+S0T/DOBYAM8E4J1jQERdABcAeDqArQCuJqINzHxjqdxKAH8K4Eql7MYiEZJ+V6RprSGgbBE016Vpd3A8yDUTp86srNRiUVhBGkUo7ImD8vhL54nkEBxXKS3mxomIKTpg1W+VoP3YSFxDlxLRiwFcBuAeAI9l5rsFdZ8GYIvbv4CIPgzgTAA3lsr9LYB/BPByjeDG4qG2CMo7lOX/64PFVVlDcvn8D6V0SFzz2ldU7BpSWBkqf7qv3vhdkU4RuRGxSruIDzM374IXZBFFtEiHMRpPuQkicQ29CsDbADwZwGsAfImIfllQ97EA7iy835p/Vqz7VADHMfN/e2Q4l4g2EtHG7du3C5o2YsJjL2rK5cfH1xpqPn10iQlXl68jDPDRR+wQU4wIVZ2LgjDXkFy5xHJ5hV6y/7p45H99PWG/qfy7aq8mkKSPHgHgNGb+OjP/O4BnIMKEMiLqAHgTsqykRpj5Hcy8npnXr169en+bNpTsb9aFb25A5aJzgW1VIVcUxdcxlYa0oKJOxXnDYKXGIlHIInZ9Ker0da4l11BzXcI2lRaBcFkssWt1kkg2pnkpM+8pvL+DmZ8uqPsuAMcV3q/JP3OsBPAYZBbG7QAeD2CDBYzbh3vQvOv/uPTR8gHPshEj+xEIl5gIChbHrDNg9B41E4g1ZT0FKur0lxUXlceYVFae/PpDdLpklD9IglDcK22laRnqtzDzS4no06j4Kpn52Z66rwawjohORKYAzgLw/ML5OwAcWWjvSwBexswbVVdgJCekIy3iGzh1q2IEEdNHQ7I2/O4OrnzdVFfM9Yt06K9fX3vD8QCLQfVbxRoIKNvsULbEgvReabM+aAoWfyD//08hFTPzAhG9BNk6RV0A72bmzUR0PoCNzLwhpF5j8dEHi8vvm0dOVVlDKYK1PmKuThpSOFRRSRW0piNM4UZK5e6LtUOdNu6U3des+P7bqwqaFMFmInopgEcAuB7Au/J1hsQw8yUALil99uqask/R1G0sHuJg4+BVTdZQTQ0hMQJVquHgnHgdfVBHqHGNaKyHBBaJj9GywvZjfv8hrsHAYHB1+yyeVdve7n9IU4zgfQDWI1MCZwB446JIZLQOlmqCGnxLS1dtTBMzWJkkayZIEcnKScpqCNqzOerwPeSUeAKEWETeOlGcKBnn/pskTRbBScz8UwBARO8CcNXiiGS0DXcj+zeLyY5r00eD9ixOnL6oCex6641kMZQlkJ4nVUTVtdcd17lRRG2q5NNcf3xF6GIEkrKu4hZ7hhotgnn3QusSMg4uuPTfV66ML300ZNG5kXaTdLTyusQ+6IjKbfQ8z3HxiFjRuSaxiELbF47INe0LfivxtqotVgCOJovgZCLamb8mAMvz9wSAmfnQ5NIZrWB/1xryTiirtAiaUfmohVlDmpRMeevDTsX79SlMkpij+5A6NeeFzCyO+FUlccmo5hEMzmmvRmjas7i7mIIY7UU8IabmuG+Hsk7FhgSpJj+JEbrBIlQ1LFfz2ls2oqzD+jXXF+cH4MAfVeiZ0QXLBcql47mvh/XKBiKTRDKz2JhytKtCStYa6hd2/66OEWjk8xwX+iZU7g6FPzmkTg1y11g85RqyxMakXHNSi0z7+0tjBG1WAA5TBIaXvrDTq5tZXNW59woVjWQNDStrRpnzLalSAze8GysrDLKk2mxHnjUV9g2JYxSKeqTuNmD0/mxqPy4BMYIWawRTBIYfxUipiqoVRYvxhlGLIMHM4gCpo3bEAW3GvCbxMtE1ryvLBlhE3vYDlHtWWHY4rrutYBEI622xHjBFYPgZHX3V387uUH36aKGe/vB45YQyhbkt7bR1k5TkHbHcIpG7ZjTEun5VpcWiQqUVNViqUZpCk0Qb95HOeWlzkNhhisDwInYN1Ryr6tyLrqHiab7JZ1VtiTvCmFkrKn+y1Eed2jUkdGFJ6pQXFZ+jGpGPlN2/dkPOyRadG74W1dlihWCKwPDCzINcf8lNT+UlJio69zrLYrjonEI+T2lp2qvK7yyqUUdIbn72Oo4bKXiHtgQ++oieIbkirDinqc7yfe5vv72YIjC89HmY698cLM4Yj6GNK5F+TU8r3phGE1iVllO4hiYdrK4RpfF4iBUhKis9HtXK48rX+4P2muVZQ21WARmmCAwRUpeN9NyeZ8gd89GRumZGz/Ecr6jfV5dq+8mY/uzSf29B6KyMSWdtNdWbao+DkRiBoKys/clhisBoxD1IzjXUHCyuc/eM06srmzBG4EPlblGNHkNcM/HiGdKqVO6YmL6bioJSd55PFo27bfS8OO62ogxttgxMERiNuIF7Vzj6qaIqu6I+sCxbiFrTabtOxT9y08QI5KNHaTmN3330PFmnFVO5VtXvbz+ichVaLymVGwlT3NqsABymCIxG3MPbkVgE+f9yjKAqu6LoGip2ECEuKF+n7Y6nWg9fWlfM7JawTlPh7vHVGSBLzOvj2jfleuSxBI0eZmT3KpG/bF/4+08SUwRGIwOLoCMIFteN8mm0LqA+RhCy6Fy0zcsLr/37M1fLsl/t16TUVtep77R9cvoUqq/+2uMBilDlGhIeU12edyDAoNx+9YrKI/9aiSkCoxHX4XRkHhsAFemjFQvJFR+eRx19aEVZjYye4wmGYlLXxMg5iuNxrRehfJrAauBIW4p/2QiZrCkzpTKLwJ9CygeAJjBFYDTiHg43+7d5pJYdG3s2GtYaevPzTsay2eFCt0Eb0wjdOBp3T116a1X7PlgoQOr0TpWVpYjRyOWQX78q578xRqC4TxTB+mwegbxs2zFFYDTibuLhhDI9lTOL+87S8K9UWi1Y7Zsx5G6GYTmvm0ThcwhxcuhiFLKOyDdy17hj5K0P21V1xJGUoiZrSG8RyFxDQ0XcXo3QtDGNYRRcQ5L00ez/+FpD434lV0+3U1IEnhjBg/sW8JIPfRNf/Pa2sXZ9csVdyK76tfScyuOKTqvuvJB2K+vRKKJoinCIzyJD4HclgUjmbiPINqcJmcey2JgiMBoZuIY6o+8ry9Z8XrkfAY8qmHLputHrSa++VNxuuS2VayiS3z0rPF5/QzF1WX/7LDqnqOT9BpHcjVISQ3Rc1b5gcCIRwB3tEMm+f+Xqo23GXENGI4ORu2J56Nq1hgqfDWMPqCyrIclIS+HPTzIi95bVdNqy+kcXF4z3pYZUFW1CWUAsR3QL5jECgkBpDCzS9mKKwGhkMEran/TRhv0IJLuZeWUU+r41IzfNonMhM4d9x0NmrvqOx9zSUqMIQ+qMtdlMiLstmxvg/62ICBCVdbK0VxWYIjAa4XzfAMmic47a/QgqUv7GgsVBG9M0I51QpspaCVBUKtdIREUUtDFNRDfWsJxCzkgWWYicJHENDSwCAS1WAA5TBEYj7uEUrTWEatO6agrCMEZQU1bV0fqO6x9E1RITwk5TlwkUD6lrQhMjGKm/se2wOjWT2xrTRwOsHEnnzlxcYsJTVlZsopgiMBopj9yDUgwr9yMYrXdQtGGJibqHWjp697uGFJ17gMvBP1tZ1ftVylJZVNgTFXeN8zafIJ5Q5TqsLSv8rUIspw6R6F4hkGiJiaFrzlNwgpgiMBpxD+SSmexWWeg1jb6y/+NrDVXsRzCIEYyWbdqYpnbpaqkbI2LcQYO0I9B0riEWiY/QPP7GOgNjCdLO1VdW034vV4QzHaFriGSb07R5/oDDFIHRiLuFl3RzRdAwbKy73avcPYPF7GotgvHaAvWAfIcyxShfs5iZaz90wpYP6Z69us5VoYgay8nrLKLLGhKaBMI2O2V/pafamKuqTgpTBEYjZYtg34Lkrq4JAAtcQ46qVuo6h1jpeyPyKTbO8SqivkwR6BRR9evmc2QKS1RnUPvy47pgeVM5jbvPDU58tTqLgISTz8bbaBtJFQERnU5ENxPRFiI6r+L4nxPRjUR0HRF9gYhOSCmPEUB+3w5cQ00WQZ27p2L9oH6/+NCNl616DutcQ+L9CBQPYaz0xWJd/s4tzN2jUTDScjrrpcldGNbxxdrNLeR3Ks92r6l5kDWk0JmtJZkiIKIugAsAnAHgJABnE9FJpWLfBLCemX8awMcAvD6VPEYYZdfQfEOMoI7qmcX5MUX66P5aBD5Ui46NDImb65W6hjSumdH6m48PNobx1sOVr6vrlKGxnEZjSMIGPDWrLLfBQEYRI5CsPqqxtCZESovgNABbmPlWZt4H4MMAziwWYObLmfnB/O03AKxJKI8RQNk1NN/zp5aMpY9WziyusQgGxytkqWla6s+P6W7RdNquU9NMUvNRbN+7/7PQIilWE2sZ6Co5JPgtEln7ulhO9r8ryhoK20SppXogqSI4FsCdhfdb88/qeCGAz1QdIKJziWgjEW3cvn17RBENH+4mn+36s4bqoIrU00GMQLHoXL1FIHvAVQ9spBFxsS6/RaBxDck7OB7817Qvv8JGH73qO5efJ84aUshS3J9b8lsNNqYRpi+3mVYEi4notwGsB/CGquPM/A5mXs/M61evXr24wk05YxZBY4wg+182l4ezksddL+OTz+pnMNdteK9xOUjRxAi8aYlOEUQMQBeJtUBesZTUyii/Hq9TflVVFqMEaYzA17m732e2SyLrjfIgQUylPSlSrj56F4DjCu/X5J+NQERPA/BKAL/AzHMJ5TECcPftUqcIFprSR6tvcpcZVPQq1ccI6uuq7UiFLo+Yy1BLXRNAMWvIU2Pg6Fnqz5fKmdWpkEX4vYYEbuvblLU/sk+21DXUIb/SZvkSE5pssEmR0iK4GsA6IjqRiJYAOAvAhmIBInosgH8H8Gxm3lZRh9ESZrvZLb8g6CHKD4dbwroqGKmKEdTqgTg+8qpgtqSsD1c2lt97tKR87X5f66MxgjiusVB3nM6N1VRQLou75tluR3b9wqVyNe6pSZFMETDzAoCXALgUwE0ALmLmzUR0PhE9Oy/2BgArAHyUiL5FRBtqqjMmhCZYPHQNjX4+WJ6iX3x4eeTYgIYYQa1rKJprRNERK0Z5w6wheZ2asWOsIHTw9QsbiOnuqpOlqU2fu8v9TjNdErjG3MY0JEh1bWnvXyDpxjTMfAmAS0qfvbrw+mkp2zf2H3cPL+lm+wqHpI8OF6wbfuZCDeNbVdanYtSNfKWj57jBYrnfV7z6qUw0V1mhfmFHpOjcfR3hyHnScorrV5VtnMcwfO2NEeSHZzod0b2SpY/6y0llnSStCBYb7cXdtsO1hgQWQck55Nw/vYrOq37y2Ti1WUO1EjWfN1aPYpSrcSMFzSPQuIYiZQ3p5hHIFGHoYDiWRVI1ibGOgUXQIW88wcUIsjY8BFhPi40pAqORkHkEZToVWUO1q4/m/yuzhmotAnkHK0XjI/ePNKWuIblrZvQ82XG/j1xfpwbfKZrRu7hNhcJ2ZWe6gvRRsHrz+jZjisBoZOgayrroJteQO1IXI+hVxQhKdyBVKA3H/i46p3M3NKNxowwnlMVxd2jbl2f1yC0CKboVTccHCv6SshhBVzDKdxbDTKcjzxoiUgW224opAqMRdxNr1hoqUxkj8FkEFfWkX3QuzDUitQhijrJVsoqvf/haFSNo6ohHrIw4dZbrkmxM0xUEdQcxAsk8As4GPB2Kp7QmiSkCoxF33w5dQ/o72fX1o3nquUVQu63leD31nZNsRBbT3QJhp8nM4vTR0P0ApC4PzeqjKjdWJOdHCqXhjnU7/kygfkFpiFxDIHRINueg7ZgiMBpxN7FbYmJf44SyjLJrqGqby/rN6+s3pgm1CKSD29GRm9+N40RvVgRFOeRDR4UeiuZ6CO2IG+useV1Fr2KgoG2jjmzZCE89zNkoX6A0mAGQTMFoFjOcFKYIjEaKo6QO6VwGjkGMoOLhrp1HUBUjCF10Li8RdUVPZswI9nHuaUbZYulKbhyva8hdfzOD31qy1k6NLHVtSwQYiSE1FxUrraJF4M8ayu7HrmT1UWS3qiT2oMkGmxSmCIxG3I1LRJjpdJpv+kLZIsP9joef+VxDVeyvRaBKSWwuCsbQ0mlKpBrd7CVNhyFVMNLvKUuflLcvldWnXDRKc6T9xmNy5dZjzgY8Hb9yBWf3qsgiaGnnX8QUgdFIMd+/0/G4QWoeyU5VjKB2QlleV0VV9YvOxRpqy10TzMPF9Jo6jaFrTbKQmdyFoHJjSV1jecFsz16F0moql/+f6ZB3eRKNa0i8eX3BIvCvNZS7hhQxgi75FcHoee3EFIEhgpCl1Ulu+vKgvilGMHZu08Y0gTOLB1k7zcVKlfoPu+tq6jSL7pZUI0dNp9lcLqMjcg3JLB13SLJsw8hxhfXWVNgdyQLA/jo7JA8Au3iCbnnxdqoCUwRGI+6+7QhiBEM30ujnrnMfnUeQ11vej6B0vEjoPAKpa0QT2GVmzOQB9KbvpLhsgTvPJ+f4m+r2B2145vhJ/enFmbW+OrV43YrQWQTS/ZVH9hjwxQj6jA4JA8vIXUMCi6CdXf8opgiMRoquoZmuzCIoMxw5j9dbmz5aUU/ozGLxEhMjdfrLDmMEMosge99Q50hKphxvsHKkjfpywzx6f6ctjqcUXDPMzcs8aBSBNLA8sAgEI3cXLCZBUoTbmCaz9BqLqmJPk8IUgdGIu3Gd77TJzzsoK1pryB2rSR+taCZ8I/SyhH4kqZ6SrKHiZie+sqMdtsLvPIkYQe2bcrnR62+6f3oj7TfLupCP3jO5G9ofWGTZKN9nEblRvjR9OFMEzZogdH7IYmKKwGikmO8/I7jpqxhmDY2P+OoXnRt/YkKWoVZt/6gYuTFYmDWU/a+Kk1RUKmbUuvKWFjVRdAPG3phGcv1uFC6Z2dvrD11zzZ12bnlWWKVjJZnR6WQxAqlF1BFkWLW18y9iisBoZOD3B7xmcF2MQLUfQamuIrWuoYaOKMCTlbfv7whEFsFglNsZnFdbp7DDLpfVrWoqkFW0Hr9HwFI5d/2NFoGznjod7/c/32MscYpA1L4/w8u5hjriGAGhS/5VTaXW0yQxRWB4GHbYPjO4bh9i1VpDDfMI6p5hsUVQX2ysrCTDJCxG0OAa4urXPqJtTFPoNGOuCwQUvquG4XPRIvBV2ev3MdMVjPLz/1VWaZk+Z+6mDglcc8yFwZFCEbdUE5giMBpx9/jAHxpwHw/mEVS4hsa3qqxPyawPFte3Pbq0snzkJtmYRqIIyiPS5mBxY5O1Zf2pjjJlODqz2CuBqE53TDIiHyoC/1aRC30eWllN6aPu+xcojYFFIMwEcnEz7+Szlnb+RUwRGI0MXUMCi8Dd7xXpo1QyoYcKptoiqE4fHX7YLWiQ5o5I/hBqNm9nHmaYiFxD2mCxR9bi8XjLQWT/ux3Bnr1C64VL19+0eq3rUGcFiqjX50EAWhKsrloKvUyWPkrCdYnyuTVKN1pb4wWmCIxGhr78fOmBQKd7OQBZrLeIdBnqr5/3i/iPF6wfqasKTQB4XyEAIu2IfXnk7ljXjV4bYyxc+dqHdPVRwBdPyTtt5e8sCxYL5ly4GMGMP0aw0OPhKL9JtoFyk7uGyoOWynqRbUwjtR6qXrcJUwRGI4N73JnBAemjQNbh90Y6Ovd52SKoN+GLgeofP3QZHvnQlSPtVsqkGI25JbaXdP0dEXMma8ezdMG4a0hmEfiQBoAlx8vtz3T96Zu9Po9YZd46BW40F0iWpI/2+oxZSQC+rAg8GV6ULzonsYhcjMBmFhsHPUXTWmIG11Fev2UYI6jbmGa8ndoOt9HvOzzok91tw7lkRrJ5eR4s9CxHoHINKayX4vejGpEKvitJR7iv18fSGXlH7K5fYj3NSmMEA4ugSbkOrwnwff+MTgde5Z6V1c0slsSTJokpAqORYvqob0KZK1yV+VNev6UYhC7SFCOYL+2F0DTnYNjOcJTZtJcCMKoIRCNC8qfUFlf0LL6vqbXypQ9v3yKsqzjnwasIFgqKQFD3rCB9tKg0/RZBX5aSW1ZEXtcQieZR5He6eO8C913Nee7BSWGKwGhkoAgGE8okrqFxygG4+gll9X7ffb2yIpCnDy6b7WJfr99omjvX0FKBRcAoblXo79wkC9Rp0gy54rtsklWEC9Z2O96OcL7XH+xa1/RlFZeBBpp970PXkMAi6A2DxZLrk6WPDtfUkmRikTBuxswDRVC+h9uCKQKjkaFrSLZzUx3l9Vt8E8qqOpe5+R4A4PRHPzSrcyBjPS44u2zW/yDqLIJ8q0LPd9IvuDsAz5LVjS02yyI9Lkm1layoObfQx9KZrkC47N+MYImJ4nIcPkW80OfBdypa/VQUI+CBlSdZn4ogdCMBg+/KZ5VOClMERiNFF47XIihYD2W6pUlKdRPKXFtNFsEbf/PkQbliu5UywY3y/Q+iUwRLZzqirKGBj9gzygSki85Vv/ahmVAmdaPFdg3pgsUdr0XU6wuzhkpLTPhiNF0ikHDJavf7+6wHMLDUDURMERgHIq7zJjezODDroTzxpm5CGZCNtKqamZsfjtizcv5goXtGlwkeRHdsiSB90WWNdDyzcIudK9DschjMzCa/Iii2GWs5iJEYgae/2ldwDUlcc9L00eGIvLn9hX5/YBE0ziMoWQS+WeBuq0onT229KKw+KrIIzDVkHMAUg8W+6fQDpVFxrByAq5tQ5spWde77ev3BfIas3KiMTTI5i6ApWDeaPlpfJ+AsAv8OVeMxgiZZs/+17rKKstlrX0ckcw25erLd1PwWwVARyF0zvtVHu5JJen1Gn4d1NmcNZUi+/16fB3EfwG89dDr5AMcz3T6LEeT337wpAuMAZDhK9Xd6TY9DOQDHzJXWQNZWjUWQdz4D5eGfWKqyCNQxAkHW0LAj7OTy+L+/DvmDxXML/cG6OLJZ0KNtNMkq2apxvqd0DQnSRxf6w9U/GzvsQXaRJGto1CKTLjrnK8vILNIZoUUwjFH1GstOClMERiPDzkG2wBZQnT5aDsA5M7zyfFQvOravFKAcTFwTZK0MYgQNvfZCrw8iWdZM1n42Koy2xETBDeezSHbNLWDF0hnZhCYMrQzxDmWe9su/RX3b5NzARgAADY5JREFUox1x0xIT/T5jpkP5QKDBcigElQGZa0oWI+DBKN9bb+4b7Ag3prFgsXFAM3DhIJtQ1rgxTaFsmWxW8mi9tS6QWougNxiFAs27mZVlEsUIelkmCpF/9cssWOi3koZLTEjmEWQQ/KPsB/YuYOWyWdneAU3f9WgxAJk/3+duGnUNNdQ5iDvkFlFDP9jrZ8FX3zUVg8pFuZval8RohvMInDw+iwDoegYCWVm2eQTGgc1wlDo+KUxDpzOextigBypdI3Pzhdx1NO9v7HAPqSxG0Mdsh0TLEHM+s9iXPlieUCbxp69YOoPdcwuN7T+wdx4rl82I3DgMFrrRhqN3n+W3T+saElgEvX4f3S55XWML+YhiuKJooyoAIFPEgyUmhJsISQYCmXzZPBZgSi0CIjqdiG4moi1EdF7F8aVE9JH8+JVEtDalPIYed4u79FHJVpVV0eJy1hA3uYZqhsRzhc4nKyfvXJcvkaWPzs50vD5qV68kfXC4ebqLETTUmV/0jx+6DNsemGts37mGZJOfpIH17H9XsEPYnDBYvOAC8DP+rCEXLAaas5bcPeg2ppGk5Er3jsgWnfPPOXAWgWhmMaY4a4iIugAuAHAGgJMAnE1EJ5WKvRDAfcz8CABvBvCPqeQxwhhmAsmm09fRHcsaaggW18QIMougGCPIZWxo143qpBPKZrsdEATmPheX5vZbBKI9i/NDDz10KX64c29j+7vmFgoWQWPRkRhBE/2By0UaLPbHCG67dzcAYO2RhwDwrzXUyS0y6ZaWGf7BiWytoXwDJlHWkHxm8YEwj2AmYd2nAdjCzLcCABF9GMCZAG4slDkTwGvy1x8D8C9ERJxgib6Lrr4T7/zKrbGrPejZlbso3E1/9869ePqbvlxZ9r4H5wdlyxABl3972+Dc7bvmKlNHXdmLNt6Jy7+9beTzu+7fg3VHrRwpBwAXXH4LPnTl9yrrGk4SyzqtV3z8OhyytPq2v3vnXqxcOgMiwne37aq9TgDYet8e/NSaw9AhwhVb7qkt++C+LEvEjUj/zwc2jVg1RXbuzb6/hx62DA/sXcDT3vTlylRcALjj3gfxS48+CkTAx6/Ziq98d3utrL3+0Pr6tX+9YtAplvnR7n0DWXfNLTRe/975oXX29i/fgo9cfWdluR17smt61NHZ7/Y3GzbjDZfeXFn27p17cciSTLndun13bfvOIjhm1XLMdgl/8MFr8IjVKyrLDr//TNY/+OAmLKtRYFvv24PHHHvoILD8nH/7Wu0Kq3fdvwenHLcKnQ5hz3yv8bvaV1CaF1y+pfZelfAnT12HZ518TPD5daRUBMcCKN4dWwE8rq4MMy8Q0Q4ARwC4p1iIiM4FcC4AHH/88UHCrHrILNYdVX2zGM08ZfkSPGz1Ifj1U9dg91yv0X977KrlWFnR0b7o5x820lmtO2oFHnPsYZV1/NH/egQ2f3/H2OfrjlqBZ+TLSwDAYctn8aKfOxHf37GnUf5TjluFc564FnMLvUHHVMW6o1bgcScegTU/thxLZppH0OuOWoHTH3M05uZ7uPzmbY1ln/jwI3DOE9Zix5557J1vTh884pClOOeJJ2D3XK/Rn77uqBU4+7Tj8VPHHoZrt97fWOcjH7oSz3j0Q3HZjT9srBMAHr56BZ76qKOw7YG5RpfPIx+6Er+xfg2WznRw530PNtb5sCNXYP0Jh+Ps047Hjj37Gq/ptLWH44QjDkHH46s4ec1hePbJx2DV8iX46pZ6JQgAT3j4ETjniSdgx5557Jmvj7243/TkNYfhzFOOGQwi6so+52fWYNXyJdh6357G7+onHroSzzl1DZYv6eKO3EIK5bDls/t1fh2Uan1sInougNOZ+UX5+/8N4HHM/JJCmRvyMlvz97fkZe6pqhMA1q9fzxs3bkwis2EYxsEKEW1i5vVVx1IGi+8CcFzh/Zr8s8oyRDQD4DAA9yaUyTAMwyiRUhFcDWAdEZ1IREsAnAVgQ6nMBgDn5K+fC+CLKeIDhmEYRj3JYgS5z/8lAC4F0AXwbmbeTETnA9jIzBsAvAvAB4hoC4AfIVMWhmEYxiKSMlgMZr4EwCWlz15deL0XwG+klMEwDMNoxmYWG4ZhTDmmCAzDMKYcUwSGYRhTjikCwzCMKSfZhLJUENF2AHcEnn4kSrOWW4TJFobJFobJpqetcgEy2U5g5tVVBw44RbA/ENHGupl1k8ZkC8NkC8Nk09NWuYD9l81cQ4ZhGFOOKQLDMIwpZ9oUwTsmLUADJlsYJlsYJpuetsoF7KdsUxUjMAzDMMaZNovAMAzDKGGKwDAMY8qZGkVARKcT0c1EtIWIzptA++8mom35Zjzus8OJ6DIi+m7+/8fyz4mI/jmX9ToiOjWhXMcR0eVEdCMRbSaiP22RbMuI6CoiujaX7bX55ycS0ZW5DB/JlzkHES3N32/Jj69NJVtBxi4RfZOILm6TbER0OxFdT0TfIqKN+WcT/03z9lYR0ceI6NtEdBMRPaENshHRI/Pvy/3tJKKXtkG2vL0/y5+DG4jowvz5iHO/MfNB/4dsGexbADwMwBIA1wI4aZFleDKAUwHcUPjs9QDOy1+fB+Af89fPBPAZZPuzPx7AlQnlOhrAqfnrlQC+A+CklshGAFbkr2cBXJm3eRGAs/LP3w7gD/LXfwjg7fnrswB8ZBF+1z8H8CEAF+fvWyEbgNsBHFn6bOK/ad7e+wC8KH+9BMCqtshWkLEL4G4AJ7RBNmTb+t4GYHnhPvudWPdb8i+0DX8AngDg0sL7VwB4xQTkWItRRXAzgKPz10cDuDl//e8Azq4qtwgyfgrA09smG4CHALgG2b7X9wCYKf+2yPa+eEL+eiYvRwllWgPgCwB+EcDFeYfQFtlux7gimPhvimwXwtvK194G2Ury/BKAK9oiG4b7ux+e3z8XA3hGrPttWlxD7kt0bM0/mzRHMfMP8td3Azgqfz0ReXPz8bHIRt6tkC13vXwLwDYAlyGz7O5nZrcLebH9gWz58R0AjkglG4C3APhLAG6X8yNaJBsD+BwRbSKic/PP2vCbnghgO4D35C61/yCiQ1oiW5GzAFyYv564bMx8F4B/AvA9AD9Adv9sQqT7bVoUQevhTHVPLJeXiFYA+DiAlzLzzuKxScrGzD1mPgXZ6Ps0AD85CTnKENGvANjGzJsmLUsNP8fMpwI4A8AfEdGTiwcn+JvOIHOR/hszPxbAbmTuljbIBgDI/ezPBvDR8rFJyZbHJc5EpkiPAXAIgNNj1T8tiuAuAMcV3q/JP5s0PySiowEg/78t/3xR5SWiWWRK4IPM/Ik2yeZg5vsBXI7M/F1FRG53vWL7A9ny44cBuDeRSE8C8Gwiuh3Ah5G5h97aEtncCBLMvA3AJ5Ep0Tb8plsBbGXmK/P3H0OmGNogm+MMANcw8w/z922Q7WkAbmPm7cw8D+ATyO7BKPfbtCiCqwGsyyPsS5CZfRsmLBOQyXBO/vocZP559/kL8qyExwPYUTBNo0JEhGzv6JuY+U0tk201Ea3KXy9HFru4CZlCeG6NbE7m5wL4Yj6Ciw4zv4KZ1zDzWmT30xeZ+bfaIBsRHUJEK91rZP7uG9CC35SZ7wZwJxE9Mv/oqQBubINsBc7G0C3kZJi0bN8D8Hgiekj+zLrvLc79ljro0pY/ZBH+7yDzMb9yAu1fiMy3N49sVPRCZD67LwD4LoDPAzg8L0sALshlvR7A+oRy/RwyU/c6AN/K/57ZEtl+GsA3c9luAPDq/POHAbgKwBZk5vvS/PNl+fst+fGHLdJv+xQMs4YmLlsuw7X532Z3v7fhN83bOwXAxvx3/S8AP9Yi2Q5BNnI+rPBZW2R7LYBv58/CBwAsjXW/2RIThmEYU860uIYMwzCMGkwRGIZhTDmmCAzDMKYcUwSGYRhTjikCwzCMKccUgTHVEBET0RsL719GRK+ZoEiGseiYIjCmnTkAv05ER8asNJ9kZM+XcUBgN6ox7Swg2+/1z8oH8pnNHyeiq/O/J+Wfv4aIXlYodwMRrc3/biai9yOb9HMcEb0hP349ET0vL/8UIvoSDdfk/2A+WxRE9A+U7Q1xHRH902J8AYYx4y9iGAc9FwC4joheX/r8rQDezMxfJaLjkS3t+yhPXesAnMPM3yCi5yCbRXsygCMBXE1E/5OXeyyARwP4PoArADyJiG4C8GsAfpKZ2S2vYRipMUVgTD3MvDMfxf8JgD2FQ08DcFI+WAeAQ/NVWpu4g5m/kb/+OQAXMnMP2cJlXwbwswB2AriKmbcCQL7M9loA3wCwF8C7KNvx7OL9vjjDEGCuIcPIeAuy9Z8OKXzWAfB4Zj4l/zuWmXchcycVn51lhde7he3NFV73kG0usoBsldCPAfgVAJ9VXoNhBGGKwDAAMPOPkG3798LCx58D8MfuDRGdkr+8HdnSyaBsn9oTa6r9CoDn5ZvrrEa2XelVdTLk1sZhzHwJspjFyUEXYxhKTBEYxpA3IvPlO/4EwPo8cHsjgBfnn38cwOFEtBnAS5CtalvFJ5GtsHktgC8C+EvOlmGuYyWAi4noOgBfRbYfsmEkx1YfNQzDmHLMIjAMw5hyTBEYhmFMOaYIDMMwphxTBIZhGFOOKQLDMIwpxxSBYRjGlGOKwDAMY8r5/2cTRMdB5QotAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert data\n",
        "spike_data = spikegen.delta(reshaped[0], threshold=0.2)\n",
        "\n",
        "# Create fig, ax\n",
        "fig = plt.figure(facecolor=\"w\", figsize=(8, 1))\n",
        "ax = fig.add_subplot(111)\n",
        "\n",
        "# Raster plot of delta converted data\n",
        "splt.raster(spike_data, ax, c=\"black\")\n",
        "\n",
        "plt.title(\"Input Neuron\")\n",
        "plt.xlabel(\"Time step\")\n",
        "plt.yticks([])\n",
        "plt.xlim(0, len(reshaped[0]))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "PiiUS4lIik4J",
        "outputId": "db7e9b80-4ef7-43a1-f612-49a424bea97b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = reshaped[0] * reshaped[1]\n",
        "print(result.shape)\n",
        "\n",
        "plt.plot(result)\n",
        "\n",
        "plt.title(\"Image data\")\n",
        "plt.xlabel(\"Neurons\")\n",
        "plt.ylabel(\"Pixel values\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "WqsUUVgwlWKf",
        "outputId": "fbcffeff-9bbf-4e14-c63c-95852281f44b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([784])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result3 = reshaped[0] * reshaped[1] * reshaped[2]\n",
        "\n",
        "plt.plot(result3)\n",
        "\n",
        "plt.title(\"Image data\")\n",
        "plt.xlabel(\"Neurons\")\n",
        "plt.ylabel(\"Pixel values\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "RY6DCyqdl1tn",
        "outputId": "dd4e1581-a72a-4258-c12d-dba01e6a1955"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result4 = reshaped[0] * reshaped[1] * reshaped[2] * reshaped[3]\n",
        "\n",
        "plt.plot(result4)\n",
        "\n",
        "plt.title(\"Image data\")\n",
        "plt.xlabel(\"Neurons\")\n",
        "plt.ylabel(\"Pixel values\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "VtsFD4e-mPhe",
        "outputId": "d3318e9a-2b30-4b11-ee70-5a81044d3f88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result5 = reshaped[0] * reshaped[1] * reshaped[2] * reshaped[3] * reshaped[4]\n",
        "\n",
        "plt.plot(result5)\n",
        "\n",
        "plt.title(\"Image data\")\n",
        "plt.xlabel(\"Neurons\")\n",
        "plt.ylabel(\"Pixel values\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Jbrtp9cFmohF",
        "outputId": "362a27c2-0d28-4960-efbd-52234928a748"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert data\n",
        "spike_data = spikegen.delta(result5, threshold=0.2)\n",
        "\n",
        "# Create fig, ax\n",
        "fig = plt.figure(facecolor=\"w\", figsize=(8, 1))\n",
        "ax = fig.add_subplot(111)\n",
        "\n",
        "# Raster plot of delta converted data\n",
        "splt.raster(spike_data, ax, c=\"black\")\n",
        "\n",
        "plt.title(\"Input Neuron\")\n",
        "plt.xlabel(\"Time step\")\n",
        "plt.yticks([])\n",
        "plt.xlim(0, len(result5))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "BYFwBYlCnrwy",
        "outputId": "4138e9fe-db51-4583-b3f8-fb3a00398605"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading neuromorphic dataset"
      ],
      "metadata": {
        "id": "e16cP-UPs4Ul"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loading datasets from neuromorphic sensors is made super simple thanks to [Tonic](https://github.com/neuromorphs/tonic), which works much like PyTorch vision.\n",
        "\n",
        "Let's start by loading the neuromorphic version of the MNIST dataset, called [N-MNIST](https://tonic.readthedocs.io/en/latest/reference/datasets.html#n-mnist). We can have a look at some raw events to get a feel for what we're working with."
      ],
      "metadata": {
        "id": "pCEna_wW4aa9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tonic"
      ],
      "metadata": {
        "id": "xmilDmHos7ve"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tonic\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "dataset = tonic.datasets.NMNIST(save_to='./data', train=True)\n",
        "events, target = dataset[0]\n",
        "print(target)\n",
        "print(len(events))\n",
        "print(events)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153,
          "referenced_widgets": [
            "5706f996092c42728234deb1d7b57f70",
            "fc2d2049970243b0b53a2028b5dfd99f",
            "52a1efd49fb7472884cfc13167d8bda7",
            "9246ce7321c548cfb0cde6c746e827f9",
            "d09e9a2b69454d89a7d389d51f62eb0c",
            "b14d4c39933b40a69c8b2a3164246519",
            "5eff40a6098d459196a9718ab58bdaae",
            "c6f5f61e4e9c4b8cb441c517e7cb0794",
            "e021e073d0404949a41880f64a76fa96",
            "fd7f2cebf88746d6a4c86e4e3b323f6f",
            "0a445b7e40584ca9bdad58e91fa25b27"
          ]
        },
        "id": "Rdc1gAY0qVyP",
        "outputId": "562a84ab-77ba-48c3-8925-31306ded9594"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://md-datasets-public-files-prod.s3.eu-west-1.amazonaws.com/1afc103f-8799-464a-a214-81bb9b1f9337 to ./data/NMNIST/train.zip\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1011893601 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5706f996092c42728234deb1d7b57f70"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/NMNIST/train.zip to ./data/NMNIST\n",
            "1\n",
            "2723\n",
            "[(24, 10,    105, 1) (13, 12,   6115, 1) ( 1, 13,   6149, 0) ...\n",
            " (19, 18, 301991, 1) (12,  7, 305136, 1) (32, 23, 308710, 1)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Each row corresponds to a single event, which consists of four parameters: (*x-coordinate, y-coordinate, timestamp, polarity*).\n",
        "\n",
        "* x & y co-ordinates correspond to an address in a $34 \\times 34$ grid.\n",
        "\n",
        "* The timestamp of the event is recorded in microseconds.\n",
        "\n",
        "* The polarity refers to whether an on-spike (+1) or an off-spike (-1) occured; i.e., an increase in brightness or a decrease in brightness.\n",
        "\n",
        "If we were to accumulate those events over time and plot the bins as images, it looks like this:"
      ],
      "metadata": {
        "id": "tg4syAO34jJI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tonic.utils.plot_event_grid(events)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "6PkQYdn24Rc8",
        "outputId": "c1f36426-18df-4ca3-b79d-c6b397c35369"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "However, neural nets don't take lists of events as input. The raw data must be converted into a suitable representation, such as a tensor. We can choose a set of transforms to apply to our data before feeding it to our network. The neuromorphic camera sensor has a temporal resolution of microseconds, which when converted into a dense representation, ends up as a very large tensor. That is why we bin events into a smaller number of frames using the [ToFrame transformation](https://tonic.readthedocs.io/en/latest/reference/transformations.html#frames), which reduces temporal precision but also allows us to work with it in a dense format.\n",
        "\n",
        "* `time_window=1000` integrates events into 1000$~\\mu$s bins\n",
        "\n",
        "* Denoise removes isolated, one-off events. If no event occurs within a neighbourhood of 1 pixel across `filter_time` microseconds, the event is filtered. Smaller `filter_time` will filter more events."
      ],
      "metadata": {
        "id": "CarVjF1R6Hwg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tonic.transforms as transforms\n",
        "\n",
        "sensor_size = tonic.datasets.NMNIST.sensor_size\n",
        "print(sensor_size)\n",
        "\n",
        "# Denoise removes isolated, one-off events\n",
        "frame_transform = transforms.Compose([transforms.Denoise(filter_time=10000), \n",
        "                                      transforms.ToFrame(sensor_size=sensor_size, \n",
        "                                                         time_window=1000)\n",
        "                                     ])\n",
        "\n",
        "train_set = tonic.datasets.NMNIST(save_to='./data', transform=frame_transform, train=True)\n",
        "#test_set = tonic.datasets.NMNIST(save_to='./data', transform=frame_transform, train=False)"
      ],
      "metadata": {
        "id": "9iyZ80vh7ypi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40e63099-6a26-4b38-b1c4-13b165ff3227"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(34, 34, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Explore the data shape after conversion to tensors\n",
        "input, label = next(iter(train_set))\n",
        "print(input.shape)\n",
        "print(label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pi6xv0GOBSDV",
        "outputId": "6e1d24f7-3431-4a6e-8419-cf53ccb15757"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(301, 2, 34, 34)\n",
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Decompose\n",
        "print(input[244].shape) # timestep\n",
        "print(input[244][1].shape) # polarity\n",
        "print(input[244][0][12].shape) # one row of the tensor image\n",
        "print(input[244][0][12])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pYtPq4kIByGW",
        "outputId": "d9100b12-7a7a-4c32-cbdc-6a48987cd767"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 34, 34)\n",
            "(34, 34)\n",
            "(34,)\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's find where th spikes occur\n",
        "for i in range(34):\n",
        "    print(input[244][0][i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V-RxcH1SDouK",
        "outputId": "e861188d-e792-4616-dc41-290c9ce3aebb"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The original data is stored in a format that is slow to read. To speed up dataloading, we can make use of disk caching. That means that once files are loaded from the original file, they are written to disk in an efficient format in our cache directory. \n",
        "\n",
        "We can decrease the time it takes to read 100 samples by using a PyTorch DataLoader in addition to disk caching."
      ],
      "metadata": {
        "id": "Ra0V-XBH8MGW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tonic import CachedDataset\n",
        "\n",
        "cached_train_set = CachedDataset(train_set, cache_path='./cache/nmnist/train')\n",
        "cached_dataloader = DataLoader(cached_train_set)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Br447gFT8Md4",
        "outputId": "47fd9900-3a54-448f-f7ba-75b94b53a161"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: DeprecationWarning: CachedDataset is deprecated and will be removed in a future release. It currently points to DiskCachedDataset to distinguish it from MemoryCachedDataset. Documentation available under https://tonic.readthedocs.io/en/latest/reference/data_classes.html#caching\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we've reduced our loading time, we also want to use batching to make efficient use of the GPU. \n",
        "\n",
        "Because event recordings have different lengths, we are going to provide a  collation function `tonic.collation.PadTensors()` that will pad out shorter recordings to ensure all samples in a batch have the same dimensions. "
      ],
      "metadata": {
        "id": "o6kBm0Rf9iF8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 128\n",
        "trainloader = DataLoader(cached_train_set, batch_size=batch_size, shuffle=True, collate_fn=tonic.collation.PadTensors())"
      ],
      "metadata": {
        "id": "xRR8oH979neX"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Leaky integrate-and-fire (LIF) neuron\n",
        "\n",
        "In our brains, a neuron might be connected to 1,000 $-$ 10,000 other neurons. If one neuron spikes, all downhill neurons might feel it. But what determines whether a neuron spikes in the first place? The past century of experiments demonstrate that if a neuron experiences *sufficient* stimulus at its input, then it might become excited and fire its own spike. \n",
        "\n",
        "Where does this stimulus come from? It could be from:\n",
        "* the sensory periphery, \n",
        "* an invasive electrode artificially stimulating the neuron, or in most cases,\n",
        "* from other pre-synaptic neurons. \n",
        "\n",
        "<center>\n",
        "<img src='https://github.com/jeshraghian/snntorch/blob/master/docs/_static/img/examples/tutorial2/2_2_intuition.png?raw=true' width=\"500\">\n",
        "</center>\n",
        "\n",
        "# Membrane\n",
        "Like all cells, a neuron is surrounded by a thin membrane. This membrane is a lipid bilayer that insulates the conductive saline solution within the neuron from the extracellular medium. Electrically, the two conductive solutions separated by an insulator act as a capacitor. \n",
        "\n",
        "Another function of this membrane is to control what goes in and out of this cell (e.g., ions such as Na$^+$). The membrane is usually impermeable to ions which blocks them from entering and exiting the neuron body. But there are specific channels in the membrane that are triggered to open by injecting current into the neuron. This charge movement is electrically modelled by a resistor.\n",
        "\n",
        "<center>\n",
        "<img src='https://github.com/jeshraghian/snntorch/blob/master/docs/_static/img/examples/tutorial2/2_3_passivemembrane.png?raw=true' width=\"450\">\n",
        "</center>\n",
        "\n",
        "A large variety of neuron models are out there, ranging from biophysically accurate models (i.e., the Hodgkin-Huxley models) to the extremely simple artificial neuron that pervades all facets of modern deep learning.\n",
        "\n",
        "**Hodgkin-Huxley Neuron Models**$-$While biophysical models can reproduce electrophysiological results with a high degree of accuracy, their complexity makes them difficult to use at present.\n",
        "\n",
        "**Artificial Neuron Model**$-$On the other end of the spectrum is the artificial neuron. The inputs are multiplied by their corresponding weights and passed through an activation function. This simplification has enabled deep learning researchers to perform incredible feats in computer vision, natural language processing, and many other machine learning-domain tasks.\n",
        "\n",
        "**Leaky Integrate-and-Fire Neuron Models**$-$Somewhere in the middle of the divide lies the leaky integrate-and-fire (LIF) neuron model. It takes the sum of weighted inputs, much like the artificial neuron. But rather than passing it directly to an activation function, it will integrate the input over time with a leakage, much like an RC circuit. If the integrated value exceeds a threshold, then the LIF neuron will emit a voltage spike. The LIF neuron abstracts away the shape and profile of the output spike; it is simply treated as a discrete event. As a result, information is not stored within the spike, but rather the timing (or frequency) of spikes. Simple spiking neuron models have produced much insight into the neural code, memory, network dynamics, and more recently, deep learning. The LIF neuron sits in the sweet spot between biological plausibility and practicality. \n",
        "\n",
        "<center>\n",
        "<img src='https://github.com/jeshraghian/snntorch/blob/master/docs/_static/img/examples/tutorial2/2_1_neuronmodels.png?raw=true' width=\"1000\">\n",
        "</center>\n"
      ],
      "metadata": {
        "id": "H1nESpEGnmJb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Spiking mechanism anatomy"
      ],
      "metadata": {
        "id": "hyWNvl4slb8d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create a single spike train\n",
        "Let's start with creation of a dummy spike train of dimension time x batch_size x features"
      ],
      "metadata": {
        "id": "8BaG_9pXScAa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from snntorch import spikegen\n",
        "\n",
        "spk_in = spikegen.rate_conv(torch.rand((25, 784)))\n",
        "spk_in = spk_in.unsqueeze(1)\n",
        "print(f\"Dimensions of spk_in: {spk_in.size()}\")\n",
        "spk_in"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Gx3TYYensGJ",
        "outputId": "cc9de87e-222b-4fa9-8e5f-c1c4e2232bbd"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dimensions of spk_in: torch.Size([25, 1, 784])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[0., 0., 1.,  ..., 0., 0., 0.]],\n",
              "\n",
              "        [[0., 0., 1.,  ..., 0., 0., 0.]],\n",
              "\n",
              "        [[1., 1., 1.,  ..., 1., 1., 0.]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[0., 1., 0.,  ..., 1., 1., 0.]],\n",
              "\n",
              "        [[1., 1., 1.,  ..., 1., 1., 0.]],\n",
              "\n",
              "        [[0., 0., 0.,  ..., 0., 0., 1.]]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Simulate a forward pass\n",
        "\n",
        "Recall that if the membrane exceeds the threshold, then the neuron emits an output spike: \n",
        "\n",
        "$$S[t] = \\begin{cases} 1, &\\text{if}~U[t] > U_{\\rm thr} \\\\\n",
        "0, &\\text{otherwise}\\end{cases} \\tag{0}$$\n",
        "\n",
        "If a spike is triggered, the membrane potential should be reset. The *reset-by-subtraction* mechanism is modeled by:\n",
        "\n",
        "$$U[t+1] = \\underbrace{\\beta U[t]}_\\text{decay} + \\underbrace{WX[t+1]}_\\text{input} - \\underbrace{S[t]U_{\\rm thr}}_\\text{reset} \\tag{1}$$\n",
        "\n",
        "As $W$ is a learnable parameter, and $U_{\\rm thr}$ is often just set to $1$ (though can be tuned), this leaves the decay rate $\\beta$ as the only hyperparameter left to be specified."
      ],
      "metadata": {
        "id": "sQkZpQLiVjob"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import snntorch as snn\n",
        "\n",
        "# Layer parameters\n",
        "num_inputs = 1*28*28 #=784\n",
        "num_hidden = 1000\n",
        "num_outputs = 10\n",
        "beta = 0.99 # membrane decay rate\n",
        "num_steps = 25\n",
        "\n",
        "# Initialize layers\n",
        "linear1 = nn.Linear(num_inputs, num_hidden)\n",
        "lif1 = snn.Leaky(beta=beta)\n",
        "linear2 = nn.Linear(num_hidden, num_outputs)\n",
        "lif2 = snn.Leaky(beta=beta)\n",
        "\n",
        "# Initialize hidden states\n",
        "mem1 = lif1.init_leaky()\n",
        "mem2 = lif2.init_leaky()\n",
        "\n",
        "print(\"Initialized membrane 1:\", mem1)\n",
        "print(\"Initialized membrane 2:\", mem2)\n",
        "\n",
        "# Create empty lists to record outputs\n",
        "mem1_rec = []\n",
        "mem2_rec = []\n",
        "spk1_rec = []\n",
        "spk2_rec = []\n",
        "\n",
        "# Forward pass simulation\n",
        "for step in range(num_steps):\n",
        "    #print(spk_in[0])\n",
        "\n",
        "    post_synaptic_current1 = linear1(spk_in[step]) # post-synaptic current <-- spk_in x weight\n",
        "    #print(post_synaptic_current1)\n",
        "\n",
        "    spk1, mem1 = lif1(post_synaptic_current1, mem1) # mem[t+1] <--post-synaptic current + decayed membrane\n",
        "    #print(spk1)\n",
        "\n",
        "    post_synaptic_current2 = linear2(spk1)\n",
        "    #print(post_synaptic_current2)\n",
        "\n",
        "    spk2, mem2 = lif2(post_synaptic_current2, mem2)\n",
        "    #print(spk2)\n",
        "\n",
        "    mem1_rec.append(mem1)\n",
        "    mem2_rec.append(mem2)\n",
        "    spk1_rec.append(spk1)\n",
        "    spk2_rec.append(spk2)"
      ],
      "metadata": {
        "id": "USJ5TdnDUnM3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54bd87ee-658d-4bbf-81ca-d635555f4bfa"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initialized membrane 1: tensor([])\n",
            "Initialized membrane 2: tensor([])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification output\n",
        "print(\"Membrane potential output\", mem2_rec[8]) # 10 variables corresponding to each class probability\n",
        "print(\"Spiking output\", spk2_rec[8])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iVABTBKCXGSl",
        "outputId": "e330ca8b-f98e-4114-83eb-222aa05d8ab1"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Membrane potential output tensor([[-1.5051,  0.3149, -0.4412, -0.3396,  0.9999,  0.4553, -0.7074, -1.1393,\n",
            "         -0.0717, -0.5748]], grad_fn=<SubBackward0>)\n",
            "Spiking output tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], grad_fn=<HeavisideBackward>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Membrane potential hidden state\n",
        "mem1_rec[24]"
      ],
      "metadata": {
        "id": "wTSXNOIQYl1k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Spiking hidden state\n",
        "spk1_rec[24]"
      ],
      "metadata": {
        "id": "cYFSnNuhXPep"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Simple spiking net definition\n",
        "In this section, we will create a 2-layer fully-connected neural network. Compared to our simulations so far, each neuron will now integrate over many more incoming input spikes."
      ],
      "metadata": {
        "id": "fbLG_XI9ZFHK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define Network\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "\n",
        "        # Initialize layers\n",
        "        self.linear1 = nn.Linear(num_inputs, num_hidden)\n",
        "        self.lif1 = snn.Leaky(beta=beta)\n",
        "        self.linear2 = nn.Linear(num_hidden, num_outputs)\n",
        "        self.lif2 = snn.Leaky(beta=beta)\n",
        "\n",
        "    def forward(self, x):\n",
        "\n",
        "        # Initialize hidden states at t=0\n",
        "        mem1 = self.lif1.init_leaky()\n",
        "        mem2 = self.lif2.init_leaky()\n",
        "        \n",
        "        # Record the final layer\n",
        "        spk2_rec = []\n",
        "        mem2_rec = []\n",
        "\n",
        "        for step in range(num_steps):\n",
        "            cur1 = self.linear1(x[step])\n",
        "            spk1, mem1 = self.lif1(cur1, mem1)\n",
        "            cur2 = self.linear2(spk1)\n",
        "            spk2, mem2 = self.lif2(cur2, mem2)\n",
        "\n",
        "            # record outputs\n",
        "            spk2_rec.append(spk2)\n",
        "            mem2_rec.append(mem2)\n",
        "\n",
        "        return torch.stack(spk2_rec, dim=0), torch.stack(mem2_rec, dim=0)\n",
        "        \n",
        "# Load the network onto CUDA if available\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "net = Net().to(device)"
      ],
      "metadata": {
        "id": "h76pAHSIZKHb"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform a single forward pass\n",
        "net.train()\n",
        "spk_rec, mem_rec = net(spk_in)\n",
        "\n",
        "print(spk_rec[24])\n",
        "print(mem_rec[24])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jBagolTKsdqc",
        "outputId": "2934285f-7bf7-4d7f-ea30-fcd96d361fc2"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0., 1., 0., 0., 1., 0., 0., 0., 0., 0.]], grad_fn=<SelectBackward0>)\n",
            "tensor([[-2.8468,  1.1411, -1.1699, -1.4822,  1.0962, -0.8985, -2.0594, -2.9814,\n",
            "          0.5819,  0.0294]], grad_fn=<SelectBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Calculate the loss using membare potential"
      ],
      "metadata": {
        "id": "ncyThyAbtMLx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the loss object\n",
        "loss = nn.CrossEntropyLoss()\n",
        "\n",
        "# Initialize the total loss value\n",
        "loss_val = torch.zeros((1), dtype=torch.float, device=device)\n",
        "\n",
        "# Fake the target to calculate the loss\n",
        "target = torch.tensor([1])\n",
        "\n",
        "# Sum the loss at every step\n",
        "for step in range(num_steps):\n",
        "  loss_val += loss(mem_rec[step], target)\n",
        "\n",
        "print(f\"Training loss: {loss_val.item():.3f}\")"
      ],
      "metadata": {
        "id": "rfGu77XD0vuI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "041f0cd4-1f1e-451a-a54a-681d575fbbc7"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 40.715\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Calculate the loss using spikes\n",
        "The approach below uses the Mean Square Error Spike Count Loss, which aims to elicit spikes from the correct class 80% of the time, and 20% of the time from incorrect classes. Encouraging incorrect neurons to fire could be motivated to avoid dead neurons."
      ],
      "metadata": {
        "id": "QS_0fJaZwUx0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from snntorch import functional as SF\n",
        "\n",
        "# Initialize the loss object\n",
        "loss_fn = SF.mse_count_loss(correct_rate=0.8, incorrect_rate=0.2)\n",
        "\n",
        "# Initialize the total loss value\n",
        "loss_val = torch.zeros((1), dtype=torch.float, device=device)\n",
        "\n",
        "# Fake the target to calculate the loss\n",
        "target = torch.tensor([1])\n",
        "\n",
        "# Sum the loss at every step\n",
        "for step in range(num_steps):\n",
        "  loss_val += loss(spk_rec[step], target)\n",
        "\n",
        "print(f\"Training loss: {loss_val.item():.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VkSsVJ4-wSAT",
        "outputId": "a355a300-43ad-4cd6-dce9-fb18cecc8eed"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 55.402\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train a simple spiking neural network (SNN)\n",
        "\n",
        "By default, PyTorch’s autodifferentiation mechanism in `torch.autograd` nulls the gradient signal of the spiking neuron graph due to non-differentiable spiking threshold functions. snntorch overrides the default gradient by using `snntorch.neurons.Heaviside`.\n",
        "\n",
        "Alternative gradients are also available in the `snntorch.surrogate` module. These represent either approximations of the backward pass or probabilistic models of firing as a function of the membrane potential.\n",
        "\n",
        "\n",
        "<center>\n",
        "<img src='https://tonic.readthedocs.io/en/latest/_images/surrogates.png?raw=true' width=\"450\">\n",
        "</center>\n",
        "\n",
        "The surrogate gradient must be passed as the `spike_grad` argument to the neuron model. If `spike_grad` is left unspecified, it defaults to `snntorch.neurons.Heaviside`.\n",
        "\n",
        "One of the surrogate gradients of the Heaviside step function is [`fast_sigmoid`](https://snntorch.readthedocs.io/en/latest/snntorch.surrogate.html#snntorch.surrogate.FastSigmoid).\n",
        " \n",
        "The slope defaults to 25, but can be modified by calling `surrogate.fast_sigmoid(slope=25)`."
      ],
      "metadata": {
        "id": "h_nbwn9g8Sqm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data shape reminder\n",
        "inputs, targets = next(iter(trainloader))\n",
        "print(inputs.shape)\n",
        "print(targets)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7m4bwlqN4FD",
        "outputId": "407b381b-c418-47bb-acd0-762718717c21"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([128, 313, 2, 34, 34])\n",
            "tensor([7, 6, 5, 4, 1, 6, 9, 5, 5, 3, 1, 9, 2, 0, 8, 2, 8, 6, 4, 3, 8, 8, 1, 8,\n",
            "        4, 7, 1, 1, 6, 1, 7, 3, 8, 4, 3, 2, 1, 3, 2, 3, 6, 8, 1, 8, 6, 7, 8, 8,\n",
            "        4, 2, 9, 3, 4, 5, 6, 8, 2, 5, 4, 3, 5, 4, 1, 4, 9, 6, 1, 8, 3, 5, 7, 7,\n",
            "        9, 9, 1, 1, 1, 2, 5, 3, 1, 7, 5, 0, 3, 6, 4, 2, 9, 0, 6, 5, 5, 6, 1, 7,\n",
            "        4, 7, 1, 7, 3, 0, 3, 5, 6, 9, 4, 0, 4, 2, 6, 2, 2, 2, 1, 4, 3, 2, 3, 4,\n",
            "        0, 2, 8, 5, 5, 1, 4, 0])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Size of the N-MNIST\n",
        "len(trainloader) # 469 batches of 128 images each = 60 032 images"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tS1e8HokfdgQ",
        "outputId": "c2abf36a-b341-43b3-fc49-884e5c611d5d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "469"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SNN input shape \n",
        "inputs[:, 0].view(batch_size, -1).shape # one time step from 311 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2mX_Y8g2Owr9",
        "outputId": "37b6a6df-3c84-4efa-94a7-0a07496c9eb7"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([128, 2312])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the SNN architecture\n",
        " \n",
        "from snntorch import surrogate\n",
        "\n",
        "num_inputs = 2*34*34\n",
        "num_hidden = 1000\n",
        "num_outputs = 10\n",
        "\n",
        "# neuron and simulation parameters\n",
        "spike_grad = surrogate.fast_sigmoid(slope=75)\n",
        "beta = 0.5\n",
        "\n",
        "#  Initialize Network using Sequential API\n",
        "sn_net = nn.Sequential(\n",
        "                    nn.Linear(num_inputs, num_hidden),\n",
        "                    snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),\n",
        "                    nn.Linear(num_hidden, num_outputs),\n",
        "                    snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True)\n",
        "                    ).to(device)\n",
        "\n",
        "def forward_pass(sn_net, data):  \n",
        "  spk_rec = []\n",
        "  utils.reset(sn_net)  # resets hidden states for all LIF neurons in sn_net\n",
        "\n",
        "  for step in range(data.size(1)):  # data.size(1) = number of time steps\n",
        "        spk_out, mem_out = sn_net(data[:, step].view(batch_size, -1))\n",
        "        spk_rec.append(spk_out)\n",
        "  \n",
        "  return torch.stack(spk_rec)"
      ],
      "metadata": {
        "id": "Lph4l9Xx8kAe"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Params\n",
        "\n",
        "optimizer = torch.optim.Adam(sn_net.parameters(), lr=2e-2, betas=(0.9, 0.999))\n",
        "loss_fn = SF.mse_count_loss(correct_rate=0.8, incorrect_rate=0.2)\n",
        "\n",
        "num_epochs = 1\n",
        "num_iters = 100 # Only 100 batches of data will be treated: 128x100 = 1280 images\n",
        "\n",
        "loss_hist = []\n",
        "acc_hist = []\n",
        "\n",
        "# Training loop\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (data, targets) in enumerate(iter(trainloader)):\n",
        "        \n",
        "        # Send the data to device\n",
        "        data = data.to(device)\n",
        "        targets = targets.to(device)\n",
        "        print(\"Input shape\", data.shape)\n",
        "\n",
        "        # Put the model into a train mode (gradints will be calculated)\n",
        "        sn_net.train()\n",
        "\n",
        "        # Perform a forward pass\n",
        "        spk_rec = forward_pass(sn_net, data)\n",
        "        print(\"Output\", spk_rec.shape)\n",
        "\n",
        "        # Calculate the loss over entire batch\n",
        "        loss_val = loss_fn(spk_rec, targets)\n",
        "\n",
        "        # Clear previously stored gradients\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # Calculate gradients\n",
        "        loss_val.backward()\n",
        "\n",
        "        # Update the weights\n",
        "        optimizer.step()\n",
        "\n",
        "        # Store loss history for future plotting\n",
        "        loss_hist.append(loss_val.item())\n",
        " \n",
        "        print(f\"Epoch {epoch}, Iteration {i} \\nTrain Loss: {loss_val.item():.2f}\")\n",
        "\n",
        "        acc = SF.accuracy_rate(spk_rec, targets) \n",
        "        acc_hist.append(acc)\n",
        "        \n",
        "        print(f\"Accuracy: {acc * 100:.2f}%\\n\")\n",
        "\n",
        "        # This will end training after 50 iterations by default\n",
        "        if i == num_iters:\n",
        "          break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9rpAub6_9qSa",
        "outputId": "d40bf921-8aaf-489b-f1f8-1c8e3cbf4291"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 0 \n",
            "Train Loss: 31.00\n",
            "Accuracy: 5.47%\n",
            "\n",
            "torch.Size([128, 309, 2, 34, 34])\n",
            "Epoch 0, Iteration 1 \n",
            "Train Loss: 30.58\n",
            "Accuracy: 8.59%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 2 \n",
            "Train Loss: 30.97\n",
            "Accuracy: 11.72%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 3 \n",
            "Train Loss: 17.34\n",
            "Accuracy: 8.59%\n",
            "\n",
            "torch.Size([128, 313, 2, 34, 34])\n",
            "Epoch 0, Iteration 4 \n",
            "Train Loss: 17.35\n",
            "Accuracy: 12.50%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 5 \n",
            "Train Loss: 18.83\n",
            "Accuracy: 8.59%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 6 \n",
            "Train Loss: 15.01\n",
            "Accuracy: 10.94%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 7 \n",
            "Train Loss: 19.40\n",
            "Accuracy: 28.91%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 8 \n",
            "Train Loss: 13.44\n",
            "Accuracy: 9.38%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 9 \n",
            "Train Loss: 14.24\n",
            "Accuracy: 10.94%\n",
            "\n",
            "torch.Size([128, 312, 2, 34, 34])\n",
            "Epoch 0, Iteration 10 \n",
            "Train Loss: 12.15\n",
            "Accuracy: 22.66%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 11 \n",
            "Train Loss: 12.53\n",
            "Accuracy: 21.88%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 12 \n",
            "Train Loss: 10.96\n",
            "Accuracy: 25.00%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 13 \n",
            "Train Loss: 10.46\n",
            "Accuracy: 21.88%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 14 \n",
            "Train Loss: 10.54\n",
            "Accuracy: 34.38%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 15 \n",
            "Train Loss: 9.73\n",
            "Accuracy: 32.81%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 16 \n",
            "Train Loss: 9.55\n",
            "Accuracy: 35.16%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 17 \n",
            "Train Loss: 9.34\n",
            "Accuracy: 39.84%\n",
            "\n",
            "torch.Size([128, 312, 2, 34, 34])\n",
            "Epoch 0, Iteration 18 \n",
            "Train Loss: 9.20\n",
            "Accuracy: 38.28%\n",
            "\n",
            "torch.Size([128, 313, 2, 34, 34])\n",
            "Epoch 0, Iteration 19 \n",
            "Train Loss: 8.86\n",
            "Accuracy: 50.00%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 20 \n",
            "Train Loss: 8.65\n",
            "Accuracy: 46.09%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 21 \n",
            "Train Loss: 8.36\n",
            "Accuracy: 60.16%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 22 \n",
            "Train Loss: 7.75\n",
            "Accuracy: 61.72%\n",
            "\n",
            "torch.Size([128, 312, 2, 34, 34])\n",
            "Epoch 0, Iteration 23 \n",
            "Train Loss: 8.22\n",
            "Accuracy: 62.50%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 24 \n",
            "Train Loss: 7.59\n",
            "Accuracy: 66.41%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 25 \n",
            "Train Loss: 7.60\n",
            "Accuracy: 67.19%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 26 \n",
            "Train Loss: 7.54\n",
            "Accuracy: 63.28%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 27 \n",
            "Train Loss: 7.54\n",
            "Accuracy: 66.41%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 28 \n",
            "Train Loss: 7.04\n",
            "Accuracy: 73.44%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 29 \n",
            "Train Loss: 6.72\n",
            "Accuracy: 75.00%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 30 \n",
            "Train Loss: 6.64\n",
            "Accuracy: 68.75%\n",
            "\n",
            "torch.Size([128, 309, 2, 34, 34])\n",
            "Epoch 0, Iteration 31 \n",
            "Train Loss: 6.51\n",
            "Accuracy: 69.53%\n",
            "\n",
            "torch.Size([128, 312, 2, 34, 34])\n",
            "Epoch 0, Iteration 32 \n",
            "Train Loss: 7.00\n",
            "Accuracy: 65.62%\n",
            "\n",
            "torch.Size([128, 309, 2, 34, 34])\n",
            "Epoch 0, Iteration 33 \n",
            "Train Loss: 6.22\n",
            "Accuracy: 71.09%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 34 \n",
            "Train Loss: 5.87\n",
            "Accuracy: 76.56%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 35 \n",
            "Train Loss: 6.28\n",
            "Accuracy: 76.56%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 36 \n",
            "Train Loss: 5.61\n",
            "Accuracy: 76.56%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 37 \n",
            "Train Loss: 5.57\n",
            "Accuracy: 81.25%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 38 \n",
            "Train Loss: 5.88\n",
            "Accuracy: 78.91%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 39 \n",
            "Train Loss: 6.03\n",
            "Accuracy: 74.22%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 40 \n",
            "Train Loss: 6.04\n",
            "Accuracy: 75.78%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 41 \n",
            "Train Loss: 6.21\n",
            "Accuracy: 72.66%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 42 \n",
            "Train Loss: 5.75\n",
            "Accuracy: 81.25%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 43 \n",
            "Train Loss: 5.89\n",
            "Accuracy: 80.47%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 44 \n",
            "Train Loss: 5.02\n",
            "Accuracy: 89.06%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 45 \n",
            "Train Loss: 5.71\n",
            "Accuracy: 71.88%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 46 \n",
            "Train Loss: 5.14\n",
            "Accuracy: 78.12%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 47 \n",
            "Train Loss: 5.07\n",
            "Accuracy: 77.34%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 48 \n",
            "Train Loss: 5.47\n",
            "Accuracy: 75.78%\n",
            "\n",
            "torch.Size([128, 309, 2, 34, 34])\n",
            "Epoch 0, Iteration 49 \n",
            "Train Loss: 5.63\n",
            "Accuracy: 74.22%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 50 \n",
            "Train Loss: 4.98\n",
            "Accuracy: 83.59%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 51 \n",
            "Train Loss: 5.20\n",
            "Accuracy: 82.81%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 52 \n",
            "Train Loss: 4.91\n",
            "Accuracy: 82.81%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 53 \n",
            "Train Loss: 5.06\n",
            "Accuracy: 75.78%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 54 \n",
            "Train Loss: 4.58\n",
            "Accuracy: 80.47%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 55 \n",
            "Train Loss: 4.71\n",
            "Accuracy: 85.94%\n",
            "\n",
            "torch.Size([128, 312, 2, 34, 34])\n",
            "Epoch 0, Iteration 56 \n",
            "Train Loss: 4.89\n",
            "Accuracy: 80.47%\n",
            "\n",
            "torch.Size([128, 312, 2, 34, 34])\n",
            "Epoch 0, Iteration 57 \n",
            "Train Loss: 5.01\n",
            "Accuracy: 82.81%\n",
            "\n",
            "torch.Size([128, 309, 2, 34, 34])\n",
            "Epoch 0, Iteration 58 \n",
            "Train Loss: 4.57\n",
            "Accuracy: 87.50%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 59 \n",
            "Train Loss: 4.63\n",
            "Accuracy: 87.50%\n",
            "\n",
            "torch.Size([128, 312, 2, 34, 34])\n",
            "Epoch 0, Iteration 60 \n",
            "Train Loss: 4.65\n",
            "Accuracy: 85.16%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 61 \n",
            "Train Loss: 4.76\n",
            "Accuracy: 81.25%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 62 \n",
            "Train Loss: 4.20\n",
            "Accuracy: 89.06%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 63 \n",
            "Train Loss: 4.37\n",
            "Accuracy: 84.38%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 64 \n",
            "Train Loss: 4.79\n",
            "Accuracy: 80.47%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 65 \n",
            "Train Loss: 4.38\n",
            "Accuracy: 83.59%\n",
            "\n",
            "torch.Size([128, 309, 2, 34, 34])\n",
            "Epoch 0, Iteration 66 \n",
            "Train Loss: 4.68\n",
            "Accuracy: 84.38%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 67 \n",
            "Train Loss: 4.57\n",
            "Accuracy: 81.25%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 68 \n",
            "Train Loss: 4.53\n",
            "Accuracy: 81.25%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 69 \n",
            "Train Loss: 4.20\n",
            "Accuracy: 90.62%\n",
            "\n",
            "torch.Size([128, 312, 2, 34, 34])\n",
            "Epoch 0, Iteration 70 \n",
            "Train Loss: 4.09\n",
            "Accuracy: 87.50%\n",
            "\n",
            "torch.Size([128, 312, 2, 34, 34])\n",
            "Epoch 0, Iteration 71 \n",
            "Train Loss: 4.44\n",
            "Accuracy: 82.81%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 72 \n",
            "Train Loss: 4.20\n",
            "Accuracy: 86.72%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 73 \n",
            "Train Loss: 4.41\n",
            "Accuracy: 85.16%\n",
            "\n",
            "torch.Size([128, 309, 2, 34, 34])\n",
            "Epoch 0, Iteration 74 \n",
            "Train Loss: 4.16\n",
            "Accuracy: 87.50%\n",
            "\n",
            "torch.Size([128, 309, 2, 34, 34])\n",
            "Epoch 0, Iteration 75 \n",
            "Train Loss: 4.72\n",
            "Accuracy: 82.81%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 76 \n",
            "Train Loss: 3.91\n",
            "Accuracy: 85.94%\n",
            "\n",
            "torch.Size([128, 312, 2, 34, 34])\n",
            "Epoch 0, Iteration 77 \n",
            "Train Loss: 4.28\n",
            "Accuracy: 85.94%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 78 \n",
            "Train Loss: 4.00\n",
            "Accuracy: 89.84%\n",
            "\n",
            "torch.Size([128, 313, 2, 34, 34])\n",
            "Epoch 0, Iteration 79 \n",
            "Train Loss: 4.31\n",
            "Accuracy: 89.06%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 80 \n",
            "Train Loss: 3.93\n",
            "Accuracy: 87.50%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 81 \n",
            "Train Loss: 4.23\n",
            "Accuracy: 90.62%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 82 \n",
            "Train Loss: 3.82\n",
            "Accuracy: 88.28%\n",
            "\n",
            "torch.Size([128, 314, 2, 34, 34])\n",
            "Epoch 0, Iteration 83 \n",
            "Train Loss: 4.32\n",
            "Accuracy: 85.94%\n",
            "\n",
            "torch.Size([128, 309, 2, 34, 34])\n",
            "Epoch 0, Iteration 84 \n",
            "Train Loss: 4.37\n",
            "Accuracy: 82.81%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 85 \n",
            "Train Loss: 3.68\n",
            "Accuracy: 92.97%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 86 \n",
            "Train Loss: 3.90\n",
            "Accuracy: 85.94%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 87 \n",
            "Train Loss: 3.86\n",
            "Accuracy: 85.94%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 88 \n",
            "Train Loss: 3.92\n",
            "Accuracy: 87.50%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 89 \n",
            "Train Loss: 4.07\n",
            "Accuracy: 82.81%\n",
            "\n",
            "torch.Size([128, 309, 2, 34, 34])\n",
            "Epoch 0, Iteration 90 \n",
            "Train Loss: 3.61\n",
            "Accuracy: 87.50%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 91 \n",
            "Train Loss: 3.89\n",
            "Accuracy: 84.38%\n",
            "\n",
            "torch.Size([128, 313, 2, 34, 34])\n",
            "Epoch 0, Iteration 92 \n",
            "Train Loss: 4.22\n",
            "Accuracy: 89.84%\n",
            "\n",
            "torch.Size([128, 311, 2, 34, 34])\n",
            "Epoch 0, Iteration 93 \n",
            "Train Loss: 3.71\n",
            "Accuracy: 90.62%\n",
            "\n",
            "torch.Size([128, 312, 2, 34, 34])\n",
            "Epoch 0, Iteration 94 \n",
            "Train Loss: 3.79\n",
            "Accuracy: 88.28%\n",
            "\n",
            "torch.Size([128, 314, 2, 34, 34])\n",
            "Epoch 0, Iteration 95 \n",
            "Train Loss: 3.81\n",
            "Accuracy: 87.50%\n",
            "\n",
            "torch.Size([128, 312, 2, 34, 34])\n",
            "Epoch 0, Iteration 96 \n",
            "Train Loss: 3.54\n",
            "Accuracy: 90.62%\n",
            "\n",
            "torch.Size([128, 312, 2, 34, 34])\n",
            "Epoch 0, Iteration 97 \n",
            "Train Loss: 3.40\n",
            "Accuracy: 92.19%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 98 \n",
            "Train Loss: 3.27\n",
            "Accuracy: 92.97%\n",
            "\n",
            "torch.Size([128, 309, 2, 34, 34])\n",
            "Epoch 0, Iteration 99 \n",
            "Train Loss: 4.20\n",
            "Accuracy: 87.50%\n",
            "\n",
            "torch.Size([128, 310, 2, 34, 34])\n",
            "Epoch 0, Iteration 100 \n",
            "Train Loss: 3.63\n",
            "Accuracy: 89.84%\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import snntorch.spikeplot as splt\n",
        "\n",
        "# Plot the loss\n",
        "fig = plt.figure(facecolor=\"w\")\n",
        "plt.plot(loss_hist)\n",
        "plt.title(\"Train Set Loss\")\n",
        "plt.xlabel(\"Iteration\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "rYZiRWtta4de",
        "outputId": "b816ec8b-9e3d-4442-8cfe-15a973ce86f6"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot accuracy\n",
        "fig = plt.figure(facecolor=\"w\")\n",
        "plt.plot(acc_hist)\n",
        "plt.title(\"Train Set Accuracy\")\n",
        "plt.xlabel(\"Iteration\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "6u0PwyZ-aXUc",
        "outputId": "05ac160d-98ea-47bd-aea6-e4dbbe54f449"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import HTML\n",
        "\n",
        "idx = 0\n",
        "\n",
        "fig, ax = plt.subplots(facecolor='w', figsize=(12, 7))\n",
        "labels=['0', '1', '2', '3', '4', '5', '6', '7', '8','9']\n",
        "print(f\"The target label is: {targets[idx]}\")\n",
        "\n",
        "# Plot spike count histogram\n",
        "anim = splt.spike_count(spk_rec[:, idx].detach().cpu(), fig, ax, labels=labels, \n",
        "                        animate=True, interpolate=1)\n",
        "\n",
        "HTML(anim.to_html5_video())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 992
        },
        "id": "2lPbyefObfa3",
        "outputId": "db7243b5-1bbd-4cef-ba6e-a68065435db0"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The target label is: 1\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<video width=\"864\" height=\"504\" controls autoplay loop>\n",
              "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAACw/G1kYXQAAAKuBgX//6rcRem9\n",
              "5tlIt5Ys2CDZI+7veDI2NCAtIGNvcmUgMTUyIHIyODU0IGU5YTU5MDMgLSBILjI2NC9NUEVHLTQg\n",
              "QVZDIGNvZGVjIC0gQ29weWxlZnQgMjAwMy0yMDE3IC0gaHR0cDovL3d3dy52aWRlb2xhbi5vcmcv\n",
              "eDI2NC5odG1sIC0gb3B0aW9uczogY2FiYWM9MSByZWY9MyBkZWJsb2NrPTE6MDowIGFuYWx5c2U9\n",
              "MHgzOjB4MTEzIG1lPWhleCBzdWJtZT03IHBzeT0xIHBzeV9yZD0xLjAwOjAuMDAgbWl4ZWRfcmVm\n",
              "PTEgbWVfcmFuZ2U9MTYgY2hyb21hX21lPTEgdHJlbGxpcz0xIDh4OGRjdD0xIGNxbT0wIGRlYWR6\n",
              "b25lPTIxLDExIGZhc3RfcHNraXA9MSBjaHJvbWFfcXBfb2Zmc2V0PS0yIHRocmVhZHM9MyBsb29r\n",
              "YWhlYWRfdGhyZWFkcz0xIHNsaWNlZF90aHJlYWRzPTAgbnI9MCBkZWNpbWF0ZT0xIGludGVybGFj\n",
              "ZWQ9MCBibHVyYXlfY29tcGF0PTAgY29uc3RyYWluZWRfaW50cmE9MCBiZnJhbWVzPTMgYl9weXJh\n",
              "bWlkPTIgYl9hZGFwdD0xIGJfYmlhcz0wIGRpcmVjdD0xIHdlaWdodGI9MSBvcGVuX2dvcD0wIHdl\n",
              "aWdodHA9MiBrZXlpbnQ9MjUwIGtleWludF9taW49MjUgc2NlbmVjdXQ9NDAgaW50cmFfcmVmcmVz\n",
              "aD0wIHJjX2xvb2thaGVhZD00MCByYz1jcmYgbWJ0cmVlPTEgY3JmPTIzLjAgcWNvbXA9MC42MCBx\n",
              "cG1pbj0wIHFwbWF4PTY5IHFwc3RlcD00IGlwX3JhdGlvPTEuNDAgYXE9MToxLjAwAIAAAA2wZYiE\n",
              "AC///vau/MsrRwuVLh1Ze7NR8uhJcv2IMH1oAAADAAADAAADAAEPmDjyj2JJkegAAAMAAnQAaoSh\n",
              "wr4HIoEyXk+UD+ecIrQavLhNEs1hBGNNqXdwFibFof09UdJCq6av2qZkoDqsWFSV2HwwZWPJQygc\n",
              "n5BCiFVCilTRSMjR68mxszRgciCdWq3bebEiY4ZhsHSwONdpiRBNCBpOIOHVld1diNG8RL4lfCte\n",
              "2TukJ67sSVZytTziaTuesXsyEaBqx/3Lwa1p2VwGShlbd5HUlvzsqmAUYUDWUp7wpKXaJxgMXid+\n",
              "aW7jd1Pgw6NFKODGFg9HHaAa9Sc0gOrz5PqwQZkOzpjWbnKAJM4pyEmTave7NPwyPc1v8lAivBb5\n",
              "fmMjTGRDVrWGS2BeIADmu8zPzF8oph1RRazpOO9AG2DypQJWWqdgxAa2D0eXgH6E53jS73Hm1j8L\n",
              "/T6QfYmDPwijAoQzx9acvfhJ+lazCj86NWA28TMRet8XxLmFfqBQW0wkq/yPePFUFJBGnGk9VsY+\n",
              "/UUeIovmz5mw/yUSIEfUqwg67w9nJN69uI3TAQRU5+rETmeMPBig+WSzDATohaBV2OKjd1LaFr4Y\n",
              "tdncfNMkFbzzgXkRm02cZvWKAWuYClYHrwrrWSCFO0erqxxCUBUaowq+jnuEvB0LvJbcEUtcuDzI\n",
              "wNHLjYemlL1GgP9lIV8kBCCBgXUbysW2BrrEax+Hoh/gJeLe/uzLhTnxt2FlNYFyXEvJgwgaOF43\n",
              "6VDADxHkLhDmY4GBYL5fW18SXdWl5CMxWo4/30Fq0y1Tg2nNt+52D+O96CsHeHD4m8QtizsA4UL9\n",
              "1/ivXDZUntJJBDjE/fbZSa/F0Ci8RPRr5uareSurWbxExDPF+R1TsnRgEbWyfsrBfSit23jZjVCo\n",
              "DlqNz8aimeUTvo9uf7LZXLgbTw/aaQArD3jQAAixIPPUq+tIt3R2Lbq2cxsaFTCW/de2sVkS/vB3\n",
              "TgwnlvGVNGhvUgDi75l/NYS5QgN4vSlIrjQ8fFZP0TWC8NdxNWCzZaXwS42ec1TNki7N8AAJP8CK\n",
              "wmdB9DfOOH0vj/bcjkneBTtSXl07V8TjB5gVgbs43MeAdNf77yyRfMuy3fkDcum3LTydx8VSTmWi\n",
              "TruQWJ7dWR17QXwOT5Lwh+ys6clgHwWapnOUszdcjN2l7RMf4M6T2zDgqRGwUFa2/kxUc2eE+nJh\n",
              "1vp0f+4nF7TKJP0sTNAABoeKQ2zU7S/Tveb3dLvVeWSBThF3a7TFMDITkyYDIg6nFVC1oST+tvPl\n",
              "IrJdu0obKvzVJxpmK2Bj1cs4/xrX8hshFkqXRoj2AyXEaByKC2lycW2d1HFMnyJGhAE9e77iuu8b\n",
              "pbH9Kg/n9WsVyTAqFVoFzHSPW6pzJuTDbh2qrgCKUYALXH7t7CtaN13vue2fEX9ilafCpM8B1xK0\n",
              "uEWgzjilVwXA5QXPmYPkbGtk3cKv3b+p6ZpYCqbJKdqQmM5kq9vaSLF1lUY4q6OuJPVwDL2H6UFa\n",
              "oRbynvLSHTyoEc8O431KB+0KSIEeg+sSL+z+abLg1o8NwacAZI6vucyZ68Dr7zpGhsdS2S9tamY6\n",
              "1vqXSkvt5ztJaFJ+5WcAFgnlOBlOnyIivFnVZSvgU/GtysaBsr2ldqC211SvJPkD8xxdCC7pwgOu\n",
              "dgKbCABDMPNQ0Ot9S+xcKUenW5C6KVgB34OxfDpXtjpuV9C3AGEkJoCK+oL9j3Xd0ROZsCff13Ux\n",
              "AVavszA5Ytzx6jiOBurt52c7V1aN6LFxzxmfNvJVkZVgwVBand1PWGDYU2rnCkAY974YAAG6M7/t\n",
              "D7/dagzgAO0r9+Rs5I5QLnuIcDQMwkMEbiADNaBuAynOPuRGhvX2e8GDPoNoBCc2p2UgZy6gNucd\n",
              "9j+ea9sKMWbdzxgKaMhztBSaEBWxjElf1hXL0WPUqKwjzuuJLMmSzTMdfMOrAkvfMqH9JFa8Wd3j\n",
              "9POKqOaS9/a0YyUIeAALpQB35DDmNSAPR7Z4CjVjUy2xb6hlMmSbDTK2QNLtBp4Fqmsch4vrgPG6\n",
              "1MSBSimPR2TuASbXVhovtdfnxb0TRu/YA/5vW82xOsDIDCbnm9iO0LkXe73c0Gyp6ptHIwkt/Lmt\n",
              "BrQQGLtrHEchkcW0eIdPrpjYzhYDAAdiOARg3TWZ++AmYGOk8wKMEFk3EJMbEESVr8qyADF37K5f\n",
              "YG8pA0StwfNBfaI8Uc4B25LfuoYt8ZkYfemRKXVqy3AWHoExcgA/fp34+JB2LlBJHPwg2FIyABy7\n",
              "QDdufOPDeMBTxTDSN5EKI2K4iu7bkBtETeig5fBc/dtEY2xz5fggm17xYkQ7iwe3t+AAAIJACEyv\n",
              "2pYxqLAnIKxKELMNd6WIqg1UF15wiArwLMGtnc5F67tSkVE+HNch+c71/r94GEtw60VP1wMC/9Bb\n",
              "gUyaLCwWkjjvW49QFqIUx0OeHDevXQQckNL0YpgyIiBUpiNanw7YV9V0oY29RgiB6T8oLm4YtqN8\n",
              "argAPDpT0QmYWCQgFz+rpiLCMXW2heN4XeHNMraUv4uwTNxfGI0SOByoz7/E8lLaZ1mwpYVNp8JO\n",
              "1IJ6ylSRhPbKhwOO7TPWwKG1o1NtcJVUrqx/laf+ggeFgHl7myJCL4jltZQpvErrJV+BmlEliiOK\n",
              "hf10X+w4YVXfRXNlNbhFLm3Ir+Ah3b0nhxT0SR+dORru1xZm1ciie1P3PWv/URy6wdxC/Q+TPlkr\n",
              "t+bcN/CNhEycPsIghadJ6JduYjwG+TqljClMo6RNPV1ElHihst7ty6GxwLK6PARqLhmRZ/FsyKwf\n",
              "3j4Xk1ZkhLElHuWvgILy56OPGwbxw80e4+mTc+PuOglfqT56+DROpiqHOpp4Gv6MhSTzFXsqmKCQ\n",
              "nmdAMQ+cI1nzZAPtpxymodvh58aan0p+MHYMcVEODFMXLPVXg2CORd6kCTW6BXHJtMy197zje1ds\n",
              "l6rDMlqCgAcRXesnsTR5ItQ6/NwJPETRE02ZmyOvnzg5RFjGkFRE1HEml1hg8aFEJ5fVRSzZtpsI\n",
              "aj5tEFae829wLBifEjCUk03yCG06a10Liu8k5GNPnSZhRkkl86WXgH6kBBkCTvhPVdE9ht9D/K91\n",
              "XwCSuK/uyhNiDhEDDcDftIdIoHjbVu83JtdatC5vwjkKU0720HMXC1mSU5Z7+UJS9T6DTfpFL07K\n",
              "coH6tK872y6rAAWv6p/yHGCWjQ5FNsl1A2obl/lN29ihzUi8cOoqEb1pIeOO3R1ymg+4+xHNqb+w\n",
              "g+Ink92PzcCUijgaiRZpXXgYePy8pOZyLvoirHJKZu3aXbCAQKHNwVT1jHc5V93GkqgCOmAofuZe\n",
              "mU21Mx9NnIQmFqK0EURfLzDZWKT8toBNpTYJVnaDt1bqFRtEiUAxq4sOHTgJ8OQHplmOxjaRlCB6\n",
              "qt0dGuhtDgScH/v1nq7WbMB6hrGoec4jnEr+fZV7ShrHxej6+xPpLfzffeS3/0m/Ebqf3tlIzRAr\n",
              "FOVfDTJbDs+a1ubWKu143EDIHR1ZZGRoJCYQu+nHWOH+8/xPe/pgnlEaKkQvHCRC2u0b6L8Qpb/L\n",
              "M4g9ivTn0ALnzQaNsNauqoP66m28KWHG4ifKo4ymVqdZ9a8xs07Js79hs8nHUsJY5bKsdaQURW1s\n",
              "ZvrISEGkFtZn5hi63Wb1ndP1WPYR6QZq+n8ld+Bv1uuyATxZl5XxXDXhPRAiTPbhMNyK+g00mJTp\n",
              "L7tY177heVIQ9AYL2Z7AV8vi/dUpTO4Z0yjMA6/DhoBeTejiJdc0vj6KV7zsfCBN1iazx6itPlMH\n",
              "9sTiVcRaS1Hh86nc5aHaE5BmE1/nmNGSCBmkTOABX9WKFnD7S5AsiM1fSlDsqaL+O9aBIGoyeCzf\n",
              "laKZ3RLaJgYHUyIuBHZRuI4VitVq1U+Z/zj3I0fe4coItNcrg/1zJgLHZWzAHqyesf1dmQ5BgfAy\n",
              "4s8Db+WOGErwG46L0aAkE3uWZFDgVJ73yMg2XtDYU6rlHThBPTkP79K9vbrvcefgfRcP+793XJhp\n",
              "QxhL/MsbX4a+qvLPDb0ioKiEsYfTTiD0TUEBCOPMisnLYpkBkAAGrHU97u152PpwWBy9CE+AARgK\n",
              "Yo8qS2nCd0lSk2/inGo6H7G4RQHInI3mm94KnafLG8OjfGljEvW3vtdJW5nZEAaASW+M69u0S8BT\n",
              "Mhaj8bEbEQw/trS5fdGzZ4EZCyKb4skGocoFwujHDHoNDGcS+H9Kp3nJX7qBNymDJYFtQ0v3w4cd\n",
              "ucXebL0675M1CuHor3TG2tNkaQECPt0r/aPZ/qI9HolKthMb4j2m6q0/A2ab2SIzYzv+NcFxG6nL\n",
              "dKAeiIAXfRKlQpyhrbDuG4+0ACy5Pjm5ZFfoSxS6Dojs1miLUbIEtWeueAsZCezB2GTUVt5peFqV\n",
              "DYXMFhjf4fxXiyyKlwhEkhXLy8K2Xc2ukeZVRbzQgmS5/kcYYS7zwvUlfR4APPznLcBCLg+gLEzi\n",
              "xW9/rIBWkMoFV1NvDnRHA+Uct9DBMDupL8KW+m1nf6VGZfQEOwWYLPjW6WjvisSq31UHAKSzYD+W\n",
              "1VGNjtoNYDgLZUcs6cdVh4nmTXQ3rrHIbJUFrYzRAC9/95ZrikN6lMr+pYLJIFnvT+mHqxOSq4lC\n",
              "d1Q5SXORrHUyUmA0aALdAywDQtMY6Z+xAAADNEGaImxCv/44QAGHpn0EktMf9fMkFSnI5F5j17wP\n",
              "Yg7+H3C1+CGDiakB/tkj7+19wMVP+nFcw9UKdtSbf80VIt+BMaLF9LhkXrCN/jh9Ae31JaL6HSu6\n",
              "pRANYILhGyEdSVmtfMY5+na+WBHRfwfnwpymHfbW6aIzT79MmCuiPu7iVoi0iEt8P8pjhYGo11/T\n",
              "opF6ZX4HVrEMaK7DJEyCpBfroLvhtm/Wu2HMcmBlRtzNYeUDuSigddLgSHxH0qE6cZYE7E5iEcV1\n",
              "ECe9bx7XLxVf33adM3Dryn1N9cgH2gk0YcfeMPxNxjzqgA4caWP4TfKlWIeYt5iKfQlUko/Wwze4\n",
              "fOwpABfW4J/tbUisk6OCIRsngaDiFmxr9Vv7j/OVwxQWflclU65Rv/RWQrp+GC0JEShattoZZFXo\n",
              "mnTU0ZLYbZhQC4h7301z4L4HQAF8lbvyIRA15oAsj/Z/zsvQz971y7/tPsVsTfonyf4WtZmDcBke\n",
              "1B3ZMihkF/u+NhZW8tn1k/Hl2SY7wRCcmeBMeRgTyVZUgoi5iYZNP2zjDKyyfBnNJFIFjTALSJYE\n",
              "2pynT0BHoBtxUNjSyfN1nQRZxY5FStzi7hIG2n+WlE/mvQH6qcJ3MvZTEM2v6US4T48dp8Pjpd7n\n",
              "hD8KgbgTOZX2juJLOh5eVH4vRTpskhwg0G6eXEvlwdXCmHTevGYeYgd6JE5UOF6y+QiDNrygnWGT\n",
              "orkBDSKBu/lIbASATBH2TSKcXMrGrPrHM0lDR4mQdMKQZo1TtXgnhivuBpDAIwewd2fr7piIZv1v\n",
              "u6LXDa3V0aF0MK82CR25i1Ymz1AITb9jVatqPL89mbmfrq3xj354G0zSagxDa8mrDhwjQdP2QZZV\n",
              "el8ujGOdn/6Kf7KJPnxlFqS4D8qEllnyUp2WU2V2LWbZ9gay5clENmqCGm7p0/tZIfA5FkLp9mar\n",
              "81flJw1pS67Z/2ZiQFdDGpIG7WpCIIJL+UCCR69O4J5o0E43x1s/ublsoJlncWILUvUFQhWb1vD5\n",
              "y9P0TSwzcsepfBChfU+xWGLzU3ePaG7JioHJ8A8vweRhK3idzxvLwnJFeaP81KIWjLgAAAFMAZ5B\n",
              "eQj/AB/BNSABxpO/7nHZG7g9odi3SmjSP1Hr6lvo6XmZ627x8iyUEqFsp/W8jPwDVjFerogguSIb\n",
              "6LmfXfzHCETVJyZ/Q5ETCr5l4ygw/aRjmrdMs5eAAbtzUcdQkkGbv9ymRFsr5QWOHA6DoM3WKD+r\n",
              "bDDriDIfoJ/Srv+TTp+hJrSDQCJSMSGAtz8rjKbWn5ia7do/xZ2YtvV1BqJa3kgzUljO62KrolKJ\n",
              "eX4Gyny6cJ6wWLb23/aRTlsvjVTimVnw7FLW8RjiDdtmdkvdoflyt48b0hJbkAuOlOdB0JJEUHld\n",
              "44wLcE3P9q9zYv0P6ARpGUW9/Gw9oksjkpW9XJRcnVEExG3Z94LKlvXyoIE50uwNiJw7wAsLJWB0\n",
              "mqJOUBB9mn4B4G0s/xLlgCm4ipDJI7MMRwmvZF+Rfw8iRMB0LHMdV92AEzEAAAK2QZpDPCGTKYQr\n",
              "//44QAGJ1VwItCE4vVv/9/wIWEH4+9I+N0DORUM6Rr1jTmxn4/A2FTrB+W+ZlDaYZ+eUqbpbW25l\n",
              "mIYjINbchexOscF04l3X38LxkRfn0c4i0kTULXXuUEfS2AclnV0mIXsWCUsIeo0cO4RC6YnAomyA\n",
              "G/XERcqsmV2FAgZGps/m79ihJpgI0lzwFUE5Mh66eOOMlOeJJtqWpwMpTSWDFFQM3yaPWMebmyhA\n",
              "YV/J8z1Nlt6F2yCCba9NZnoaT3bHiaOf66Ho4CRgtmR+F1jZFQ2o9DJo7ly0fSyV2s+eI6uP7Q/Y\n",
              "odpSjkXBIqLNTLXwQYFn70UDB5fWQ+0pKnmhJqBZBquL+8Mn+GJHUCG6vHE48DL9g+CDO0Xq2ewT\n",
              "dO59D/10ryS07XIIKOAJi7I47Drrcy97KIaMpUKqjh8oaw+S1fnTFBpjWaAQzZBuowM5pS8jwSvz\n",
              "TvTisMIFhbLvKs/A7IBojhuHzodPA8kg4IoZontUUHmSiMd+aOqyo66qJg/alyIxDUIIjcULpCU5\n",
              "XpPDgUsL0QToP7pySqdU0pF16DxFHQw4EhmBrROQqUqXnV1dIGOuu26iLMlfVADKe2IPM0V4LOdf\n",
              "g+/449Uw+9Pww4Cn91Nh8onNPt+E5Ei9UNPTKiiJAgAZudEBw+iWowy/nowyHONg9wB3Hq4Brh9J\n",
              "MSmR39ER05LVMNl1W1PZ6QiC7DBvBXWjXfdKF3UBIZ9t+kKYvw/pFPJhTfYVGobq47UOdQCYsMgR\n",
              "U9Yd//HyzyypzViIeE60HA+TsJ2CjeK1DHwfqRZK6dD1NrFcRbjoUg2yakxrfJyFhq1YkWu+tGaI\n",
              "ijxYsx3VvZcfH27O8lvbaNt5sVgSRg1icmrd/7fLNtCct2Eb5n0KoIu+Bt+lBuvZKaZCMSwyXldq\n",
              "TgAAARZBmmRJ4Q8mUwIV//44QAF+BeatABnJ03qX85UbIIV3VAsESZ5FDYukhkIV5mrIztphHs88\n",
              "3JfoUYI6SziIU8HhyyxjFd9bLqVbfCh7TYxrlyWsnDahUwAONsbd5jvMcpx4/E9pCt2HRvKURILj\n",
              "d9uJmxXbAySSzbq5treG83DeA04dg2oaoU9NeeCyGCJ1kUrka7NaYPPQZzxMBpyGPeNA9yft7fhC\n",
              "SNrcBkHRb1MUQvd3X1EXd1uO2bOKbfGFNc7I4Sq02Gglhue9TGdn/X0AV4yCbpJZ5nKwKxMAscF8\n",
              "NMUP4kSAocRZZ4xPS/sihp73GLbRxezEfoFgkPzRqW+Xa11dFq6lZ6LD/ulP5AOnojoqti89wQAA\n",
              "ARlBmoVJ4Q8mUwIX//6MsABiluN2AECm4i4FdTQdbIocSn9swefTXCOoURWS6Rfnm5HVPL2ix6Jr\n",
              "GuNTpZe7SibdBG9FYIxE99m3wUsewI3B7r9r5T43IIJZ5zIzU2EjZlsiEb93CHfxE+VRChvV+5o2\n",
              "sxtgtYQFivkgqVUAHZw8zEqoRx0vkSvHReThRTIboyoPeKMIj0ST1st9zzkvoUJvMEwNuCfHUGsp\n",
              "hFYeSeXPFVY45y+BYWM2frcVTFpkSNfWo9d0XDmxZga+mKswfZBvMs4/4XoCMd+Qo2/NTf+8cOmz\n",
              "RtZqp8w/RsXaqk1pqR+SLAURaJSk6SnxZGU69u1vexkpWe6ntvbp4TXyssEoh2xrqyY+9fCXgQAA\n",
              "ARVBmqZJ4Q8mUwIX//6MsABiluGLYuRDkQAgVwKhC16MpJoXWvw36o2vDElTo9P6yQYa3aDid3K8\n",
              "R1jv9o1izJjKjYKOZbRZi4GB/LSF+Is1rpRTn8ey0+CEeBhyNBGPM9I4eDH9XBjfzY4H7g/j17Z2\n",
              "a+FVJ8oqClQ/17MON2SgJBZcdlwMvb6hyDQC62VWjwnxhLMiaO8U1HRnEJp6KdbMAGBSWKP6xOWI\n",
              "Ns1ezSHKPkCQR7HWjww2LAUFvNgNs6Qvp9pLt0XHYc5PGffz1eEmRgtrB66I+/FGX4UT3IpUkEnK\n",
              "McRiUUkwVlpJaIbzNFxddyHGiuF3gobCFgK0oiyOr/pVrGBxENr4BNhtU5iwXpr/PKM/AAAAsUGa\n",
              "x0nhDyZTAhf//oywAGKW4YwGbR6hnZsJjm4ZnQATozdvkujwTx+gUJ9iGXGZbbzI0LCf7sjBIDoK\n",
              "6m7uWtunFYkfZqhTWusaQWuk3hQ7HLoqR/30FWeoQnS4976GHhNJtAR+ixQZ1fynVpejBrSmbQFE\n",
              "cUw+nJSE/g3eAv639UM4fBdOXgJtytHV4GBTfUspnq6ES5IhIRTXtIpFxSDWFbBkeAGxnTjJ/cd2\n",
              "MfQ5WQAAAMtBmuhJ4Q8mUwIX//6MsABiluGL20WjcHEIYUiA77H3LGXMPrLL97QN5zgtXQBKqcj/\n",
              "iRI8JW4V2W1dsuFjp9ind+q8o9BTujRYqucViT+pvMVevtOisdx3ywd0QPgiNCOMLu16l2upM/mQ\n",
              "QSRs2dKTygYf93x10KkeiL4yVHITYH76OPmEBHl9ottTNWAas9jDWKtCGFlj9bo2PX8ChaNYWRa+\n",
              "Bn6zwJYx4qW1mue+ExJq1Pe/oz/js/oc1qTRoqHd3ibx5rcB3OD6fAAAAMFBmwlJ4Q8mUwIZ//6e\n",
              "EABh2Bj5TbBRLAPSamKAD6i0gKifoxnL9dMSdrkrZ4bbzulqVHLAJ65hYvE8U0muGPNjUVkNoouK\n",
              "zL16ln/RX5b46j9flU4wugnJD0IBXEI/3wdd3aMkkLGDRbhpR8YsK/SRafi8dszDxpNp0c75Lmcn\n",
              "CX+a+68gg4TBZN+Rutt/tZkChtbyuy9yylclL5GH23U7n+cQg/RYvUbG/qExdtGsuwuAY3JI3l/h\n",
              "npn2gLEq0LqcAAAB6UGbLUnhDyZTAhf//oywAGM4Tnuqn4wAnU1adnJe3bCWOP99a2Z9SypO4+Bv\n",
              "m/5fvyegedFttrFzGZma6J0DH6j7wTBuVdre2XfnTCHsxFJbYwgnopMdgG+9HkVUPhRHaAxXmmu3\n",
              "1sM6jK21AUVjNb7XlQmXRVCBDkFEASnfn8p5wtNnJi8GTvnDuSgoDx1pfV9Dxl7FFYr6t58UweYn\n",
              "Ya0Xw2YA3Wkf6T5fHS88AFOvBzQcevLtQCGO+u1/CLh/0tj+rGBC0VHWgPVsDSadZ92wfw2oJ41C\n",
              "4MKHPM9sb9TlZzovk8LqkOaAXceCNF+m2QgHn/ctYFlUWITCJdZTKM7wA6WZ1RA4a8udMr3ZPXhY\n",
              "pv//mSd7HZMiSCEdysjgFPTs6/VrjvpZ9QDhjWBOf+KOdtOG4SOYi1kqa4EnVeyLl+TovyGWIdIU\n",
              "Eyel4Ux74d/vceSNx36K9qaji+Emh3VJfoPfCYEczgenYJ6Owp29s+C535rNlb2K/wZVt2XP5DWA\n",
              "IUNLrwbg3tJUUTYRMJxYpUGRrLMXquk3ijF1dk+scP/091sTyA4jgQzkAJm3N96hRGbO9n2DP6cK\n",
              "LgGJ+c42PuPiudPr5RnV7AGrpjBQ5WxNjGcKdt0aizRiRuXYcPacooGZrZwJOQAAAH1Bn0tFETwn\n",
              "/wAaVQsQbwppcQqUNzSF4Pa2wAk6GqNBwPiZmL3s/Kh7tRkJ+nS8DltSAOrnY7QjiVtsf3HncH0T\n",
              "UDq9+sfpVqIbMk0iPoKSubionjE4UNzknnEOFUcxNBLIsrgvOzMrxrfqSN0SKKc2krsrQgOh4+Yz\n",
              "ORQS8AAAAG8Bn2p0Qj8AHwyKp1jpzurpoiU38b18cXTfWPu8sSwyU2ABYzrAY2OlIBKKphBTpWAw\n",
              "YeDUh43OHT18fepID3PrS8uJ0FUmrpeejya8qhOOOkjY4Svu5UxiXq65EYzmby3bG5LJXImy3d6R\n",
              "PKecy8AAAABUAZ9sakI/AB5nNj7iP1ybD9smzQA9v2eFT7adlBkMa134DFuwg8jAJOwR75EfcjIn\n",
              "er3fg2o5hTYrbe7Wvz7uxCK7TUvR8S2+sv41eQrlp2w9oW7JAAAA+0GbcEmoQWiZTAhX//44QAF0\n",
              "AK9LGpK9LvmZjbo5u9eqVpJEgcX3lxsB6A0J4wxMdMeuHFMeDtDcYrBJgIUpC0zlVQFP8GRrYk4V\n",
              "ozyJZgJupNNO1BnpODgj/V7ztb/DOCDImFCSfOh8XHrc/mrw8cTQzcboP+vHr6CZDX1mwTKftx/+\n",
              "JySHNgVfKPOSgRGpXgwz+Ab3VPDBy63sRUhnw151qxDZ7wQPt5bR+3PZZl8Xca1lEfvnc1rN3SM0\n",
              "fLlL/9bDHkHW9r6V7r7NBC3zU41td/KJbmSpWjmxYeHogIfc5YTqSNN5Oy5Cil33IHPTkZW5/GMU\n",
              "/xU/UIuBAAAAj0GfjkURLCf/ABnGn2pXjB0of3djfEKG9raEmnAASx6MWE5kvmOuziNq7NOw2nfx\n",
              "82lYYJPo7fHHBk8fCcuJ9d7fYHchJzyk37F3QXCdJ/VcgrBi0bzd5M/b+W+EofQqozq5UedRLXQl\n",
              "4iyWHXx+KYCKU6PkXBTG6xM0rwsFZwXXn3oSfP1QcPe/Bd3N5cNnAAAAZQGfr2pCPwAeZzYmQTbs\n",
              "a24rogdHIAVoHsxbCDDIm5JEvSSgj6irkAtuImHDdRu7pu16qvTf4p+sV2AQj3asIUFMuMBueAh/\n",
              "uIVc6JqP90TZYuIhqB+mfSWc3Fs8Yq70KjpAjnA+AAAAqEGbsUmoQWyZTAhX//44QAF15PquAObm\n",
              "fWA2FQ/XYNLwdNtO7CDN/e4CKQEA6T6GxU3UcfZ7Sfk+4u9r7NhW4/B7++RMwWfQvrBfbtaO1rqs\n",
              "/i0u7kY7pGVQz8whTwohNKEIqO+eQiYXOqMEeqd3MY8WAe47qyGLLd3PzspLfyKa3r1IRHPyIvxP\n",
              "9qcMX5Cku2K/kd20Zww7j5i/YfZwkQIOto4Q6+EEHAAAAHFBm9JJ4QpSZTAhX/44QAFqkCd1MjVT\n",
              "mPK1Rhe+XM4qSTeDpgRsIyVDE+ELDD5nEqorAXtSmccPq4jfaQgJzuordoUWrPbF/SMKINrd7mck\n",
              "Tj9lXdGQvqOwl5v7c44fENplprmyNkZ7azBEOpUmxahJwQAAAKxBm/NJ4Q6JlMCFf/44QAFqkCd1\n",
              "MjVTmPK1mkBK7acACxJZSDmqi7FNjHjv6EjYPXkHPJo+hu19Cxj1/AU4tMjNXAMOjlVm8sL+jd9/\n",
              "gfJ6MaidZ99NlbxO3rBHSzN0773I0k5MwDyf6d8AK5LjgbC3c+mPGt1QZbhm3QRK96w6HKacW9Y6\n",
              "4uekX14O4cN080lM61TskvLVej1rih7sUpCPLr2IbNChA8ExgBbQAAAAd0GaFEnhDyZTAhf//oyw\n",
              "AF2q4YticqiJ2hBd+2ODEC+MfDvfsMSSpTRRxYaSEgBPr7z31fD6YX3OBAXE91Ss6m8ORZXSExzN\n",
              "tTy/iRAHacnaoxKzf/l96pcV8FzR6iv2QX3Sl1r1kpxWFBygh0aikcKnWLBn4gTMAAAAekGaNUnh\n",
              "DyZTAhf//oywAF2q4YticqiJ2hBuHQi1OOAIHwl02wkSJHVGcTNjYf4DZhHn1s7/Uyl/9E7MsfgR\n",
              "I6/uvg9L0FtqOR6224NDyje+Uy+OGH8SUEfdcfVuGNcY3JdrirDGk/l0o/l/m4sNcIAJa361yMiC\n",
              "gLuBAAAAuEGaVknhDyZTAhf//oywAF2pnhuAJ1NX0JxEj8x/E3ZQTq+1qbstsylcF0K751B2Rx8b\n",
              "wmlH4S5dCAzeAv3IePWRX7Q5i5RwMd2DmD7tIH3PnGlcdIhms20pCvMkoTOwsZyNe6XQZhGz63rc\n",
              "b6uzqjcUiDCjgMoy8CylcOjXy52nGsAgwtsSwvuDhoaP+WqT/Eup/wlIIwLQhB6y3Gn7ykN76Rkj\n",
              "Aj2ziHrgVU0jnjMl2EAYQxZdFJAAAAChQZp4SeEPJlMFETwv//6MsABdqf0Samr48+9LonhJcPAQ\n",
              "RyxmNv+077hJMIBHDkDy9J0n7ELeQYtqFNEOo9vzIeau8z1ciJ/HtONPUhyzmEgK+GEMrn0142if\n",
              "+WVKwqHfbJuReA3aEA00mGHG0Wl6KbYawbJ2OGGiN9eHf43ESIuWeOBi6cMlgCDN+zdP2nTRlMSj\n",
              "Be/kgY4SWtIsBXQZ8oEAAABKAZ6XakI/AB2s9cFomMQvadR04Pmi/yWDIZ3ICawZxzAIq2rd3waV\n",
              "IozsNik0kggleVK6pNGRU8ppsrkLGYPdj/0VdfA7+ek+YEEAAABcQZqZSeEPJlMCF//+jLAAXjmX\n",
              "HoDLXX4cKwFRHCXNPlYAhwAHgVmcit2DQAPuJJIXYUVHEIv8VLqFO+gPJeDcX5pVg1Y7tM9S66XK\n",
              "2dbDBINNC6wuEKkskbcAA1IAAACxQZq7SeEPJlMFETwv//6MsABbk5/90BI8d6OXRO0l9RRSVgV8\n",
              "FjsRvgGvNOMvPGKpw/Ovu2Sn6GnD2Zjaw0qwnpqr3JaI77sZPfgPupzX1FPFZR+oXtZfMSehMEnD\n",
              "2ptIck1Pp4GEyRj4gibWyM/gaPp8X48n+aKCcTBy+G+MChdOeQH1OH4dK2vtuGoUQHzBP9wxukYv\n",
              "TjJDXYCwW3Pxtm1JUSTDZOCwrk5LjZqFbIOnAAAASAGe2mpCPwAc/PYHxSRCRueedKLIT7GHzIiA\n",
              "i/064MuuOvdccjlUlTJfwbBOgpfVma8OyV7wdH4r3i8Im8BS8u30xVgnxOcGBAAAANhBmt1J4Q8m\n",
              "UwU8M//+nhAAWqt7PiAH2xq2FOhO0DCfOpD605DWQ3hrU8w+TZx4p2uHEvwuHyqmO4Kw135HCr/A\n",
              "7uMXs13p6EttSGdWUueZe5IsoWWpNbSQI0achUcm8+25x2dsy4RSC637GXL6qnFmp+FliUx1DfD3\n",
              "r3y2sWfpl2LShjfVvjkt8OSrN9sOtzg5Jl7PIUtJZlaHTFkz463ZR8y7BEw1WmBctWI1ixL+j8k3\n",
              "2Fd0zACivO504TzUxd2Os41xh24pGIAj2Puar2kW6GBVQAHbgZ8AAABYAZ78akI/AB0HNkpMupf6\n",
              "tFHc7YfCp9KkC+OM/fP3i/vGUK2WlaOyav6MIafE0xxWfDQsaVOkCboAw+iPj5h/BCM+eVdsnG3f\n",
              "C23K4V5rMrZbx7ae2zvjUwAAAOxBmuBJ4Q8mUwIX//6MsABb+ZcebruAELYgChCmt8t9DPOctIbY\n",
              "lV4gu7WNOxawtiwYMjxkjThJkOpiUxfIa3Qr04GZEa7CMOtrIJVeBv4Ko2jAscogXw85wjikz2OJ\n",
              "Lcqlcspufknk2yTSJJmNABvSQ/Otw31VUs2aL8roN58oYFnHSynoaTN6VG2l9uOoG0nNQ22SXuHI\n",
              "bUwvs+xMOkrEr+POhYiK4uPnfKi66Bj8Qku3viugafGOqq+hG6XiXweKshPk1yhxJ5UFwexd5jIA\n",
              "AZMrsT1jkgB/jfByjAEtgy7VjbqP/rnWXNhZQAAAAGNBnx5FETwn/wAYfyDNMBxnX0wz0SVchkrb\n",
              "2clj2mgX4Oq9qmTqGR5tZFWPAwu9PZ8x7ywydW5n0HL8nEKn3l4o33IROTrYnpBz/M5v0J6YKFR4\n",
              "jqOQD42tvwJefTtaYzzbmzAAAAA+AZ8/akI/ABz89VSz9Zo54mkGGRjHUzajWcTL7nDx7SVG0X1Y\n",
              "lbqE7SdqYzcbKJWIY9gaKknDJvWvgfUe0Q8AAABjQZshSahBaJlMCGf//p4QAFhsGWm4ABNEe2L5\n",
              "EtrQsItTPXlySd//JPoBJBz05rIwF9uJXipOlES/5XEUQY+AaNd6jlU8O7ixQvhI8xkM33ZPHodd\n",
              "NeQcyNbQUbfmUdr7zDMwAAAAoUGbREnhClJlMCGf/p4QAFhrob4gB+CRvP37BzKMr8/EOoLyw1mR\n",
              "crpJmLjWp3dpuf50zwffVshX9W2nfNswYf1lyujZWfvWXeRzoVq6ssnrtc7UU2JxvDX3OIWbS41H\n",
              "QhumNPzNAUwmh1pdBGr0Q3geAPnEpQEaTAQEaw8nvum06bLfBKJNBtC3+Xxh7qImxOnzBVeFT6b+\n",
              "9stHqOBwWA+ZAAAAUEGfYkU0TCf/ABfmn36TsMCOoO5kCWMYBT5spxfLvI1lczcuTM9YqgC5Evou\n",
              "x2E9qCidP6lDOgRFqsVo2CJ2faLEnwJz5TdL2CF/fNtVYPmAAAAASwGfg2pCPwAcTPYXKOIZmSSS\n",
              "ZQP/PLTpSKlqiTylQtCVr1RVnHIl7dNmSPVuShBtQ6sMtzaqvmV5Zt2dWVvrhe0CS3M773PQhkR6\n",
              "QQAAAK9Bm4hJqEFomUwIZ//+nhAAWPmXOtQ5BIzSCcRUZyIBFhheWTt23c9eRFoNSfA9w5+o9zeo\n",
              "8mjNjwMXWUUSHu/WjNrWN6JPTdD1RHLsiSbNJbZRvZfYbAAXYZ0MPLFvzK/HnhThyxHBJJNtDS7i\n",
              "KQk7JYnXIU4LrD9CUhDFEoFbgb95R2RbJuJV6EQuNGf/Wl/nP0LUcjRuZbgCgSMqMGQQ/PiRp4fi\n",
              "d0b6BqFQeCzhAAAAZkGfpkURLCf/ABffIM0wG4WszTAoBg07U2D957YfR6klhCR567I5y21Sa+rB\n",
              "AF3JPonSWjNCUdrNXcqB+c40Xtji5MDij0gPuI7bnBS4HVk8cbAc9O9CzDmdSrkGb5K96dt5ZJWB\n",
              "nwAAAFQBn8V0Qj8AHFjhqoq0hzVyuXV3//PKVxOW1pZO1AwSqep8w7WdDex90zh7EZBMLn8Zktjl\n",
              "4iU/QuDOoIFG6GAFgzmgytiFYcqhl4s8dlP2DErMqoEAAABIAZ/HakI/ABxM9VS0aEBhV1vl+DWF\n",
              "TIdv9mi15vNuQuH4iNjmPhg3tTe9GDHO7zIQ2unckWIJmCjFh3uRndj2KHJOTPZkokLuAAAA50Gb\n",
              "zEmoQWyZTAhf//6MsABW6m1huAGb3RJg3MmUzA+xJ2HX9u2Ltmy7YjJgdulHOG95dFjqCXr8fIg9\n",
              "enMGZGUDmJIDhaGK8Yot6e939c/o2ZBt+TIM2ae5CCsyv2OFd2C0R0/zvcSyw89Uxt5Z2Swa/QlT\n",
              "dAdzyhADi6iprLNIrHOUxFrXDE8B7pps45ynibEzrAYLmGL9wg9vf/GytayalUP5kd4TWN3QtRlQ\n",
              "hHQPC/y7CYFpp/vtd8t761nZOw6n178ytiHwZW+A8zoHXDP7m2J/Cxa+UmK1ZsMefTZ4VBQH2YBV\n",
              "QAAAAHZBn+pFFSwn/wAXREjhEeP2k6TbZjAAuu3Pc+DeFPCn2YuNvd0o+bK7HmvRMpkxXkzci5HH\n",
              "RBioeSNgbotwFNjz5iZSGahE9DBhEbV0YEWR2ef1cEkyGYxlnlExUJ2aUnPq1LDpzJAccP3jcRSG\n",
              "7oNi1TABuieFAAAAXAGeCXRCPwAbngsMPlZg4IdWemSI1u58/k/yKY5ew+lCQiEc9Xxj8p8xOgut\n",
              "f4ZaescyUgYKRuHMTM6FggQwpy25EPK5xrXQB3OLfz5YDauOYb7RTWZr1rToSI2fAAAAYgGeC2pC\n",
              "PwAbn99VMPMO+AUgAC2fIaPKS03lHJNn5erq5n9HNGpCZTxH7FNjgZb2iPBTjfkqimdgjP4fFqvB\n",
              "0knWpyCsFiAKjGwyvpML8AxTiCbrwUrKrIKm9WrCLNSymIuAAAAAo0GaDkmoQWyZTBRML//+jLAA\n",
              "Vun868msARHuA9aNr23dHwdhzLBPCcZQvKuVRXEE31O8bgvUEanhqeHsRZebHP/bnzoliiLethgh\n",
              "2cRHSGVdFjg1P0BSro0/uED/Qoh1vnn1kmIwdd2YB4UXJZqs388oT8pXzsQhkH9GDjldzVPzKrVJ\n",
              "8FrriE7fYYu388zZ4nj7aAFce1tnwDSMhn1mQkB7fcEAAABTAZ4takI/ABuf3wnW3erLc1dSasJl\n",
              "8XmruljjAVhxUV1j0cOSkJHbjVlxg4pIyu3UsaqtPPINYdVDVOrEMeUTFbmIhmWtfrN/lQySC3cZ\n",
              "ZNfTqpkAAAC1QZovSeEKUmUwIX/+jLAAV33L2PUI4fvs7PWyWGh84AAdbp6B1Ly/kj6WygHqozsp\n",
              "MgKaEB5w3lThM/U1eB/BKje6pfdH/n45Nra2kXINcZclwBv1+jArDWQ+MiAK7x4/aZzH+LX4Lf/9\n",
              "sq1YKHHqWTBdCWdKVp1JwMUNwsj+WbOPEQdkS5tHvl4IPaaAsNZP1qFCliAfdY7ANe/o+Ng0N/RG\n",
              "d7DOxuUTb7o5+bAAlmnJ2/nCwQAAAJdBmlBJ4Q6JlMCF//6MsABU1EAp4AZ2daFhVh7GgAvMe7Kl\n",
              "XkiYKNd3lYNCR/zqVtBS7dA3B/3r1MnrLmUznzqx7qh/EZOZMM1SVwHjOEr5ZAwqGK34mO1W4kdp\n",
              "ckb+C/O1KmYDYbz8USxcPjdZ/Lninw0megEopPSlAzvkaWQ5mNdwahhLb1EZxhalO6nKQWM0uwhW\n",
              "V6GVAAAAtUGacknhDyZTBRE8L//+jLAAVKn+uQAJYzlCceSLmkxFzr+Y9Qz/MHtsbgk3UdO4/MEX\n",
              "VI6AYqzQlS4L2oMSZUIxn5ONmgHktcnvvUpr/Ia6+pSCjrI2kIMFdbe4HpoowmwYf3XnhvmXGq0p\n",
              "UzIkeYM7ozkaSooBSFFJk/UuJ3zn4CPpe6Cz+DtW1Fo//DUCC4D3iN4toGpMpzUFFGunZJRSq1lu\n",
              "51QQSikXI4wXPCxfzaZhemAAAABkAZ6RakI/ABq1FMQeBparIclBqYPqKyZ1a7Vr/91VHCN2aSKE\n",
              "NwnXoYrQ2kPgSTFd4MIGIj4U583PqRV7m+dA4tHuAXk5mNDFPj/SFCt5MeSg6qTlI6UgPqZWNCRv\n",
              "V3OvsTED1wAAAKFBmpNJ4Q8mUwIX//6MsABUquSyABa268IJ6sU1yjk9Ov0YmQBgSVskhQjSwDCS\n",
              "kezth55qlvMoVDTdp7FZBQSNoFxkpmmA9gwMY7iVx2t+GPh4NbIH026SLr+fUeLqWjDIR+u/S9q2\n",
              "/fMuoYFoCnvD/6mYgrb1IvZhJOl9j7dz3kgRMjRmgd7ZQ75hXEIzLcnuqLtQHGoz7H05aF6ejIqb\n",
              "YAAAAJJBmrRJ4Q8mUwIX//6MsABUquSyABLJ1va+IM3Hrr+Y9RyB2JLEfiTjOHqdc6HM91EeL4Ku\n",
              "Q5tdqUTcU1D3B0v21HIwWFnLVaIAjaMtg7erAb1pZJJcGkoUyIPMaKIXIpXgeXjeE/PwSHu8jvhX\n",
              "L2Rd9L3QjuiYmTtIBNr1h5L2mZ86BytwP2Zvom93zZehL1PSkwAAAL1BmtVJ4Q8mUwIZ//6eEABT\n",
              "+7ImQAITx/S9ZhyIxm4hEY/2gdYbgkdVa9VD52uR9b7yAxnfMPOK89MKRWGxj8SDJ6WXGV7H8DVO\n",
              "m20H8oyq7Flk1woBgAzAIBFTt9EVbds927ZJocDxMgwzoppoi2I9Tc4kbv2Nfmn/bP/i56PMfq76\n",
              "3cQu1tBY4YFWewmBGJhKG/xI6aaXP+woy41a6xmJG/+MOSAeQ/oeC+mW5S2wrj+9iz6K+jSOtfyd\n",
              "a6EAAADhQZr4SeEPJlMCGf/+nhAAVHffwTb6wKfH//ClKbVIotu/QJAFt2+gWBE4xQxVJ16gA9C5\n",
              "ToQM8juucyLFBd2pdl7dVks9bsGkoDRqNQ9Kmv8g1wDrE6qQT4Zw3cC2PJi+Gm1BGOGmvyvKwTGA\n",
              "ne+hGo/zYiomQ1x2ZgjxcoY3ndD0iohx9SEjVaCcj/lonfrhe6yt+IrqgdfIypKcTkHHHa5ScAng\n",
              "+EWAq+eP822hDYYHQ8uOi/68xb+2o59Zj+tYoPqOHK4isa0FF7e6rH6kh4Fl7AZKNJsvmiac9Gvi\n",
              "EFwTAAAAeUGfFkURPCf/ABa2SPdl/Ak5nwgGdEepZLBtQIsvgXMt4LZsjesTA5EY8YZfO43EJaBC\n",
              "S2f3Jge/rABAzhYSKlsgWSe3TqdXWhJ7i4ZSZlNStUWoJKxXvtKRyud9l5z/WAKZ2wTv3/6cMhDP\n",
              "xGAt5sx34uEKVWcA9IEAAABbAZ83akI/ABpon4Y50OuwlCVsFaRDQEQVusrBGqzFDNQiZJr5spdm\n",
              "h8GTBlOuVywul9ql6logLA0W5EokFSXp1KfiHlC7DJDNiE+y34qs+QCUMbADjNbAQ2QRMQAAAORB\n",
              "mzxJqEFomUwIZ//+nhAAUdQ3J+5EAPwYJr7ZKoDv/d85XKCsHd2nsgZ215Ylt6S1LUPJDGyKFqiD\n",
              "oHprqqXPgSP5l+E7vHjAFtc/Jwu0xYg5zzP//ttnWWRoUa6HNNFN8aTBt0aTi1D6jlirtToszqzc\n",
              "1Ta0L/nEPKixr/BnHoquUKIRXGl+4JR+BubRrdWKxTGrvRnHG+HFZ5HjBOJgoGUU1YMzhv/o5y4Q\n",
              "Ko4MjhOySZ8fmnNHDyHIPfNiOkKLTjrVIRLZvmz0ysNIDbNl50XzjzRfyHlsVpSQhyOiVIxXSVIA\n",
              "AACGQZ9aRREsJ/8AFiqVc/PuFrf4AHyrYLqIyGvRO8q/pY/kv2xZj5TJ22eTXwHvt7CEFmmaYkCf\n",
              "2Q81f+QBufU/DqC+w5begHpqMQIsAwBAje48MtXOYhIZESUi0r7quhr5TAQpB3kI/+sWZs1VWQjg\n",
              "9SfQOJsRl+vHY4RD8oI8Baur5JdZ8Q8AAABgAZ95dEI/ABppxwxyBTBRpmw45AK0lDLo7ihkX/4t\n",
              "SNJigUvqEDDFqwua6fK4HCaMbrJ1sDfql052wX0h3q8DeRqumdm3aE2h9NAcwbCWoUTsSvKvWDlm\n",
              "apxwv/tHWARcAAAAWAGfe2pCPwAaaJ+F/kcZEql60xpmm65dP33ZFamVVHmd9Uu0ZZuZSaS+okCV\n",
              "Dr/wHSOQ79iZEk4Y3hFV1uN0ABCAuU2tZZg1ThfTVK2jUjjFH9ZLsDSPIfMAAAC7QZtgSahBbJlM\n",
              "CGf//p4QAFI+BaCAzAktNPZdDL6IzuwswrvbcAGNJzYv37gZBINU8IpXHhUzrvUnreZ8zkhzksrB\n",
              "iUIeD9c8FMaKHD2bWOxtZ+5dfNpm13SqXWVZepgeMXPGsV721MjTyz77/XPR/Rl4pACMdGjlow22\n",
              "v/Vy20v/M6Tf6fXMr/d07zCdBxiafq6af0GVxhzwWP3cd1eYipv7IVxIAVspDEcgZr+OkRWwEA/I\n",
              "M1AA/5CkRwAAAGJBn55FFSwn/wAWJkj3ZfwJVm3c1zWI8C8dJR4gsfjj0arenX/1f6v+rhJfqzPy\n",
              "Qk1ejLDcf0eey2x+kKhTdD/sWdDphTFtVKYyLnSPUO6+Py1NpcBYpKURVgm9L0iEyUEBbQAAAE4B\n",
              "n710Qj8AGl4KMgLv06YzcdzQfHNegQ8pk4IDZjHli794Kx1nQ3yYjRWoW+mKTB5Piu+pIbBeLIBG\n",
              "dOWwUbK5XRs1Qg/5H/xpaEL4BNwAAABTAZ+/akI/ABnIn4Y50QhSqB4EKxEEWuCUfDYHk74Y9KWE\n",
              "T9a52kIDDg0nFGokLWYrBrKC6w4T3AAKzmG3zeMnjuJryGw0YHLp3ZcIClkVKKe0BU0AAAECQZuk\n",
              "SahBbJlMCGf//p4QAE971a7WAD6Wp+Q0DNrCManE7k0UoJ5T8zxwaZZ9fAlh3VsnYkHhCb2jUCmc\n",
              "E2ae2syiXpJdORjR+9jkY6SK4T/9N8dbrxnh2otb7eRv0FzHSmntYCe4OXajPtu8+0inUVGDF6bf\n",
              "CgvLNq2eWdU2dL7XXj+StKaw/seB1sIoOVWM2natSVdL10e6rNcN19x3qktjPGeQ8ro9tuqGb3en\n",
              "OncGvjfiD/B8gDwB13mi5P/GimDiG1XRGJllMwoq5PTuVDZ5r9zKwd+239VVib+0B37C4AOnKSZj\n",
              "VKMMJ5WIXTSy+OixZYwLbHH5SarUWl86DD/AAAAAjUGfwkUVLCf/ABWUSM0wQGztCKX0QAt5w6cq\n",
              "Y2xXGL46BnJE7rMOAlgXRSUgBy2f1sch9Mov1p+UjjrQo5yc1RSsLUEOGcm1s9mv5g01CHttq/B0\n",
              "0nYuwKSSxbhMowQYLaWU6kIohsx7ogWeakxRZuc01Wf3r27uVp4ncRLyUAKYRb36M8qU89hQu+Ak\n",
              "YQAAAGEBn+F0Qj8AGcnHDH4TFomcUe2AAohIL/D5thDqNoJ5aFhdNDC58O+ZL4n/Zt9RG3Ea10fu\n",
              "b56l7HJIPHXCyU7gNpqTuRJlhvvrwq6+rKF9x5UUKU9mdOFHKl2cAA4wAFBAAAAAaQGf42pCPwAZ\n",
              "v98J1ZjLSgnVs+1tkM9aV87BuDAa8lDqlwGhRcnZDcOIPGIRmXTMExUIZt/nOSrXc+rbhPprL+E9\n",
              "6BDKAuBn3eSQ4Dohw6y93bavDIziVDVZYxDM0W4AE8m5ohjpcIAf4QAAASJBm+dJqEFsmUwIZ//+\n",
              "nhAAT/4vDNJO66+UeJLf29hpAFOH9QdyuOy07TIK4WiCP5V8ANt8/FlqwliRj0ePZxwei2N/P/un\n",
              "876MW1SDtfK0zqA2DiKWttNxuedKBH3iOTcDHvbTKJ4+EASpZuH46tZDF724vxwOt0xZqxIha5rL\n",
              "qNXIOJoqz9ddSdYYP5JvTJv4bF1gEQ61D4Y3AIMZK+208o9gX/oYW1+vdZAyvr+au7+CkRgQe/an\n",
              "1ZWeZ1MeDXQEacM/2HTF7UazUvl3g9NVkNg78ZL3Au1rDsuF0oRK5jTHczD+4aPpMtDDptl2d8M+\n",
              "7BIXM6txq3jpFpqDqgKXScf03TpuYgzN1liSgHqavk1vn7y1ZoC3+9xDUORbzWBQxQAAAHhBngVF\n",
              "FSwn/wAVlkj3ZfwJVm3c1zWI8C8dJR4hG98VWHsy+RuI40DoKvmwBi3LyLUjLh6Emp+VnetsrvyC\n",
              "qs7R6JwM95RQAja8F+i5ILGX4GTk4RpafPLoIvXEiu4Ez8MJ6dfaUqGkqjOTATgnr+RJbnEm0Pus\n",
              "EnEAAABSAZ4makI/ABkon4Y50OuwlCVsFaRDQEP+iUTnDUeLdKO4jqmTkIHUEJ9ByyCglooanYlP\n",
              "aHqtNEaII7e1JcK8g9Y6gfZoNYDzuclnoywmbKCBSQAAAQpBmitJqEFsmUwIZ//+nhAATUQeM4Jv\n",
              "ea8BxIFhIAB2fCFtCJtpvIVo1oOH1hkIyGCmliNvHLVfAuPMiiVcOn5AuZj2l1zSs63QafWZu+TE\n",
              "c4HTmi1fXOm2BvBaw6EpS4wgKP9BzckdjdnBaRfdmkkV43BUXTt7U0E308p7G8ztC1m0Up2R3Fz1\n",
              "EUKNZCid32rUA2gxWIV/kMGLefBBi1JwUZOFu5TfICLuAkTDjXrssg8tf3eZCnI18fWOoAzD5XpQ\n",
              "Kldxr5kjiY750UM/oHFvHExVUltQCurIsukAclIRb4NgmR89ZIApcd5XNVoQC3jl88tTP8peNzlY\n",
              "d1fipZdueM0P1S+y20Qk4AAAAG9BnklFFSwn/wAVBEjg6Bxz79pZaalbj3VsyZt0xodeoTN1kWrB\n",
              "v9M1iTmBvaAEbWoaHWNe/x4C+vYYJTKguhyDsrXYz9t6xd1MQZSbeYrIugNMRDnip9Ye3uqGEb12\n",
              "t/h9Zx2pX1xQ2/fRW122kPAAAABOAZ5odEI/ABkeCwx+Z2SwcUlgHyKEKIH6a4sfuQeVX3OUaf78\n",
              "ncojwP8dmPGHq4Vi0Z/kUTpncmrKWiRSf+ND0OBcCxJQZegYU4go+YIvAAAATQGeampCPwAZH99U\n",
              "jTJfKYc/fCvxSXsIbci2usJTnSflIV6KwuvYKI6zixXSnKaJDSCQ6KaJpbdUxct0AAwNNoQSgVVR\n",
              "Mm7GsnW78DFgAAABBEGab0moQWyZTAhn//6eEABNvnsosaudmfpKQKsnwmInJL66VsnaI+WYxmgK\n",
              "92Tr/qpS0jrwoeLC6v0qvumRYnmtcQPM5KIYcMhyqdrcWmIHg3D/eCuw8OYHRP7VpwxyLyqmWm1r\n",
              "RiIqOgXbYE9cQbtgRB5OyDGTIr2oOaqIzVKnOGVlFeyZHVcLuYvaOB7a2YuN16MQHWm69/TBDYjd\n",
              "AN7/094cWCHL3yggu6BHVtaNRuTMjekjVip7C++E/2o+xrTbqB4IJix8/5lwsucv26xVHYQVcvyG\n",
              "6fihMF8NpI8uPem7+zWIov/c6cNMe7Tt3BS3T5LjDkpyb25u3/qYCbt8gvswAAAAY0GejUUVLCf/\n",
              "ABUGUjavVIVE3t1tQwlM78Mnl92+YpRT10FEbACuuVsUgMk7MntbwmhonzGODwygOzAfpJsdlJiJ\n",
              "bA+abvE/zxr54fjEzOatxgZ55ACjb1MouvvV8kab2e4Z8QAAADsBnqx0Qj8AGR4KMgGwoEulbsKp\n",
              "Qtbg/+ANy4tudt3bEMCm8ff8Txo6mjdnsyCcVWCjSfcTg1WwHoDFgQAAAEEBnq5qQj8AGIifglEg\n",
              "ptWcH9Nc7VlOrEr8NthwZj1O4a+hdG+4hnmXtGSgjKEYcnzXLYdS0PhRc0jS9La7dKgXcQAAAMJB\n",
              "mrNJqEFsmUwIZ//+nhAAS0QY88uozkKoc1ABhnC8wlesHMoJUDKJjbhO/KuLSfOXnt0T9HKYJ6IF\n",
              "/c0M1NVwvdH9MCnkcaQZX+IKgj9DRwq+RJKbyufBBWvmJEKD2N0JFlEztcHFkbFuxT9zMaPq2Qu3\n",
              "iTep6Imw0nXoMUzHUAnZO06z3sSezGa18iRnFOUniLDTF3xIuCjweDCXEYVlbUJDMiB0hLLDQ7kW\n",
              "MA7Qg19kBZrQZvxMrSp469kNJfanVgAAAGxBntFFFSwn/wAUepVLm3hWcsNrviTmPRjJhj4El3nv\n",
              "BFaisu9W4Y2uEZk2eN3SIRWhMnDqyoYIsInPdXsEz3XOv2C+q1G7EE5JUMvK8JadP+kbt9k0J+Ib\n",
              "Dm7TLXjYCgOfBxRAWVdF1b/wooAAAABDAZ7wdEI/ABiJxwyA2zNUT2ovlhdsh9R3sglAZmBDnA6L\n",
              "YxvXRCmjcsdKyBBAFse0yZc8StTA9zuQAlIoWhlu43wKaQAAAEUBnvJqQj8AGH/fCdZIRzaJN+BY\n",
              "QXQNQCyw5YhRYnppekz3eL/txbwqv6gYbUnsqax7wc+wYm3p2zm1njHaovDpk+OgOmAAAAClQZr3\n",
              "SahBbJlMCGf//p4QAEu+ew0Na2gBXWM1+qhPEwN3tBzcaskcbdX81mh1wtVeHM6n6qy8COZjOlFO\n",
              "QA8KOMUQyODjxw+AuH0zglEXXWv22wKQ0AT9N1PVG9fSJAII369CMEUXi734toj0+kxpW5r8+YF7\n",
              "8NGz+j+zFIpXIVA4gL+LGw65xu/hIDkTE1GBKfIA6+67w30WEGJ5uK/8Pkfl0MWAAAAAXkGfFUUV\n",
              "LCf/ABR2SPdfCiOMAC69UjtPFYIwNj3HXtjuAzQKsdR1WR7uK3Sjxwmu3b6mCzLj5+pvXwVQTEIg\n",
              "RSpzQ2bYZQbCaapHXXb1Ml9PBOwxA/lK9Gp3G+JZBK0AAABPAZ80dEI/ABh/jGdYvdgxWe4ySk9C\n",
              "p/2Ra6AqiMaMAl726+F/6o5zQOOhQn0Q5dOwK5w4DJLzP09Tmgu87/SGiXyvv2Ryc6vFGXLntbnB\n",
              "oQAAAEcBnzZqQj8AF9/fPhd+4y0T7K0HHGPSYnvODb48stjFFkxySaZFT0P5gMsubDchDuVN/UsW\n",
              "WeUvUl9mPZWcM4TJXNsfwBg3oQAAAHBBmzpJqEFsmUwIX//+jLAASgF1xqLJQtScZOcg+Uj2Di1M\n",
              "nsfebktfcNm7e29T3gDcm7BcJwAZIYrxOP83wyoipteeyqkPdSYN6YCGmcrvSnD+h0uzVcIOSDnM\n",
              "T88evkaq8uzMwpqyHsMSdz6SX1phAAAATUGfWEUVLCf/ABPkSOER5rYcXJuObrorsExftO4SIjm/\n",
              "BjDfIvffUmtZyvz+2GP3hwIizlpcV1A8qx3vw2ZP4naaJvUb75+9CKgGAFTAAAAAQQGfeWpCPwAX\n",
              "399V2Ztn3pr5+jfYltKtoR5AaU+qTCPB9dXXX1XrHtSjak5gtLnGsRlbRwXPblfKbU8gQs7yp3mz\n",
              "AAAAbkGbfEmoQWyZTBRMM//+nhAASb57DLktoAaULhAS9D6GnLXKGYXJDGauCBNSVaHdcnQzcTUp\n",
              "sVekuqCjBQvja4aKVi4awxJoGVUOtT+MHqfBDnKlrQ6OsS8bHr1yLNbIXfEjPyAYMNyPARScFcFB\n",
              "AAAARAGfm2pCPwAX4mX9PA91xjWso22HfwMZY96opMKX3W7d9hsMWYvz1QdllMC0OLt1vjxaAuHJ\n",
              "GXwEqQwsPsbS6qH/PiE7AAAAm0GbnknhClJlMFLDP/6eEABHQ9V3XkFrHByGkwmDNQEKn75NBfzK\n",
              "1IUwFv6sUfUU1iud42Zw/XgTDKJjaAi72EdtEcP62amzyXVLZOqaRw75GgleFYtEbp1W1WHgDAz2\n",
              "cL+/vY55L0qoKkljrWJeX43tiFOitjt0sh1LuWeScZdkyXMf+iPwR9r+hfcOi/8fUs3kinzEFY7J\n",
              "0FsbAAAARQGfvWpCPwAXQp9H+Wip1nAtCjqJMOI9APxfBjjo4bumzCjOanC74aVlRQICDuEJabhu\n",
              "ZhHmwnM3o4y1fUYehmErVIQUEAAAALdBm6JJ4Q6JlMCF//6MsABIViRXiB6wBEeksqjr6IDU624+\n",
              "XwXBtSbYRA7A+C28Gi7Dh399yUaleWTGUXYPGZtyaZggIcHGLzZvpC3wcht8UfV6xH7gSlBEq9qe\n",
              "SG2+hnSUsMWY0lcihD90blNvLGsQlpFmv/o+MqXWDcyp5IyVlgrn85VdwRr1SI1JHgzOC1qdjgAl\n",
              "llKHzDvdYu2514dFRpm7Sq+fK+3B1kpscrt53CF04vcm4vgAAABXQZ/ARRU8J/8AE1zVaygCFEtD\n",
              "6R9szS4wY+GsiWXrTGA/4szgoSS07p2esfASs/82ADrgahHAOYaH0O84GWB1eg6EkVdBqLcKtKfX\n",
              "0+qS97gjlTnJuMIXAAAAQgGf/3RCPwAXTIcNc1apR9VUxBZRS3kzwBnx5zzdWC5L6rFic+rcBOgm\n",
              "qe1H+p2+o+Hpx/QVeP6LWV3aT8NYeSgGfAAAAEcBn+FqQj8AF0KfCciYxC9p1HTg6vsiqmgLGyUT\n",
              "ImFNkqQQmKwK2dv3lpMo86cSpHyZdS+ilOjNyVV0vds73X4+a+fGplIBgQAAAIVBm+RJqEFomUwU\n",
              "8K/+OEABFvmWwOprMAWHXaLFb4HmCGMLCICJg6/7EZyQQ3IIVbyka9TT2rp4SRCFHzkM4EqAkuBE\n",
              "BTtyh5PbcfyMgq2Oj7zN7AldaTVYQO3q1GPyhSRTGXwEIH6MFS6Jkk5zEMWPnM0BSA25yRvjw0Jw\n",
              "0QduuACd3IFfAAAAPgGeA2pCPwAXRSX9PA91xjWso22HfwMeJcrGz4xWbZwGYIiXTeO6+CV7XCmK\n",
              "jHV8/XDgFJ9hw9xfX421AAelAAAAbEGaBUnhClJlMCFf/jhAAQz2thWAImFXo23EAURrrk6w/YSv\n",
              "tpQUqlhoBVX++4jAjEgXoh6NNkbYjmjwNcwfDUzWNVLPEo/K1DijSpN0s2XMgXMKVI8epN74805R\n",
              "uTu/zDfWv1b6K+1eFwAk4QAAAHFBmiZJ4Q6JlMCF//6MsABGAgqs4AON73S/+MN8Vz39hYBPU7mp\n",
              "ZlefLVIEgWde9Wy4JJOKvIJcLiIntzeLUAp37+pqYps6zv5d3vHCBa92ghaBLBfpJmHpMeeu8kvi\n",
              "6+jClM/MCnDaPznS2zmXWEAWLQAAAF9BmkdJ4Q8mUwIX//6MsABGEJJVzABxv/9IfGG+K57+wvky\n",
              "lymHQvSMv361t36J8jA6kK9cWbaLj7iUj6IR8Ku7Yrg6w+zWujTLzTK6PeiW0MoZrohPWhqld1KU\n",
              "erwesQAAAHxBmmhJ4Q8mUwIX//6MsABGKR3y4AfgW777z7j+9j29Xye7A8Ck9UGb3FH5Yh/4p2z8\n",
              "UraG6HnozutKgE4oaxxMQkkLFagzmgL0JoGlSrjTu2j//WcT7yJPIPfJ/K2WKi6ntb/oAr8Tf/di\n",
              "hSvNh/UrKZ2tcW43Mh9ghgOmAAAAcUGaiUnhDyZTAhn//p4QAEVoyuKABWolzqT96E0TYCxYwMZl\n",
              "roAvEBKCfoM83E1qH7BqOx0Py5cfpkpGT66G7mIdiy4sbp+uaWa38UO0UtuEvxyLC+GSdtT+3GN/\n",
              "NzBPWRxQ43EQWzgXkAjfvRkyLKSAAAAAnEGaq0nhDyZTBRE8M//+nhAARbjz64i6fq1ysS9NPACw\n",
              "ZlSQYzIvs7d0Jejp4Q/ioR59V+vjElh4e19FANbUHFWl8rRsGGuewxQmoApVCQ8ZatikIMLVioGM\n",
              "csMS/FpRhsu+IDzxs1NKwC+wfFVqbzAoLu20JIm3ElcNoCAkCLC/XJ2H+48Afdwq941y5/O4nZNc\n",
              "izQ/YsAqv8DCUwAAAEgBnspqQj8AFrUl/V+YF8TwQrRZRQntKKNAwzbdCnD/jOMNKjQtXfdDu9hH\n",
              "7HDyBL9CYq04eJr59GJD3RZLrwMGIJtDLmwgVsAAAACaQZrOSeEPJlMCGf/+nhAAQ0QJkkAEal+v\n",
              "sv2EMa3xHYrSVPhJRdZ+0HwzqZPLol2F1c7KmSLhnjCfj5UifIaHbV9kHMcTPxwqlINBcVLNNX9P\n",
              "Zh2uQAer7aldlYrgdX8EZUeSFA3nrltU8G10mxEjDAORJI3aSICx/I9PjYkT8gTRfD9dai1AFE0h\n",
              "MCSXthOnn2mpzUNzSJ6W4AAAAE5BnuxFETwn/wASXPd/dpf/ETESeT7pQ2spyEY8BBUKVfTYfr3c\n",
              "RQwJqVThdyp8tnt7x81E+6x9Xn55DgTO9MBQoe4gq2T3fuO9yC/AiYEAAAA+AZ8NakI/ABYrX4b7\n",
              "H2mO4+0R0WC5n2ifOiauwn1n576cSxjvySSxIUq/LFlIE9d+MfqTQ3G0NQR7SXxgg4EAAADFQZsS\n",
              "SahBaJlMCGf//p4QAEO6bzvnbpLAD9fygdVdVhiJHI5ZuD8nljzY5S9U5mGgMNYvSVW35X/sMpWj\n",
              "Xm7E7VgryqmgaZCxmDgZ1YdtNvQQ3GDk0IkGPbvXkPM+81YZQuZosnLkNhMro7NIpRcF8ItvH8Ze\n",
              "C6Rxo/C/L5uA4lyqi0kmFc4IPzVIDgtBW+z6erklSP+1DfsuGwf9UTQhL8fi9ZdLdtgqdjJoPkxL\n",
              "qoxPgWwNaRVL2KeKjHA71dzAio99C6EAAABkQZ8wRREsJ/8AElzVaygKMYErROVfMuGtK51X6ste\n",
              "J3zFVgTFtxqZ70Y8mdiXDEbBQLTydh0gXokjBFMOd0EmfxLyc7S0Lsh7tryV80couXwgXjZWndMW\n",
              "fJnryHlsvh2sQbKhHwAAAEYBn090Qj8AFiyHDgC0LcaeHZp+zwneQveRSAtm5Qki34MLcujS2etP\n",
              "a0IrGKhBUeK6NprmR4FEBS+0v3jYwAc4A/S4wEHAAAAANQGfUWpCPwAWIp6otIZ9uTOkP4Elu6OZ\n",
              "1qr/kFVEFtvN9sVHshABFA5qOIFzsEIue9572JOBAAAArEGbVEmoQWyZTBRMM//+nhAAQUPqckAF\n",
              "VJx9/XBiU//H7Sib40Jz+I6rpqshu/7zfa/7P8aK9Xmle3Bhy2ahla03y9W+h9WbGGJP+q5mUyd8\n",
              "qB5VcnMfXz6AbueFUsX8mK+kUtecSGSTiP4fD277U/AsFVme+ldTYwkZPLjsbYvgiJEvKtw5tt9b\n",
              "JrdC6HExdIl22jyxh1sCp78ft1bQBiQ5M8aPq5Hz/sJwDpgAAABJAZ9zakI/ABWbX4c6+oek/qm3\n",
              "/o0/DGYGyQESlK3YKfOKve0MgOQSNg8CK+P/AJ5hWxpwHHaygzq1oKvR+zbFU7JExCRoHbSCHgAA\n",
              "AFxBm3dJ4QpSZTAhn/6eEABBQ/nd9NdXKVhbkqkGkoyCIa5mARFFePpugldVL5KmCz8zwZD5S19W\n",
              "UFluggPHzue1bHnCb1BdEml8htsG7QpGZB+p2rRQtR4CObpKGQAAAEZBn5VFNEwn/wAR1ojbgQxO\n",
              "o/9vTSyn9iQAJja2CiRij1h1Yy7wZ3xWlBTzAYpeTVDaGRgQ0H2ZfJDGVj+dp0xDkrPTJgk4AAAA\n",
              "PQGftmpCPwAVkp9GmLBCRbdbtMWoJCGFr4JqVk1mzmoBNf4mDutu8duaxY+GH7FXzu7dRtbAU1Ov\n",
              "XeEAPmEAAACeQZu5SahBaJlMFPDP/p4QAEG6baz9Wtvpm8yWm+htPd/Qk//aUbOVMNKciZogJqm/\n",
              "nHZhoU+U+4jUAm/8BD1+m/lc1nLOXrDFivc/ajj0AZrWmDWQ8ipVs8l1PlWAOCQ7qmoq3Q2Y0Ydk\n",
              "tTWrgP/ZZ6I1Hmw4lWJo7vpAJ5V8pwfbuKoxiOErbB6RjNzjzqOWrdVOj5g6gxmAR7UAPyEAAAA8\n",
              "AZ/YakI/ABWSnqiz9Zo54mkIA0LrSxbtXaTIZhtyDZ6mn+AxiLm2Z3FLwjlykeceeRdBfutcb2ex\n",
              "kGtAAAAAcUGb20nhClJlMFLDP/6eEAA/dkh/PqYkEAHOQXlQsR3/L9h2O2gwVH775lBwctV8WVwz\n",
              "GDQHkp7/9lsBo+aBhwMnuelgnqSp96etVOcDTyYZ4oRM3zC/lXKbxCn6KkJoMzCYm6z9kxmblYsT\n",
              "hvX3y8CBAAAAPgGf+mpCPwAVAp9GmLBCRbdwl98r2Sfoh70TVBWo1lPvu9NiK5NxYdsKWHnX3RRY\n",
              "c3JFQE40/MZfv4MrEAcsAAAAbEGb/0nhDomUwIZ//p4QAD9eobnE9ZCLUU1esJf+LSAEPGqD+ouU\n",
              "DGKcCby54wvc/2aoCKc9J3oDiUAnoVnLIR2X+/mKRGcS0p5HM4fwEZUI19xjQqzUuXNKSgoMzjMV\n",
              "fCyDbCxhAzb3mlbb3wAAAEZBnh1FFTwn/wARVojd0eiJ6wJYOi7kU2S8f2ccy1itHHwMAk7qt0+O\n",
              "albEO7Mvt8o8GmC24JwcrFkabUJM9CF6UHfCoCXhAAAAPAGePHRCPwAVDIcE1wizI+aq7Ppt6QPR\n",
              "Ml2tR0kOSDnTxrRhtSpxbw+icdDiPND/We4lZfGG1TjRtSAD5gAAADwBnj5qQj8AFQKfRpiwQkW3\n",
              "cAZIuV2sGP5WVgRh8h/xBF0pBTcyEii31OtrMcTz2/9eeQhiwNX7ER9wBZQAAAC8QZohSahBaJlM\n",
              "FPDP/p4QAD+8JzrQBOQAV6iUbPLzz6M1ZBscyOE89B7IHsZLTmVw8P1RYJZA6bMANe6qmMhgXO+3\n",
              "sxuw611hAWuE4RqKibK7lH+Eybps77QY+GWJXS8eFSBRplZMV9WD3s30H7SE/o1YlwN8tRNrEipX\n",
              "8jPRmzNqN5rt8bBbFN6z2iUAYAGJt7r2BKHXdlvffatzFsZ4bAGJWz8DRyYx9rmCIugIWbA6fPGi\n",
              "L15CMWGqMkEAAAA4AZ5AakI/ABULX0/yxK4wv4j3NGG2BrP8sS8vLnbKxsH5OfY2kxrNIr5aH12D\n",
              "M7h01INQGvcAUkAAAADBQZpFSeEKUmUwIZ/+nhAAPghgOADip/+8FGyNqwuo2l8wqRnDYUcSP4/B\n",
              "u9ir/zt1po52fjeXcRXiOjEDoCcjjeaQ6DcjmLAmUXdjZG5kMJVQXBXWTf31gtAmLBpiPG0aUFDj\n",
              "otNEfnrGDcriuMwMHM86mvnmGsG7dsazkSjpSKhADBLOPl8i1lxwtP8BVmNpTL0BCW3D4nUa1SK2\n",
              "MNWUBZSCGIpHd3PrCQXvB6OSezngmAP1ceIBOeRQmCoIat+HpQAAAFBBnmNFNEwn/wAQ1ojh5ea0\n",
              "88DgCstgOVisHJHlGJ4GQefyffrPevmblL0Au3k3VRHDu2xcHFy5NX6MdM7W0mTJEKJ3DzQuepKw\n",
              "GwmuO1gg4AAAAD8BnoJ0Qj8AFHyHBNcIsyPmsCXnPbQhGMVczc1jbYkGJZRg2vwG60QvGajn8jXm\n",
              "Vp7H5qnY/+2QYEp323aAeMEAAABIAZ6EakI/ABRoCzG6BqVqRXqTA5fzMxCsVVxogJEPuCjNKE+d\n",
              "DRIT284hWhGgfUOK/TTiC4+hQ5ruY6xtbDK+Ycc73P9uOBFxAAAAh0Gah0moQWiZTBTwz/6eEAA9\n",
              "/n/10v4APyiJWI4rqEyrlr8TxgN3aKTTgqv56rLGhhe9xUgrD/WF2C35MhwTxHcppZNt5F/PlQV3\n",
              "+2UCxOBMdRwmtsYvFBgPpr+QHOr8Bq9NCBE0NvFgluAe1c2IU/UzMjAFLir6JiOCQ6jrxasrUq6i\n",
              "lFVZtQAAAFkBnqZqQj8AFHtfdngZRmMpdgYBJ88QjSgYeXKWw+0/1JysF0Lt/A44CBsOwlN8o4Gb\n",
              "1EuN/jM8pww/CFSfNcc3pRJeMJtTO96dalfAVj+MPLfiTesbDrh8wQAAAG5BmqlJ4QpSZTBSwz/+\n",
              "nhAAPl+R4O4tnbZxUsB/0UzjqaF3l+FPYRxvAAMRNg76KKM311u42y20baECn1Nn9y6n7q29K9ey\n",
              "5564uehF3/e1Dax7SDRICpxG7A9xQcVZDfv6jjRG8YWHcbA6yMD1gAAAAEkBnshqQj8AFHUl/TwP\n",
              "dcY1rKN1U9L4PmtimKeZKwnWxtmJ/+JiNkHuO06H/cfOQflnXbyFsMw/53BFNUSlaJJUhONj9e29\n",
              "GM+4AAAAekGazUnhDomUwIZ//p4QADyepX59uW2Ng5aNABunYseJSVHWM45nOUjnn7MiukRncnY0\n",
              "cPSgPq9vvwzIa/RTjR3b+w+BENh5BSeEr4b5ar9CZJTlxFdv1onlPm3bI+fvP4+NxQu6Nx11OdFC\n",
              "1n9NVUqNsV8qVssTMRFxAAAAU0Ge60UVPCf/ABBc1XRx3FydaXxaN76QsfeV0b5ejCn3jXTBoeoc\n",
              "K9xDP9fIntseRZeu/entN36PMPeOqyma/CmUq9ACtghAD5+hwKB/NZenGIeAAAAARwGfCnRCPwAT\n",
              "7IcPgNeUqgAF1pxO1VT7288tWBTSb4+Y2I9Pq48/If/C8+UloK0ZubRJuKORZIgtmeoHWZexiceC\n",
              "DBwZ+vhgAAAAQgGfDGpCPwAT619+/x+uTEA7it133P/ycIp+CQH7/di3ULHSZ+ovAL0vtUdwdFEN\n",
              "Hnb6A3knSotI5BA8NCRvGHxBQQAAAHtBmw9JqEFomUwU8M/+nhAAPPwnOs4zkAIHTDM7ojFezZpi\n",
              "soY7ekbXilFgkOhwrYFH0SW6It3YM5KZ0KV+/e1yX9TVZmvLaMiBZdsPivine72XOhGqW3cjxhX4\n",
              "nHU1G1AeY2ETpS6hoeNEaOiNo5SgjlkoM8Qy5ccQekEAAABGAZ8uakI/ABPinqi0nk7WFbcCiadD\n",
              "iXrgbucB2Sw2Gxy7E7GPSb15mREq/N+Tjn3RNYn/V6WOZKQAnNzkSowbkjftWdU2xQAAAHFBmzFJ\n",
              "4QpSZTBSwz/+nhAAO1Y6/PprrD5ghAPRoQT4DPlWYfbkfabmdfVFTBT6M6MkTZ78Jqs520RyO+dh\n",
              "HB+7V7s/4EbooNBHbjUWw2iZ/7vUQM/mzlJcl0NnfInMc/obeD/lf3LQ7aLc0GxgA/BHwAAAAD4B\n",
              "n1BqQj8AE11fh8GrcON24SUDt3YSvjHmLNlwwXEpgtQHGli2KIakka7IyGVonvMkrzqp9sGOONec\n",
              "1HkC7gAAAHFBm1RJ4Q6JlMCGf/6eEAA7PpaQ8AKuxoFFSVFo936RNMlaLjMJLX5bWfMdLXv49uuU\n",
              "P3Rs6qIKO02oYPV7BsveocxVAEfSdAMJz1Db2quN4MbNYHdGcdcNuUCzJe/lYZHtcfxcqI+pses8\n",
              "Q0JcF4A1IQAAAEFBn3JFFTwn/wAP40/AG+bMr0dgRB265XQkD0W/nza/gUvtjar30U/DySY6YjKL\n",
              "MdzO0Bts91MAx+j+O3nL0FokgAAAAE0Bn5NqQj8AE10llWAGgMQaHk3kz7Fa5LVSjHg4Bbt1lZJD\n",
              "jX/jAQ6Um37SJS7rQDULtu7i59JivgDDQZb+bn9Q3xkb+ayEffoxew3FsAAAAKJBm5hJqEFomUwI\n",
              "Z//+nhAAO5wnO1VrgA4qw6xXNQ0oIWxdiPkvdtZ6S/PHrr9DAT/6fTcIPRZ8L8nz/LCpNGx3nF6k\n",
              "+TuSIldreVofFAumEQ6mv7495TfT+4mjrJhk5PR9twqxVEAijq5vsPH+MGgXDlEiteagWSeP38WM\n",
              "7s1K8ApraoEJjSp8x7CRrCtpfxDNMWGouuWB+iJuLN457lkzPpEAAABhQZ+2RREsJ/8AD+M7RAi5\n",
              "QzBb+lW39UpME9y6zG2qyY+ktm4p3PhXbe7Ap4jXbqZrLS6U1UVo6oRGUvZ/SokZRxCY0Ve1mObH\n",
              "rfjexHVbSOZylg9PIWfEwuaIIKGCFJ6dgAAAAEYBn9V0Qj8AE16G6NHmJ7VMUYAovvvpbjzTdWpG\n",
              "TlMZI6POgawQ73SEo6mmPjFXOQkcwz83c1SMuy8rkZ31eJO4JsWBR3kxAAAARgGf12pCPwATV/Gu\n",
              "su2G26ob9h1Zr1KA5IjbPFHV+HhXbWGw0cdTxd73pfOL6E5f3cLn0gLiJ8jpv19wrjwRQvT9RYir\n",
              "5d0AAACoQZvcSahBbJlMCGf//p4QADucJzxcvcAOkMHe6g9mNHis+FPkdmVpy9TBrC0vSWItKx9W\n",
              "YfRNYNEzvSd2dkC5/ELbn001qFJ433mWKXp0DaDVJSKdH5j3iSVUb2rs1hfPJG56q6/q15gdRYza\n",
              "Talwa3jazeWMCYnEg5nxdM8eNpDjKTXQAwAt9U09fhfUBUP14xaOPmsX5dMQ7XT0xHvRv0SrmkMk\n",
              "QC7gAAAATEGf+kUVLCf/AA+Gt5iTNF59w1CEg3rGGxXYiL7/fpNwNUEITN2ef8b5v2Ekera4CzGl\n",
              "wE705hyxqrvERZMNmAtZXtQ3sifAAaAIPSEAAABVAZ4ZdEI/ABLUnIgaPCAFt+VBIDYi6oR+rOBn\n",
              "cwyk/qtYm2t6prRZtOPk6E/VLYofOlV/oS4e468sNj929n3+hMSQePUJmXqYbkXkwven6EukGfed\n",
              "MAAAAEEBnhtqQj8AEtYu/mvCNTqM6xyx5i32d6O+iOx1xTgbojD/Cpzl2CM/PpFxHUAFnuYihc90\n",
              "yJ7DFkul1menBDgaXwAAAJVBmgBJqEFsmUwIZ//+nhAAOec6WALSLAC2b86g9vK27fQJ3bXgEZgG\n",
              "CrG481NcTdE4n15vgCwtOutRAi5VLwEDCK9bL6lr3/fHpjTaLVlsxvfH021pyY/7RWeiPk+YAML0\n",
              "icjgNNCvaRsLA8iaeWMW8pWcEbDNWkQ52OXkf218Udb2jdXqW4HLX24PJS/p104cuE0D/QAAAEdB\n",
              "nj5FFSwn/wAPiDi7hGh1HsdusA6ZC7imXBQ9LeSXseWvhhfwA/OlTsfs39NunCzLREP2eZV76W+5\n",
              "iBBi/7GhjJzBiiyhbQAAAC4Bnl10Qj8AEtTiZdwWSX6KoumnF3JA3q0HYiFyjzT+DVfxtrURcTcg\n",
              "LrAJaOmAAAAANgGeX2pCPwASXV+CUSCm1ZwfbQ6ljpyNsZliJyz/wvezwd5mvrHNxN67NT5tFTgb\n",
              "J+fFnLGawQAAAFZBmkNJqEFsmUwIZ//+nhAAOH6LOXxWCuL283NsXvc2GP3Q5a98D57IKc5rHsA1\n",
              "OudeDVI2HSXHhXHpIMDX7gX6r3Sr6KI+/88heEqqEx/TKm+PqjATIAAAADxBnmFFFSwn/wAPLreY\n",
              "vzTRmsjf4s8X+A+Gw83C9ZoyylHs2meQhpsp4qiJ8kXSzqc48a0kuL2gPgtW4rEAAAA1AZ6CakI/\n",
              "ABJWLwPikiEi263aYtQSEMLXwTUrJpPgbhz6axNwQC/mrH96FLX+q1YWyZMbsEAAAACPQZqHSahB\n",
              "bJlMCGf//p4QADje4jr8PAC3hg7cAL8WTidGUCUMg7O5Hso2oRFtdx+fVAPlSQ+3zG8pQgvZ3Rjp\n",
              "9npsHGOw21DyhasQMcgKhRliA03F7b36/82VLMrPVPp/+dyefXw32VhPfgSCGySuGkkNW4rHW+Ln\n",
              "gqaV58nesq096Swxk0dV//BahXnhWBEAAACCQZ6lRRUsJ/8ADy7CFWHUZsxHfTM50AEK22q3E2CX\n",
              "OUGyJkzmo+0n8f19HG8flgbLbhqfKFUHWoAb5DoIg7wuANRHT2IOBWkBxn8Et2QHg54rBqSE7tTI\n",
              "Jv11qw9P4YsGJtwXyaTWoVJKI53PHjoMZFklD+Bj7G8xoK0hN1j3pa91LwAAAD8BnsR0Qj8AElTi\n",
              "9SeAdpbG/gFJQN/mp2jp8O7ZrHvH7B721XK24RDCHlwx4Lt0yvQA4UmHCrrL+fX6JqHAn+EAAAA9\n",
              "AZ7GakI/ABJX8a6y7YbjGtZRtsPCToRl6NngNODZlaO1Kb9yrapkZY1m/m5ZbyYNbrlMz+0wDRJd\n",
              "ex2CpwAAAJVBmstJqEFsmUwIZ//+nhAANzcEDgAnbfnannuZCUwt3nEzczaPR6xghyFZqCRDfzJ0\n",
              "ql9mIDV7tOMx3s+JqsWJ97jIj+yGZkfiG3FatrOZWbpkRc4aKVx3VBnLP5CVHnUvsfUCRh4PlPwO\n",
              "41qIVxLCYmeT/k2MvUl2srlzl2x4JqHTtQ1q2PLnlMD2/8jfHtS3Lj3RFgAAAERBnulFFSwn/wAO\n",
              "2ztMms06TpjdH7GqJ1SeamytB8vZSpElL+d08C1s6yyvOTsumiO8ysLw4PVa30Tx7bZssfs+5LiM\n",
              "YAAAADkBnwh0Qj8AEd6HBNcIsyPmqu0ImXbkNC6U20xTfXyT82oTysqWNUhl6+JdsLBd0x/Cd5UT\n",
              "OLKeQekAAAA4AZ8KakI/ABHdX4JRIKbVnB9tDqWOnI2xmWInLP/InGTmC/tp1gcTRjtrqYnyYLQ3\n",
              "l07PAywkdYAAAACzQZsPSahBbJlMCGf//p4QADd+4jr8PADdb86g9vK27fQJ3bXgEZgGCrHAvkB4\n",
              "kx83Z01KMIlM1oJHd1VBj5pl3akl+cPMto/gawfwulYphLO+Le4umQ0hXnnMGHVbL75Q1D+QAxFf\n",
              "8qSHXZEqAaU6XeiJuXn2BMndPsRq4ItmXThiprsmVUVBUkZjDjn0bVZfcNEa/bVqsbHM7rkQQh2d\n",
              "G6vUt7j8mbQ8FcSzr4d9iGJMu54AAABGQZ8tRRUsJ/8ADtgybRWn6go4EBbx8KQRL4lhjcGd8mJe\n",
              "QS+wMWKcEn4rL0BV7IcpLSpR3MqRg0oE7XqwJ/fJSRcvTYAtoQAAADABn0x0Qj8AEdTi2jIhdu3R\n",
              "VT/NPwpB3Mv1eKahp9UE9eJDKv6SE8+wgXHJB3ySxr0AAAA5AZ9OakI/ABFdX4HaUCOhtUm48jGH\n",
              "O8GcmMPAyPOOyAWcrTGVlQnF9DmB0TLPZbTBW2Fsp1MRRJ1RAAAAhUGbUkmoQWyZTAhn//6eEAA1\n",
              "68cqADrAZZThvv6iGNfsrdWLMc2fWMIO5pN25iBbxV0lyOdp8jH0xasxfNKoW6GIw0gGLSlAqFcP\n",
              "UHGxAGO5+KdmAfZ991KvAnS56QkQ/1A2bJjj+w0xGb8yiwF2aRd1qFhr8PIOC+sOrcPm9e8vpxl0\n",
              "4cAAAAA/QZ9wRRUsJ/8ADn63mSD1Gm68sL2GaRdjWtGPAZc9Cpc9Uy84lHLooFuRK4ZJtK79N2pq\n",
              "G/uRdWJ0f5kbRj1UAAAAOgGfkWpCPwARVi8EpGRU6zgWhR1EmmItukc1cx12VK2eNQrrrWDhH8s5\n",
              "G5IufMvv6tPlJjoFwY2TMCEAAAC0QZuWSahBbJlMCGf//p4QADXz7atuNIsALZvztTCorbt9Andt\n",
              "eT5MiPZ3I/ExgYy+kjeVPWgO87DnHy3fxRkvsxCbncEOvEkbW5eBq9XmPZbosF2084F9GU4YdUVi\n",
              "s8fxZnmcJ3CGPN0IYRr1pFgV0wjptnKzkQ/tXbpnoPfMgm2YfOMsaLDrCsH0/NfkG9j+qtMRKSs3\n",
              "C8m9fG4HjMggUBiTtl3Ga6X3JyRi7KzLMxrT3MAQAAAATUGftEUVLCf/AA6AMm0V9moKOBDO8fCk\n",
              "ES+JYZB7BVqiT4Y7Fh3itL4zYnDCQ/4wj31ZfADiD/681uleu0UumXlnZuQ4oysUu0ETerKaAAAA\n",
              "WgGf03RCPwARVOLvK5Y3XFwfCmTk4LqsrkoIPLjon4XOWUND4WL+x95kPROIGW/1rjb2EHpJX55H\n",
              "ad1UzEfSuIzkFDyZxbWuADBi8ohZdjDozTD0UqytjZET3QAAAD4Bn9VqQj8AENYvA+KSISLbrdpi\n",
              "1BIQwuIbQFLEMzQvrPXJcZpbaFfrDP+Hc3JrHqcGk3tBgejxMsx1/lpoCQAAAHxBm9lJqEFsmUwI\n",
              "Z//+nhAANKvQ3xACrsaBRUk5/zp3BWPaEcCY3oS3UWe5kgj1SmW8J9BXQv8rmKJsImS929Fpfeb/\n",
              "A6KdKOMBY7nT6EHzJC/ZDwZdH9phVg4gUIHzGyaKvgvLzXlqpuQPO9lnryMvXdXstHhkJ0LmmOOB\n",
              "AAAAQ0Gf90UVLCf/AA4rT8DEq5Zll2rir+IJmpQUD20Wvlc8kwwvtQbAg3o2ZrJBat8Jb/t3B3Q+\n",
              "By3p9IfB6SSJPh7z/ykAAABCAZ4YakI/ABDdX4JRIKbVnB/3nUJ72hydyBGaRJkiSTZo2S79J6is\n",
              "ODBeXS+rVAvMWyprBnA1GFsRhj17MmUxeAa8AAAAtkGaHUmoQWyZTAhf//6MsAA1XvZwX+ACL9wF\n",
              "Gw6sjYDZl2I+S+urfxfwLNazU6+33eqPooi/esdavESLl1uWp+SyUP3Bx8Dooa3yIMorbYOxNedd\n",
              "Y1Xi0WJhCBuvYsEN1igsD0XXnNZq3Mo1hiF548e5N1TNGNvBYVyanzdcQ6jYN+zwRQtuPsyC1gJ9\n",
              "C+1QyQgWMm2xD9YgnF3VmrkvPYCNLFp4DKcWsBIQ87SHH3JdMyGjCwj5AAAASEGeO0UVLCf/AA4r\n",
              "O0AmdgAITKkeOeSGCHMW+zWEqKMqTepUs4tzlU5peDzhJph7aAodZvKEvIvnaEmjjeGxhHr9LxAQ\n",
              "rDyx4AAAAEoBnlp0Qj8AENTi9Sk4J9x/jdfAo6WQitqJ3e0Jr00hc/0NwNeXGHNOxgGC4YUM5SPF\n",
              "Vi8RlZCdWq4Kym0ErnsVniV2SMVCBtZOBQAAAFIBnlxqQj8AENf+upsvdNcoAuoFrUwUv8oi+OzW\n",
              "XL+SJYdAeggV6Gs8/ii2n9jSM5WiPmflfy4Xod6DVocPZkZZc7dSL+6P6GEYCwKacLwS6ZuBAAAA\n",
              "mEGaX0moQWyZTBRML//+jLAANQ6oIRL803xPNFgrKvRx8r3foX7oXrbsFZSBxQWBRBNsiYyToPI9\n",
              "MCLLXTExwiQsLVSCtGsmB4i3MEGMkOcBi50EmlWdDN/LlkN1v+GHbFrRxN+VmeDVfU6rpzC76laj\n",
              "my7K8gRkkXw31CX8GOp/1PrDDXO3007RZu+UbWnCW+mHWNcG4wZcAAAAbAGefmpCPwAQVb7awAqm\n",
              "DcV72AuXYI7t3QJwk6fKzIfK5BgMbCkXjj6Jgbao3xOvtglUGB3FfgxZTnl0m7Wr3BaHDtECGu/w\n",
              "hv8NxWjbcun2QhGD54bGHNanU0vqNg+wzF+PvWBnBH+EPcEekAAAAKxBmmJJ4QpSZTAhX/44QADI\n",
              "nkAL4bABHWvS8USSvxk6K6rFYWn/osqCk4Vb31fLPGNhiVNIJnzyNPBAmsg+Nu9VOkYeAE3QW9xR\n",
              "bBiI/Y6jvgKfOE33nEtMtCH9wLUGUp/l1xAYkTN5ZMqU9BZwIEHdp3crlsR25DOhw/UEPPR6xacs\n",
              "2ZUg8G7kdOdUOGYtSL40nWMH+JeeyjSkvvhwOcUI0JNPGZyLmXrZkJeBAAAAU0GegEU0TCf/AA3T\n",
              "T7UupOF4w3u5YROmwcPzZpAQgb1YJeQNiPLiD9lhwkVUVH4WBb6vJLpWzEEP7tYRif0qNxsNW63L\n",
              "t+wqmMh/+9KtuL0loCLgAAAATAGeoWpCPwAQXV9wzg46OBUPltHhMYx0ul6A5JO/2UWlY/n0JVjQ\n",
              "F5V4vcwRHL1HL1TOCVci60sokYfLC2oUffVrHeBLCfBPH2qWPmEAAABaQZqjSahBaJlMCF///oyw\n",
              "ADQe+dB2RRpIyegvzMv5E75kwGeVVHsL1aZ19e5uSnQcvniCPl5vMXXu8bbL3qGiCX+XvB0f6zCj\n",
              "3oJyIEjrB4FGJo6RR+3nvM0IAAAAdUGaxEnhClJlMCF//oywADKgb/27vh4eijyEAGdyLdjyBfpa\n",
              "OZ37LJ48k4d5ogXRErH3DNLrEeqfDtTLQTW9OPGrJ67oX46DSM+udfMfSrdRe8V+ykuLD0DupiRA\n",
              "ZQKKSvdEdJ+dUN+RLaUc23gRp5SPFU6k3QAAAE9BmuVJ4Q6JlMCF//6MsAAyn/7qeAGfIVu/1d+a\n",
              "JJSdqWdG9EVqThhE/WkxW8B6aM9qtwNLjJ5UYhI8Ev5mrYG/u1qDe5q9dNqpwYJGlmDdAAAAkEGb\n",
              "BknhDyZTAhn//p4QADIsDfoABdSEzKL7yEEvpN52+zqAmn3l1AqQ7g8Yo3eHhyyKBE3AoJUOZ+ht\n",
              "cmFtUkpig8kTgrwzt96Fp5O0pWSuN40uDK440RTClYNC9fWNuog/2oqqTYGVTlEiWrTv0Y50R0Ox\n",
              "ce1/8TbGSL9TATb/Wju49Oxd7tZfH+mjiUmjQQAAAIlBmyhJ4Q8mUwURPDP//p4QADIrsT0AAuoT\n",
              "4wB4Wv+eGPANN3IVRJX2zQxBKvhnjCjqjy/4BwbldhKXJgGv3nW5o/S9zsP9I/sGDkjHwaREZR0f\n",
              "+x53zVMaCY7gJdo3YLFRrlzVJASZ8Wj+DvPSG8LL/sWHWHTahbNwhuS3nBHQoMvX3ymkVXyBUwAA\n",
              "AEcBn0dqQj8AD9567Gn3mQ6Wvq9375vsgi1hSGGyJk4Ciyymi/0B3JU1a2X8L3FWgWMfSD6k/wki\n",
              "dVoxAFkBYZM5xaFWKsBTQAAAAJtBm0pJ4Q8mUwU8M//+nhAAMiuxbSgIVMA3h0+8MKVNpcq5uhwr\n",
              "VTj6kA2x8632NGRTBPOrbv6gWTneb/cb6pnw68LQXZHFZbJsKExbd8fPuaQuwKQyIfmXxhOuubyF\n",
              "BcxFgGlNM002yAU1q1r9YD4noxcq/hs4+aMlwK6XSZquswv28fD/zG3ku+V/YV9OCPYYlD2HJeE/\n",
              "iOf6aAAAAGMBn2lqQj8AD958ReAG5rbow4O4TXL3a4bVLPctbJgXAr2dW1F4BPX8uxPPN8qBgCh4\n",
              "uoiCWy96cprkbnyONSl1jtyF2opyHkT9U9yoLVacfpQR7EQOx7WFQj91JBnl1Z5jwwMAAACiQZts\n",
              "SeEPJlMFPDP//p4QADJ65+sKJpeEhTgALn3j3eeL+aXltMGg+dWor0F8fTNRrvI+EcqE8CdMx8Yn\n",
              "aCZgoUVRSAmLAwXSKG+TsfIyjNVkl71x687dzfQRBXlH5pCUGV8Ym+Ohy0SEoQ9emLeluWEdMtKs\n",
              "oryjnoEf1DXQFtRmZ7GLvszfgp8E7zGzpVhSqxGuyY35vnqNZkK4LS6aKHjAAAAAVgGfi2pCPwAP\n",
              "3/qfrI+XBQAtn3hBdiS0WK2YAdNp47BIqnXikLpc1rg+MTIII74ySyn7YW1XZDXwGzmobNOfe7Oo\n",
              "Vh14flrWIXY4vVd7QztlYKLFFwOaAAAAlkGbkEnhDyZTAhn//p4QADD3BA4AJ2352xBIAshKYW7z\n",
              "iZuZtHo9YwQ5CPS3jgJSdKpfZiAJMezBz+dUOkL4aK6MXTffFKLyN6M3pDfaV18eWdWjQjL8tlzd\n",
              "rvWpgFSbVPj7ueeBtXjJnftvFrppz/8ueWCiGePhu/GBXRsvqGicCHOdcmlkfxQeJlhANahCFM/k\n",
              "Dg20wQAAAFRBn65FETwn/wANL5Bu6PRE9YEr1lK/Iwc4jKT7Z/yeSatXd8JskIFhEeMALqh6cZKf\n",
              "olSwEqVIlmf/0Lwt87Xjcz99zWw2qTT2v8WVWRP1mSYnp0kAAABBAZ/NdEI/AA+Fg4HREBnorkTa\n",
              "4pfUSQLS03BSWDV6+oA1ACYTiks0KtyU+OgpCVRYjwTo4EkCwGUKvBJCRBTLDUkAAABAAZ/PakI/\n",
              "AA+GewPikiEi263aYtQSEMLXwTUrJpPgbjriwyMksZ0+J63GhfCf1wFCaN3lypoOXIVDruGY/MeY\n",
              "HAAAALJBm9RJqEFomUwIZ//+nhAAMTwnO1VrgA42/O6FXUFwpEfJe7a8AjMAwVY4F8gPEoLgDssj\n",
              "7GqyaNu0A4ClKdZ7WKqKJ9lSgNDwun9NjJzodxm3hM1Pec8kGZPhsIntPlve6u+lnJ1yrOHg/rZr\n",
              "zbklVmhWmNbxgoirWdIlsRf67XbGefOlLVtLKi+eaCNZj0cQv2wcnFUmUaOrgzkcTlfBTnGxLvCt\n",
              "77Rj6aflnfSi7nGtAAAASkGf8kURLCf/AA0wl3uvjF/8S4VuWtamUoeg0YOZ8/QDy8MtuOWl8/Ie\n",
              "nGt3w/zv0dc1AYMkf6NnKV4AJ36OZLYjIsLgJJ3pS+nBAAAAWgGeEXRCPwAPjHCW6htgJpP2ZJ1C\n",
              "McoyFNkl+M6GlSTIEdVG2+BunDNVOhREjPJagjtVgFID5XzBVoi9l9iQ/8igz/ruTuNzMD0pxC5b\n",
              "7fSZh6hQniLrMj2j4AAAADcBnhNqQj8ADzObIZcQI6G1SbjyMYc7wZyYw8DI847IBZytMZXsU6Np\n",
              "UXdy8sV5SvCYEwTUspeAAAAAVEGaF0moQWyZTAhn//6eEAAvq8cqQblLfpdTSL8ZtvhB4eG8QeUd\n",
              "CpSZFSFAAtNdigtNNpJLz6wYN6rSDSMGiO8eKjltCZb0H0hPPHcXtGRrCq4SQQAAAD1BnjVFFSwn\n",
              "/wAM35Bu6PRE9YEr1lK/IP4WpmtBxAbao8kAT52KVgnyehJsu3PSjiIXDm3yMzNe9uhMOhfGAAAA\n",
              "NgGeVmpCPwAPLnsD4pIhItut2mLUEhDC18E1KyaT4G4c+msTc4w+mL3JiqEGS85XpOCbJqXHJQAA\n",
              "AJNBmltJqEFsmUwIZ//+nhAAL/wlrVw1wA3W/O9YYE1wpEfJe7a8nyZEezuR+JjAxk+nfGQZpXQn\n",
              "MFUssfk34A3hoKBLk4Nrt3W31nyNoPrOWVb/zXPEswM58WRZshFl6/YpgIO3wuZjFOMAwgcgjlcw\n",
              "yJB+/NOuE/XDDrw0Qu2nfyijxrJzsQenv39llJxr2IWyC2kAAABKQZ55RRUsJ/8ADOCXe6+MX/xL\n",
              "hW5a1qZSh6DRg5n3oYCOezCca8qtO0Trv+6F4xtIVadrmqvaMSly8g9EHwQ4GZeU5pCAVqrXOJUA\n",
              "AABdAZ6YdEI/AA8tg2/9yxuuLg+FMnJwXVZXJQQeXHRK8x2k4WxvCGRDiBV1xlgK7teXTXdzEMim\n",
              "oCwnQT3sNWUqXgcH+Cor8GS3vtHYsNE8Isr958dG9DwJjBsfQyOVAAAAPQGemmpCPwAO1nsD4pIh\n",
              "Itut2mLUEhDC48qZtVokC6YoRTRMTZmNA66h91M214LfjhwwaQ9RNCpS9SCGz0AAAABxQZqeSahB\n",
              "bJlMCGf//p4QAC510N8QAq7GgUVJPxVY7aGd+opNjntvOJiBJijFwMv7oddR3MK2hAU7KUMz7s/R\n",
              "tutb2scIf9YMg51Sg0aimRspOcaP0yBmbkHpy3tjDKXxtXjbD8CWy4MZeQVrbHEFx6UAAABAQZ68\n",
              "RRUsJ/8ADJNPvbnAYpoI173ps0m1d2xkK73EPqcmmHTPrh9Hk0zL8RVepWDIYcAndaa7ockH1h5y\n",
              "stLaUwAAADoBnt1qQj8ADtubIf2iJtWcH20OpY6cjbGZYics/8L3s8HeZr6xzcTeuzU+e3wjgsTf\n",
              "pbr6AMtFYetIAAAAfkGawkmoQWyZTAhn//6eEAAuv5HEcGYKE79ugFD/PL214tZ5NCrsX48frFtr\n",
              "8nI8Ca9SMfQAjrupTF0I5zAerqznH+o8S2/F23bwQWW7BY2InJrluCCGjxhVoF102BQaNRS5Q12t\n",
              "GUORr7wZGBBMW02G62Hk48n7137bbIAIHgAAADJBnuBFFSwn/wAMkztG9qbFnm4tXfACQI8Ef6Iq\n",
              "6kV2aB8/oT1BztNltozavRHZySZ1QQAAAD4Bnx90Qj8ADtxwtzl+J7VMUYAoscPffFgDY/aK6LHB\n",
              "EnXhJItQYb9DLSV/VqqRJoDXew4Jg8m6Euso/WeWQAAAAEMBnwFqQj8ADtf6n6ykU1Yqry6fmHLy\n",
              "Vrb5eNsLlIbv2agwoEZuFuECmhBRFWpXVh+Ge39CHofqzjcwGVPwGs1d2mmBAAAAW0GbBUmoQWyZ\n",
              "TAhn//6eEAAtVdD3HADcaaU61TCWdCYo06fJlSeqpsSQeAIIxEDUDpkpO5zyFCHMQ0aDr3ZtXHIA\n",
              "tESIxvp5PWqcIP1tKDEl76v7UceSENjvGXgAAABMQZ8jRRUsJ/8ADBtD61anthUAH4CocDpZQMff\n",
              "qUBi7mpQiWSTGIlBcQssdGFmf2llzcMcU9ui8NaNRpi6aIb2Cjlc2L6RUbeNOplTbwAAAEIBn0Rq\n",
              "Qj8ADoObIf20AABdW5WyMX3V3DRB7Opd9sovNpBvCyALGwJXBEYLkIA4bOgf4TILuJ+EUya2Ksf/\n",
              "dVR/itUAAABiQZtISahBbJlMCGf//p4QAC2cy51otIErxa7XuA6rg0Vxz/G09gM8KBkCrMm5bXxT\n",
              "czVQ5b3a/LsDlD9RSu/AAlTzwluKVIJ49Sskp2n5I9JAPwmEY6wgunziXldWYuNnRzUAAABIQZ9m\n",
              "RRUsJ/8ADD+QZpbMNnGgVbElq8B9dn/HsgisnTT9HcLZ/XfkRasT0Z62puGYdFnikhuH2mU1uE1H\n",
              "toYF8mmsSegJuDphAAAAKgGfh2pCPwAOfnqqWfrNHPE0few/zk2kXt/Fyd0MU2mG1e44JXv3iCpG\n",
              "LAAAAIxBm4pJqEFsmUwUTDP//p4QACxKHEQsufwGzIQPSitBoKysJdEQhCLIkf9ovEQ2JOOBdfqJ\n",
              "QXr5ktAkdvUO2RtIi1trJSNnRNr8N5OYXQWg7ZJ3ATAFupH2K/aOv5aUqf4oUekR+M9MaMIQWEru\n",
              "Xt8HUyiXIRNFHoc64K7qoLNzI4EsqzKQfZu6kOsPCgAAAEQBn6lqQj8ADiZ7A+KSISLbrdpi1Ibp\n",
              "ke9yLOAtYPhHd+8FZPuFyDdQ1hNwsXATGAZB0qRfU+z7azZSwlicYlghIjXbMQAAAGlBm61J4QpS\n",
              "ZTAhn/6eEAAsNdDfEAKuxoFFSUJI5mqYCv0TC9riQNX/VLSDrNbtD/ksHv3rfK0J1zs5NTZ9lBcN\n",
              "K6YTG0/wff0LpYVpXmWUoHWcJqysDbgJFmwhDgHUu2YYfV9C75s9uOAAAABEQZ/LRTRMJ/8AC++Q\n",
              "cnQdyf/daEQUokkN+bwxgbc+04WxOmt9ZVOm2DLn4jZlBtu8gxXG3wGMNl16EAGk0P2R9xRy5RQA\n",
              "AAA7AZ/sakI/AA4mexcdMRWE8GVFhkPSbsYdqVAVIX32dOflTofOPjzCT+o/v3aLOypSkO+ZsCZb\n",
              "thpGl4EAAACAQZvxSahBaJlMCF///oywACzcy5Aq4dvdfWblAAggj6LbUhWR1jNuejM6ert8fh7X\n",
              "BsLPBozEbdwxQWyXHKmzDBWj3HEjXf1CpyCKUqcvn+JBpbFJjtVKg04LTVvOm9qNuO28vHX/4/Np\n",
              "CF5zXhU+qt+JnOV6+kzJoitVU14/oYEAAAA1QZ4PRREsJ/8AC/M7QCWSLt5DV2JB13b48ZZJILF+\n",
              "DpYmHzwOGsLeCd1Ib4TToQSUd+ZGfk0AAABGAZ4udEI/AA4scLc5fie1TFGAKLHD33xYA2P2iuix\n",
              "wRJ14SSLUGG/Qy0lhY4zbxGIySC+Il3rPaz/f8zXT+b28uuE23PL/wAAAEABnjBqQj8ADif6n6yk\n",
              "U1Yqry6fmHLyVrb5eNsLgSGxDzIptRvAjVRQnWYzYfGigGwi/HqzcCX/QPKhslYylC8MAAAAREGa\n",
              "NEmoQWyZTAhf//6MsAArdRrDcAM3uiTBuZKAkLq1LqCdMC6gOmK1mYaoN0uZf78z18hK7WWFcszV\n",
              "pqx/y54JNOWNAAAAO0GeUkUVLCf/AAuiJG3AhnUTVPU16HPKftFKalUi2p/zi8mII/ALx8mhNdgK\n",
              "1tYQYtfqbvFErcMdPMjAAAAAOAGec2pCPwANz++fC8CSVP12eOLVy6YcrPsXTDtXUR9t3F9v++An\n",
              "H4B42s8LWIh02WnhlAjHNBjAAAAAaUGadkmoQWyZTBRML//+jLAAK3T+deTWAIj3AetG17buj4Ow\n",
              "5lfTpzmKg79VAgVPeDR5jjFtJjtMI7OBgKKvxalaRhNsX28vWzSGlrZmm6mEomCyyKpjLHOBTsSA\n",
              "BUfv5AY944sh4MC0gQAAAEEBnpVqQj8ADdRPuGSaDEJs3VQCCG75apSeLYLS5PIqJ43exNLTZZgt\n",
              "1EocybOrwyDBqAfSEhaRXiv0i1qsuG2HdAAAAFtBmpdJ4QpSZTAhf/6MsAArvMuSEncNc/1v6OAD\n",
              "rxvQOpeX8kgWRdgavt1293KTHdNoiE8obIxFuMPAUzjU+y+CPq9IPWw6LSWkRqL2pnQbFtlIk5Cd\n",
              "aRpONmrvAAAARUGauEnhDomUwIX//oywACpqIBTwA/Jt9a3aTflIYd57+wsAnqdteMPUmgc6UnJM\n",
              "QHvcqdk47MFc5adksynjZ9nBM0FtqwAAADxBmtlJ4Q8mUwIR//3hAAJ8rNhR4MVHU6KqNsnwytms\n",
              "ErvTXAG2z3NFVI+MefVLg2qNHUkx9Eaff9l/VlwAABSmZYiCAA3//vbw/gU2O5jQlxHN6J0zH78V\n",
              "uLo0N73OAAADAAADAAADAAA0mNB+M3dqw4YgAAADACYABlBA8DtfmE9dW7Q1Q14gX6bfCLIlzG2I\n",
              "jsnWaj4b7FROZAv2bK4NP2S7J86XB5TZIVMQL9y2PU2dVIcfiZTchvxrkzXKVvLU72YtAKeqoJ+/\n",
              "9XF0REPYekwHc+0QBhoIMN81XJ08wcbUMHtsK5Kz+PaNrtKOsmwjNXzY5aICXRgo/tfB3sVntB/7\n",
              "inwlYfHVBjtiyKvkDyp/XsHRdI3qp2NOAoHSNswip3zAn4MdchPIw/oxfGMp4VLwfw1BUmuLmElu\n",
              "Zecpv2jAU+wCNMj9Mv84fh1TbANayZpa9Ky/4QcmVlr6KjA4+IkGZY60MLxcDzG+NHjrgi7s4Xtg\n",
              "/Y0ghmwvQFM1LaIMx4gaCK1vKCcc3bUygAAIdtk0NGZadoPmCh5pGjHML9c78QZaKQ4yOrnI3Box\n",
              "nsXlprFW3E7yvrphBeTGKjB32kdz9wK1Ijza8Z6BXkFt5y7fyHPfsFV62qVAEQlKexTw9FWjdamA\n",
              "vMHvmu76i8rgCSjRe+4U+7MMMlCpkDIWRCNEVK0j6oztHQvntK0RDdM6Jrg0AMkngzdfWfAm3yR7\n",
              "CdxIiADsLq5UG+62pzFcXu8elN2wlPMOdxAK39gJ1XIxWXdeIG0lMIss4X6tqagZnfHEpva30FOJ\n",
              "74CoyK3biMq+8gdO+5+UKAiWDyWVU5dCLQeAvVuAPTIRTlrQ82yXsH3j0F7uKWjYpC7/AtzrFNDM\n",
              "NsB8prdvUfnmK3FxBCQMEIaG/QrdZTIVcntH7k5dF7A7Nh2AwEXAsr/c8qfvDzTWAO127rUNL1Zs\n",
              "HJJHXPt4nyStO4PVxyK/3INCCcQlTl+OEKAt8I4K4kQvFkxOXGGdywADsF0FLHKuUAFdKnim+4xd\n",
              "P/UGCGpyqoB49x0Ny5/wnO5fwbBQa3Hx2hOapRoOwIQYEjb3YvOX0Wj77btYktLvZhcogvi5uSfH\n",
              "KroaljkOfI/GftcTN5ph4J5xk1DZHZAr/j6cydU1gu+j52M/w8UjWz7SV2Cq/z+NTpMNYyzTELdp\n",
              "rivZvvOvJ3kZYGLDpjfqKdL7VHUBJ+B/2e+08V4WDlnfB0pXxNzp78Pf3YI5K6K4kUv7CIo8vv+8\n",
              "Pc9P1nLAojctqPi2J/wankfuBZ+LH1F09UiVxkuW+sQ4GGDn0FFJRzYnMpz1BfkmflHzGAA2ifSH\n",
              "yVHi/NzIqsn3WkYFHiGVMS8U6av/4C5YAYOXFHtV/x3/dV9u7W9bmJW4iXAdGB/AOi0noE/9wZcv\n",
              "wFTSH+lOas+47LpuKX3axngvC0DKgvEbgmv3I6bHkqL4eAykL852MlEBOcwEemojK1B5xT18feYJ\n",
              "qfxp/UuqSVXgkNsPVEpm/ylHx0G98PGBp/KxtfVIaAMvkqxsTWPwTnQJft+0Dl1th2VrOS+fCTNZ\n",
              "F9P6AQr7lL7qxFHh9SJSb+Mk5qIDGkMH3z6YFfFxh5c+7wNxRPJHzFQRj1bogDwzIPCyCTt723No\n",
              "RD7glRITeGt3gVL88d70J9Lw7bnaR3NCMvz7mVSmHIKyfTD+4ArpOc68dKg/9oAxN+3VEjC3x4IB\n",
              "5Wjo93I654PYqCE1HNI7mc2ZcLDv3VTZ3RvU1oLefnI/jm92uTW/qAAB5rmkVGwTO+3ztHeTeg+S\n",
              "JNDHWgs/RdvM0uMwZ+YFbNMonJ7Hj4xTCA20B579kUktKCnimuT2dlKH/i8zhFVobqpv00gcC5yH\n",
              "WDg81Za3AyMXiGaLh35PrsbYdMhKll8wEv6JmZDRl4Ks4QN/Z6UJvHWGwSLUE+ZuVLF4oMhJ6WFB\n",
              "P9TpFRvYYno3Y84Of4CtIsC97smmAv7LuNG9+A39baZ7rfwmlRI9LS70CTc9Z9C8DshGHlSzL6Cm\n",
              "SH9JhrCrsElVoy31825esXU6ElUkrSZ2wkCNjTqiJCuhnrJfX6AK7BqLe6EwuFFmV9Ku/gAT6+UO\n",
              "RLP0gF9zvePf9Svwg6j34rLRf05A/IFEzz4mts/tzHIbSiZtBa2pzwtk30tlxG07D6IDRMv33aVK\n",
              "kM1yuOl9GyAfjUIUZrA1dFxr9B43XmTv9gmweiGJge2ugWs39Oh9rR4bpUbG5iEXC266dOwZHORV\n",
              "N3bStCeJeSIg7GoMaG1AABO6bnqc4Zjx7+M2yBTjpNoXyFDd6bHWe2KxM7gzSEySgf+y64rVQJpR\n",
              "MViTo9j+nIPYOfjcRPAxPmgE6LuCbvmVmGcXcuNNow7NSmViYiKYQFIsFWeY0Ue4AlxQnqcfCWu8\n",
              "cuxrXoCSalFtBkTibxRhYZa2nqRVFEFIWNnFmYAzO/v2S+SsdYtS8Z+PFJ2p+HiuodmKtj9BEPtV\n",
              "R4igBoAfjof16U8xHLap9+lZcI7ZkRIOue19HgbkK7YVuDdaj/fAXeosFs0e7BlUFHHdjcnCnTS8\n",
              "Fms+3CWxbAE5ql5iWaXj7/iWgPqurEczqBzFPiErrRdfHIw4szWlVCuoXfTUsd/1B0peNr8cZwkM\n",
              "lmCFreVOtYGt5ML2kCBuYp2bCFGN1geirpE209M8VpbTnOKefOmIxDvGjKz0i9FHNnPq7ij+7RIM\n",
              "0S3Vy26DPOrbAWG6MBHezRhC+mJtTkdORRVquqkoq9xTnH/5gqVYpJCTcdpBaew2epQoMmhMFWxi\n",
              "icMRKlLbf5FWH/8tttJyFZmcFmnKksBFBpb52uNsIvWFdaF0P60iZ4y39i0gStn3HlFVSmrm+Jzf\n",
              "0uqmYLeArPmX3YPKHsPtcDle+wTXkcZIFJ/uvwACtQKyZcvzJxrnyP4OmmkMy7XKgIo5qjT6d7jk\n",
              "5wHLIgawfqaxpfAi1VDYDHKEpNHtpFY+nwN9GK6lcUnAz2gdBNTmZJGiv0t8CQy6kEEPMrxRmNI8\n",
              "8rKjXsGNoXLqNVhsGazeOm12qfKJDbUQRb1+SvukS3/6FClGg2M93kbDFet5ixsTxkkrLdT91ADM\n",
              "i/AX8gkQN4aWsSV36rTrEJBgRBfXKgQyMXVtNadbEL9JcsOZF4iV9vEMGAAQSRc9xc83uh+bRuAj\n",
              "fAuH2rH/fPD457kIet6NEC+YvyoANeYXrDF9pns1ynK1bB/z5kMjVavql3n8t9NqbYHTJun26Bm1\n",
              "dPRisLLD2/IWV4JKLWqUHW6UFbznWDuGpe9aVbh2omhPlmWRf+EGIsixS1c7q2DU6PSnzBcEYrYZ\n",
              "onESketvHmv/bpGaEfMpB3SxLvcCcffHnZpgeqQfHN7n6LP7COBmabQnYFeZ2pS8bsSvI0MoF0V+\n",
              "gpFA6dj6tdbIsyIcHTaeLatpmBHTiKK84s/twDmNt+nVo2UEB7v8CM+sOeE52o1x1ff5b0j03E1l\n",
              "jTPAwQMR9EvOsXBbrWIvvqmtYAAAziBBy0YbEzwGlPJNteLvrCUo0gfbPHG2TORtkCvy57+fSvOI\n",
              "bx8WtGiJlkEqLrJ+O5zdD30yCPbhA5McJx71EJ39XRpixR/Uo27RJmJj0kt9rs3tTkojtnLt1eyk\n",
              "wsaHZxMci7KkY2ARpT2KWVVk79HFX9v3h8ZQCe0BKi45kl4+nm13RPd47JWg/41OseahliPAUC16\n",
              "vvxiYMKHX7unMd06A0H1sS2LXeMYoJBcwvxUKHAqs+aK+GRAlVQn8AbJp5iE3Pt+BAlBI7edw1hn\n",
              "g2KrL2HHdUAT8AsPRS09d/GT6nRd+lK+4phnOUVWtt/c0jQBBef4DG2KZ55wVE7KxbWKpbgLRd6i\n",
              "SDNd2FI/ooMpgDuyjxU9jagBIyfxA9VxLWGvR/0iYt87QDvgqtlWBY1q6M4amJKD6AANkg/XNjBZ\n",
              "XnChIXJ7gxgxjm60KfJ8wcowhpJ9/MpOyvYUHB6TuFGV7u7BNphCF9+rrwZY2z8tkkVXEVYorUnh\n",
              "Dt4LBSs+6zoRRjX5vwpmRy+EgwPxdTaoCOCRAUIpxGJ6J9dLnqJmu2ABmF7oY6c4bsJyrBhan66a\n",
              "0k388CV1LXhcePRPgZLn8YqLDiNF+yNlSbyVexIVL1VfXkWdHLwnoUuW8AVMS/duh8k2FAgz3Y4t\n",
              "yyZbJ3TVc1bSAEFIz6QMXj/+bMl69Evc4ag7zK7Fs0v7P6ccyZERdY3nrIMha4YfEjAMdPPb3J/8\n",
              "B1EfvKB53tU+xXqiIBh8m61EA/x0V12aeeAhye/lMVqtQ6aQdGRGMIABEfHjJAMuBgG8oz2qa5JR\n",
              "aE9Aqeb95bArL8TbB2LoIT0ylAPt7CNVsgpFLnwqQccW161mnBqacVMDOS78VFz7Hsp4HDoSMVrs\n",
              "yp0AdSmo0QAwQRJyVqB5Gk9Sg/LB6v3BRQxnudttSYhAiQPOJYVN3fw6H13bMvbhApwYIId6BdIz\n",
              "R48eGdEb/bpKakwq5/2XiIexDb2fZr0eERrLfOpFWHlh0Ewk5d8V84qbAUpHha+uphzMjiV6daH1\n",
              "Tl4w7Y21Uxzypx4HDP4W1MblXTvV7pelDIcydNXu2Bljfu8TZEnwh3iX5oi0Bm94iZHr022IAAGn\n",
              "d5SAInwJWoAHBrMme7XtJ+3/InGY9iZ6lg5KRnchIg8ptV/i2I0p3dWMWxi+CZxN0xZPcdBet7+P\n",
              "wshdqu5UA3vZIJBcHXLYaEQy3ddUP58DQf/DB26BFS6huulvzPNHEnB5VoX6GvbdooB1++L2c+0m\n",
              "McPABeUWCAhSTYIv4CfJEoiy4mfcAV1JlPz2WPmXizdYOtBZp6KVcY0j9MK2OieiogB59nxuMbZ6\n",
              "E+m95va9uiDekOMxrhFrkPxFAPOxJ1L3qpix04CaQjujCFMZJTB5qLsBuKlvWUXFK2vmUEBhQizG\n",
              "SexcztkgratvrFU+P6+ZQpWYepLt9caFwk2tm98vH/GltZlCh+Sne1XSvuk+4u7RWpsVRIBF73a5\n",
              "HtXmEN8u5yZbFzTZBry65Y1v53vjTKibkDPutV9irP1jArj+ss1e8LhHq9mwHHUMwIUQd+5ODqKO\n",
              "wrAnuAmkpUMsbWPwowgEp/Pn/XprevShwEY/wPArEVFHJwwssvfpTEbCa1NjSb0zo1SNbyMWkwMm\n",
              "114Tf5GhT+qc79aDPOMn5Q4/C2L0uLnQTTRLL8PgMQk09IzH6KeZIrqm0NATPipJ4jNS3rrvGW0W\n",
              "Siaw6UUqSR/eUr5NAsGEC6orKwcc551J/7EmeVoKJuZsqLnIv1OjaQktfu0+EHRDyhSyLc2fC+xX\n",
              "hUkpe+z/RAFq/06xWbBgfcZtewhdJmYYTjJsPOL1NCNMcaFURYEaRT/eJiHSV7nMswU+U1LItDqC\n",
              "23sESCcBx3A/EwkDvAryWY6PXk0LGvBsqKAXP1ghclTMhMIvmL42YFw+uBF6OGXiqhcfYt24PyfN\n",
              "hkAuX/TrlJoXQREn8uHrIh8v//qAtaP031xH224Eaey3CB28q+JX7+HFCzxsEV5X8JyEi92cMX6P\n",
              "Ik2bNRKPK4fC0kSLIKSUygUSsAogOZUY2UFmoLETeSBXFFDfb7ge2ChFlu8LL3yV5S/LnlwbRmN1\n",
              "r2+XbQ+hzzQrzZM1mJsVUvqQCCT3dW5fGImGeajJ51fyvcP58A7TdQ9cQbdGHUDUfBjmwsfdea/u\n",
              "eQsr+LvTdE4sdwfsVMJ2/+tMR9aHuuSfxGOiTJR0Sed462kEJQ7bQswMmk93RjFAsWNFa06plz7m\n",
              "2y4/4M/TzkzdSLM/LE8Ahg9T3f/51GHNZ1FpymG5orOEX5E40d342Xvh7cyQk5b0ib4rptTud2Xd\n",
              "POhCVafuONav7fd/8+jabZ7+gwMN0eoP2WyXCy+qd2y37KXg7G/4N0hGDgpTXiPuhthg2paQ3sn1\n",
              "gq4zP74DkYshFn/Aj12O2WlssMkIiPJuMviko0WvB835spq5qw63fN8EtLRaSuN9bzP+8o7em+rm\n",
              "IbHrNBSncsm3X+53rXcm+PD3dwRPLKDrgwf8/J1WpWU4edq7881A0Zu/VGOsP5BC50Q2YZCUH1Hc\n",
              "eD0xJAD3/c3+QXQEJDql3TfU4a+d11myXAjKeLNYYalsJFLBen0vDlwbzphRXPZmo4Db21XqJYOS\n",
              "4OPcO5f1S2LGRKCuS5P/gEzC0TwaUBQ+PDa49j6Dc/073NKL24gTBnVHm7Vjv6jcevDuYOVS17rv\n",
              "uwO/ubB+c9LEP5bo4P+3bv2UCu0c6vOfUH0Eh8/Q25rw1W5aiPew15uRrA1G8TWg7JI0GgPO69kM\n",
              "w5o4O3KTc2MurS4jPLe9Bld0Tn/oK621QA1xkqTyGCwJxsdfDS2XNIWNm7Xt/Bt6zH8kubgsGDnG\n",
              "qIEPkc8t6LRb8XZMztYZzUrDhFyBNDpli5/nQOOEMNrZYZOsRV26w9kRXiAQ9iqmGhMuwYPDZZgT\n",
              "QOpXgc4XScsj/tRKymtZbnofILLdDmwOpwMIoMl1R1stKWX8TavKVWPwxtKN/7SnbZMpDU42OvhG\n",
              "Wl6q8BQo+6Zp1PG0zczhFKPbiCPiPBsoFe1Wp/tZ3obsp/jnuLoNeylovF50VsB3xBZpmpbdnexs\n",
              "P42dv/9KsJrp3YFh1vYhZd8oEza0OEhG3q0paD73MawD587YgXtprlWV64DIwOrNZgstUbwkwmZ2\n",
              "9yxQhXRudr0DZ/F+k+2VT5Gvw4BIb1ckZu5M2xFHAa2hlRoug1aygV9T2hJJEbNetbGgPICcjueW\n",
              "tl/soAcKORseQlody2x7o4h8qoklFTj0dbXxZg6aNi7Apxt1O+NJwBV53PpK2W6EC8ugAf24aM1o\n",
              "N4lsvdWETu+qUtU6mKRg6uYVQoghmIMkxmvOKhlym9BrznKrwK+1iHrJ/uyPcwcEEYwATpPllEWG\n",
              "m0aP9qgMMRaQZZcVMACh4QbQ6vLWk0ODnrv4wbOLOG8ojugf676BoZjuKvAFtyw5JRhJxGIbzJRu\n",
              "wnVrgTUjKLjae+m1iSX7VrxEGQdv4UayZ+eWNtlXFHSBCapR0eKLP70QFUuSrgW/9sNloWkhSuhW\n",
              "kaWLb8nU3F3yMqdZ9j7Iu8K292Am9A37G9GwjwcAwJWPIMBcYfgUzzS8vPqq8FJSxq630BHvTy0i\n",
              "l0PONxK2mkGAO3AAN5G3GA9pAAABHUGaIWxC//6MsAAqWaUV+rm1ATsbD7BAP+PO2h5d7C+RBljT\n",
              "ggrIkMntry1E4Ni1pWSUJhn8ym/tweV+jkzYnUVpi7mBu/bTaRd28K3T1rxD4NrKW/jkxcNkTeQb\n",
              "ireQSowwfLROC6TdzL1gsXbTPnEivJAR8aYrd3JT7s6w8Fc06Y2FojCTOWi1ACUucOgVHxwPUX6U\n",
              "CiQTC0947LqmO+fMlkLLgP9StJAdZMCiDRizF4IgMWf0yllz6704epl1FQCaKYaGNyELqj53vgiY\n",
              "oeaEyOAWkfc8xOg/qfjpS1kwrERymJfF3bu9hustPwxocETnoQQE2TMyoeNKi+S1Amb5N2Dcz2bK\n",
              "4Cpe6/ZDrHJldqTZil/3aiGiSgNdwAAAAG5BmkI8IZMphC///oywACpea424AblutHucfGG+K57+\n",
              "wsAnqdteMPWgZH1gsif57ktwomEnoQ/ln1OiGndKLyhEh6vfrf5hrAFQrMyN/mG3s6SqqirW0cVt\n",
              "OBCGjNyStW0csi2aWjiT+UlmMJ/P/QAAAFBBmmNJ4Q8mUwIZ//6eEAAp9g36AAXPpQzwrEfeiyqr\n",
              "cjjFegSJcA/WBy/rBXyJj0vShOZzRgDx1K0KVYaCP9S6YN9Gp0OXOWbdr1ZnJ7ge6QAAAItBmoVJ\n",
              "4Q8mUwURPDP//p4QACo77+CbfWCgR//w4CMuZ5B/Wr7y9D6Wqu1PsSycOapadC3aF/qc5mrYwwIj\n",
              "UIxfJtkAG7wP7ZB+zlyXCF7yJajW/mxweVFHuce2RqLXg7UY9NyGlUwVoNRzwA6LTbNY6KTBqGr5\n",
              "ODwc64Z3ZmY/ATgkyqXepfPfknkRAAAAQgGepGpCPwANgTL+nge64xrWUbbDv4GMse9UV+d7Btca\n",
              "OjIxl7QbwWQYKrm2G8xK/BjohaW4t6WGFOzzGkS8UiAYsAAAAFBBmqdJ4Q8mUwU8M//+nhAAKNWw\n",
              "qQa4tY4OQ0mEwZqAhU/fJsj0nE8gnxcFhYNx8Qm0LUAuYPK2ceiUe4TaZqTBHE0xKOsupJpAdB+W\n",
              "H+8jMQAAADsBnsZqQj8ADS/vo0xYISLbrdpi1BIQwuU8i6T0f4TLyJKLyLpeWwdfyP5kF6gDgJUa\n",
              "V2SfI3jTSvBLwAAAAGFBmspJ4Q8mUwIZ//6eEAAo1dDKUVWaAAdo2QeQ2LFOj2O2gwQxBUmUMYwy\n",
              "45EeVwwyGzNCLZ64P7tnA52DsxKox7piHYfpdNn8jTmUzaSmLyG6XL+8iYQc9cLkpt9m1RgRAAAA\n",
              "TkGe6EURPCf/AArv7R4swxWJAdvVyyh7LRS+NGl/5xJd7cbjE/I7YVgwU/gYWRWqQ89XPc1uZx4a\n",
              "37VCTGgVACzcFV+nQJw+BGGwgAxBwAAAADoBnwlqQj8ADTRPv3+P1yYgHL0tT+mkcawt3QCH8U/1\n",
              "lW9a7l7cpI6mjkRkjxfvG6ZbHwMaS+VDIVRdAAAAskGbDkmoQWiZTAhn//6eEAApHxeK/DwA3W/O\n",
              "oPbytu30Cd214BGYBgqxuPNTXEmPI+1SSCTNrlEmrVv455o5wTgd9jRxNm6vMttRIq0QXxB7drrA\n",
              "2ESdCN7GtbbemKpoGHiRNclO8Ehj9oC5bcFqHxM0iruw4hTGgLKyiNouWPaeJBUiO405t/VwNum4\n",
              "NIrJvJkKtTdU7pZvZfIKAAMQ4nbd97Rx65+ec1+QFvuxXydOAVMAAABMQZ8sRREsJ/8ACxMke6+N\n",
              "2oGQ3Qm0GpYzXbp6pmGdCSh1OnuioudC+RaIKh8Mm2LN0CJdc8w0OITGeCeqkP+IURsv4DZAjNkz\n",
              "xrRYsQAAAC4Bn0t0Qj8ADS8FGQDYUCXSt1+atcUDzXmqIzq9sb4L/eghqERh1bGE8AUGIHHBAAAA\n",
              "PQGfTWpCPwAM5E/BKJBTa0DDdDk2cVFhqy5lyEhteX24j0Wkm5r9AMcpOn5qJ1xKbVpvxh9lhO8p\n",
              "GKv0AyoAAADHQZtSSahBbJlMCGf//p4QACfBBA4ALqE/8IlSuf+psvRnOkMZR5L2k5Vcg+tz0yJM\n",
              "SfXYCZBS02iR2U/M6fzCPe4DhzCKgxFWb0MHbIM+GDN3bMK4U/FOJ7JOWjfA2ZKmQTSYf/18jes7\n",
              "bth77aiJA4HasncdDa3tu9+b50rxAH/9nZYL5RQ3Lip6N0AWcWRQ4fpwKG+VP0ylqV3GCPvO3SEk\n",
              "AVfu0xqXQwJUQSVKcc2nimCl67ZVwKim4/bBbk5Jn9jeud6LaAAAAGFBn3BFFSwn/wAKzU0TlACA\n",
              "ninTtl6tqcE2/L0sZUokM772W1AeBwmbUFUUZ/2w0sPSRHBkO4KDkpwzNEpatKmRR1JR6ip8bSb4\n",
              "wwXE9ReBM1kBmRnhVtGFQzoR3O51ub5hAAAANgGfj3RCPwAM5OOCa4RZkfNZ+o+w0sYSPZ/ptxYC\n",
              "MvhJBpKJImS5u/nXo6F+8EgtBnLtYA/ZHwAAAEsBn5FqQj8ADORPuGSaDEJs3VQCCk10b8VmzzsV\n",
              "9XRwywwAK3f30RKfrbG6ZUVd0WUlegH0MGywUQaLfOqftK9Ng2xHpTMprbUVguIAAAB8QZuUSahB\n",
              "bJlMFEwz//6eEAAn/wGRHEQ77gWE1cK7TbZhs7gADr+rj8N79KffgSivdTQvjiKYn86xtNJEFVhN\n",
              "PYhhF22pxUVod0t/zgH2AEntw6l+B5YxBjrerKKiHzypgjNBPhpId9zlYk+9G6bwfaCk/7GAWLBP\n",
              "fwiBgQAAAE0Bn7NqQj8ADOEy/p4HuuMa1Qi/W+GfNbfmMEbyJ+tICe1RS1A713r8zV2Mya5r44KR\n",
              "TkBnt/hBIwoSAbIqaHEja3osQKkn4GPNLuBQQQAAAIFBm7ZJ4QpSZTBSwz/+nhAAJqpR3MkAEaIZ\n",
              "WHyKmhg/d4fsnrPRzqICUn6ZJH+iZY34R9Mu2ecIcMjddfkh42bWn+s7fsis913uqHxnjTvCsYS0\n",
              "ewR1u2b/pKwP0gt5Sp676083HXVH0nvh0utllk8Etm8qmSsv96pegGJzdWfoEHEAAABZAZ/VakI/\n",
              "AAyPnz0gBnjywTeUZMhrYPPCrAIKi07dX8vd2BZ4MPyXgbhjksw4N7RIxZ06kmY64S4x3GAjlx+o\n",
              "MI9HW+fSIPUDao2SNSgL/S40yEabAYmYc0AAAABNQZvZSeEOiZTAhn/+nhAAJqH87uvTS36XU0jA\n",
              "Oj5lvEqn4G9vF0I6/6wY6gANpBg+gRRWC0B/DLIq3qPNPiHgskQYW04YhYlc2ePNISoAAABKQZ/3\n",
              "RRU8J/8ACoIkbuj0RPWBLFiUCnsvmgBJcyJzm0AsUXXv93Rf5D4Xf5xxJDI5QKU6eCvISRJ0Mg2d\n",
              "xWJibMIxdQk4OQwMEfEAAAA/AZ4YakI/AAyP76NMWCEi27/ulYK8rgjh7YkH+GKR7/6X+QcQhvpk\n",
              "pJhGblYp9JaLzt18IGfdYrMgBmic9SYMAAAA10GaHUmoQWiZTAhn//6eEAAm3yeAulAC6hP/CJVv\n",
              "Na5/wQAz8nvnd0HO7J44Yg5LUxZiY3N70V/R8w9i+yUMqbhqecVy2knxlXfu/JkR8tNzsfsPeauK\n",
              "IYYspNzbSXOj27IiLIKhLTN0ioPvRpAIAvglHgeLyqY0ByCWctBoUbyxBUWPGcpoWVCWm6kqNnaY\n",
              "mc2whZJIQUHWe9RFnwYx6whLUh+sJmG/2tnHgSfgClfg42zqgxqzjly1rCEWIthwqkVoQmReku3O\n",
              "y2HOvNp1ZrOnDYUPRYNCAAAAX0GeO0URLCf/AAqDKRtXqkKib262oYSuhk8P6urACvPu5UZ0Avhu\n",
              "3tj1BM0YG6GNE6VgMBGAMm2RuBvVtS4GKMrekFL6wKF0F5X+gBGnb3SghZCo+Xfs9eg+AmVgq/I/\n",
              "AAAAOgGeWnRCPwAMjwUZANhQJdK3Y0SGyaRSKPDQAsEzmGs4QG7U5fFqw5SFzXS1+KV3SX+pf1Kl\n",
              "oKomAf4AAAA9AZ5cakI/AAxET8EokFNqzhAg9MVJIQFw6xKJ0fpdm395D2v9QSAQTCBeAxekZcos\n",
              "I6eSqffJpPoSraDZgQAAAMJBmkFJqEFsmUwIZ//+nhAAJaqbDgAnbcr4ITGQlMLd541R7+PG6DEr\n",
              "n0LPH+3ArhnmQUicg+PMvMxgaInw0CI5ougjMf5ciwRR/AV2yeeVX2q+QDlziQLr/HzFxw+q0WDI\n",
              "8viHxBmIy8f6wY48dBU9ogVEa82H1jW4dFgAJkvgtgy+R1Krpzv918aXyPz3Kl9rCA7ZGsj26Hyo\n",
              "+3Cq7N4RLRpOTVBxlx3163F447mN/C5Tt4xLdcPbgcx9edCG8vCDgQAAAGBBnn9FFSwn/wAKPUq1\n",
              "k1v8yBeyejwH3d+6HsF8YAFrkqLnjO0Lgtz421hcVujCO2S+SNrcXKc/nD7WoX531uXjDiM4t3WN\n",
              "Bjm+SNvckoBsToxHHAXERdjvBM1TWhBAMWAAAABaAZ6edEI/AAwZonF4j5W2+sptC+/whsRmkAH2\n",
              "in+ESxWM4/L+iIOYIKe0gAq89XOO4Uk4LCMFKanAs0zsrWKjQi3Iuuw+d1wYDmOQvNmaRx7HlUwK\n",
              "9sXLY2i3AAAAVQGegGpCPwAMRE+4ZM27GtuK54d+NxehOUmKhv09NJ4H06FLa0aKMtnvhPHxn+do\n",
              "duupGXwhZmpVqzPNmx8+ikn5ZobdoN4Kuea+9dvQpkHwctm0bMAAAACQQZqDSahBbJlMFEwz//6e\n",
              "EAAl3yeRJYvyAFeojJmNd/tJ59yslxGGWxyt2LOZo7F91w6aOQFO6cwkCsZfa5NYzIOORCozTKnq\n",
              "izU5hnskbfOOyO6tSpagbjrkOoWlePKWpmCywVCrDReaWlsSbXqIXq/pRkf1VYZwm7/rIjHtuLUy\n",
              "8co2ktJ4pqTDcr+GCgGVAAAAXAGeompCPwAMQTufrF+Zl5wADf7fwsz92mS9WG8pRDW6WjFVMu08\n",
              "fHvY1MOz83RnG9XQuljtAZKTg7uNbyrEaGfRCceeGVMmLjwciDoOKa9+gPnitngErJIQTRCxAAAA\n",
              "sUGap0nhClJlMCGf/p4QACTcefWHxBoAE6rDrGCsnf/tkJTCUdl9kiGzNeul3Qn0HvmnuxZY4zCH\n",
              "cLo3GiPQP8E6y4qNbQcRnv8SkvuW6EsxfiL1i0tegjoFs1MJwCfQDx2sRaxrX8j/4XOcL/8WwfkN\n",
              "rJ+p50L4gSbLxbfLvCmkdluupwyCa/HSaBXCJcKjJ2fsgkeCWwn0TiJ6aImKqDw2pIdM52oPcC/I\n",
              "jjT1hy4N+AAAAFxBnsVFNEwn/wAJ9Uq5MUUavFgMCkQk1B4bW0gp9NMf1ZwivnwilMJiIKrFD8MG\n",
              "se6wqpTyeNEjslL0wL/WABXn3cqL+2XjB863Uekch4yCGSxBUsQPOCSvFKzBiwAAAEYBnuR0Qj8A\n",
              "C/Tjfv8frkxAPLvB8oOcQvFIOb2I2VnpHkCxQrwMvOjUL1vp3BEXzWsbLEVEcTdkd+ZElRN286pO\n",
              "fLNBCYQNAAAARgGe5mpCPwAL9E+/f462J1fLqXNGPwRMWZuwImRBaNSiEKghFfbtI6ymNcmupTL8\n",
              "yNly4UpfY1HO7/sN6fpY3U0w5yoWwVsAAACQQZrrSahBaJlMCGf//p4QACTfPZTqh4AbrfnamFRW\n",
              "3b6BO7a8nyZEezuR+JjAxk+P3/u7LA5x8/+ECAk1as25W84xqxl5lCgJzsgQmiMYaUY4PKkhv8Yz\n",
              "Z5Gx5iOVKpLooxE2IDAnZnzh8zkylksjARr9XNEzQ0cs7seAr43uaPiSPhjwhuP8JOL7Yfx7ABdx\n",
              "AAAARUGfCUURLCf/AAnzJHuvjF/8S4VuWuPtszoXuLm3zD5KOyzY5C+3VUoh31ytKHVxOC2ErMxu\n",
              "vnRbQ3cWquRcO7BhFobDAgAAAF0Bnyh0Qj8AC+7XsYheB9ADc+YSulXiLkFQrmUe2lNuTWj5CVCH\n",
              "94/vlR2T9dK1fbpnFcvPlOsc8jaakMZjHDqqeezE4fID5ZOcZHrm6fPvhD4Z67gP6tB6K/Y8gysA\n",
              "AAA3AZ8qakI/AAulr8DtKBHQ2qTceRjDneDOTGHgZHnHme1GGBn6ay3tm2O2unMXuBsCiEytXq22\n",
              "YQAAAGVBmy5JqEFsmUwIX//+jLAAJAC64ymfQX9hqQuZMCYTaDuFNOZ4rzvxY/YKNtEHNOiCvxJ0\n",
              "2hIRE7fcpddAFrwoxcHe/ADEjbDAylkoD3z75skrNqbzVRYFExAg6n6bXPDoxv7StwAAAENBn0xF\n",
              "FSwn/wAJq0Ru6PRE9YEr1lK/IP4WpmtBxAbaykrKZyu1ac9bOfmLsJtR0HnnMK1wrnAx5IdnK1kV\n",
              "1mKxlVBBAAAAOwGfbWpCPwALoU+jTFghItut2mLUEhDC18E1KyaT4G4c+msTdGetwYs1p5+7gec2\n",
              "lokzfXojJjiF1oE3AAAAr0GbckmoQWyZTAhX//44QACLft4AlfiBWtndMWMiw6zEAwtMx6bi4t4r\n",
              "yx5B2oaB1R057x1NDv65YP4FZpprF9WxFVE4VX1QY0gU5tOmrGtoLiI+vVurCxBRZj8E6ezVa/Yi\n",
              "x34SzuvLgPRc2IeW6kTP7n8jxo9Nla43bdu9RP/Wba1/kM2Hyfc9Pby3Xvql0TvJovmjluYxh5L3\n",
              "o66fHDlGCPgBaFL/qqZ52BtgD/AAAAB1QZ+QRRUsJ/8ACa5tDNFIXwAcWmuk5Sm9x8zm/p//cjQo\n",
              "deAPonwPyj6ALKCml3FEaxYNfncHqyPzmmX8J/EakqfIoLAtrPB97vMcpv2eAL7VWMo9WwdzITmo\n",
              "d3Vab998yDgoH6AcYgw8B6uq5+2vgSWaGuHHAAAAVAGfr3RCPwALoGVzD8JFAsT7spV/hQkIXMY9\n",
              "/qVJtZn2+JwQQmnkTfZ73jhotDats7OS21E90RHxpMxUQ6XWB/J0v/F8MwYBu7H2Lm2qxK3clCmI\n",
              "uAAAAEsBn7FqQj8AC6KS/p05HAwcU5rM0FO+xnGz27eDb26IcnY7CtxwBXHEuOBVLaaQ9eYw/+l6\n",
              "BzhtQMz20bPjRAAdKHa4+XKmnEsAj4AAAABhQZuzSahBbJlMCFf//jhAAIZ7WwrAETCr0MtH3f+v\n",
              "5Yr77Db7WXV3TtGcgs2mLRhZObXzY6EA9Lo1QRpn4LTxnkJUZkcQmaZYkfOnbSuPdoxDI2EfC//J\n",
              "jo5S5qdSj5t6QQAAAEdBm9RJ4QpSZTAhX/44QACGe1sKwBEx/ws0AUR7PxzEP2Er7aUEbuHs00Wr\n",
              "i/kvMrlTiGjYZxxlJJLrSQQt/SUzvwhRod8M0QAAAFdBm/VJ4Q6JlMCFf/44QACGe1sKwBEx9agQ\n",
              "x66XAidYfsJX20oI3cPaOtjk9XxOEwxcACR7OzKpPI478WL/FXSFjLepDB/NhSuDAgnMnP1E9Xbo\n",
              "dgRg9IEAAABOQZoWSeEPJlMCF//+jLAAIxSO+XADOZ8Kpxuf1Bd3rhoEF6Wk8ax2wHuIafLgDpVu\n",
              "oRP0PAQWhIIvF8muUYNsRbLGv5W3KUv+O+RSEUPgAAAAMEGaN0nhDyZTAhf//oywACL5SkNUSnP9\n",
              "INgnfN0yKFQE8U/sKiqgnCpTsnBaRet4QAAAAFNBmllJ4Q8mUwURPCv//jhAAIdxLy4DDlqoI0ma\n",
              "qDnPlR19FfGcdp0EfvcQSoP6cfR44BsOR1kJqQhkdi3peruuyyrAUByUqAuWtJSK6hULPZKZgQAA\n",
              "ADsBnnhqQj8AC1qS/p4HuuMa1lG2w7+BjLHvVEpMSbbO/5dvzKNPKUx4QBY+xgRJrvSELTBBeR59\n",
              "nMOJZQAAAEdBmntJ4Q8mUwU8I//94QAB+p9aCZDwXRPQUNVpVRgRDNqps1H1heWfnNnypqMgdA3V\n",
              "E4bHVMGCZVh8IydDLisjOUG/OPguVAAAADwBnppqQj8ACxFPo0xYISLbrdpi1BIQwtfBNSsmk+Bu\n",
              "HPprE3SNtgtxYpncsGTX/D+vgmsRajm5CTDUQUkAABATbW9vdgAAAGxtdmhkAAAAAAAAAAAAAAAA\n",
              "AAAD6AAAHkYAAQAAAQAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAABA\n",
              "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgAADz10cmFrAAAAXHRraGQAAAADAAAAAAAA\n",
              "AAAAAAABAAAAAAAAHkYAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAA\n",
              "AAAAAABAAAAAA2AAAAH4AAAAAAAkZWR0cwAAABxlbHN0AAAAAAAAAAEAAB5GAAACAAABAAAAAA61\n",
              "bWRpYQAAACBtZGhkAAAAAAAAAAAAAAAAAAAoAAABNgBVxAAAAAAALWhkbHIAAAAAAAAAAHZpZGUA\n",
              "AAAAAAAAAAAAAABWaWRlb0hhbmRsZXIAAAAOYG1pbmYAAAAUdm1oZAAAAAEAAAAAAAAAAAAAACRk\n",
              "aW5mAAAAHGRyZWYAAAAAAAAAAQAAAAx1cmwgAAAAAQAADiBzdGJsAAAAtHN0c2QAAAAAAAAAAQAA\n",
              "AKRhdmMxAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAA2AB+ABIAAAASAAAAAAAAAABAAAAAAAAAAAA\n",
              "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGP//AAAAMmF2Y0MBZAAf/+EAGWdkAB+s2UDYEH5YQAAA\n",
              "AwBAAAAUA8YMZYABAAZo6+PLIsAAAAAcdXVpZGtoQPJfJE/FujmlG88DI/MAAAAAAAAAGHN0dHMA\n",
              "AAAAAAAAAQAAATYAAAEAAAAAGHN0c3MAAAAAAAAAAgAAAAEAAAD7AAAIGGN0dHMAAAAAAAABAQAA\n",
              "AAEAAAIAAAAAAQAAAwAAAAABAAABAAAAAAcAAAIAAAAAAQAABQAAAAABAAACAAAAAAEAAAAAAAAA\n",
              "AQAAAQAAAAABAAAEAAAAAAIAAAEAAAAABgAAAgAAAAABAAADAAAAAAEAAAEAAAAAAQAAAgAAAAAB\n",
              "AAADAAAAAAEAAAEAAAAAAQAAAwAAAAABAAABAAAAAAEAAAQAAAAAAgAAAQAAAAABAAACAAAAAAEA\n",
              "AAQAAAAAAgAAAQAAAAABAAAFAAAAAAEAAAIAAAAAAQAAAAAAAAABAAABAAAAAAEAAAUAAAAAAQAA\n",
              "AgAAAAABAAAAAAAAAAEAAAEAAAAAAQAAAwAAAAABAAABAAAAAAIAAAIAAAAAAQAAAwAAAAABAAAB\n",
              "AAAAAAMAAAIAAAAAAQAABAAAAAACAAABAAAAAAEAAAUAAAAAAQAAAgAAAAABAAAAAAAAAAEAAAEA\n",
              "AAAAAQAABQAAAAABAAACAAAAAAEAAAAAAAAAAQAAAQAAAAABAAAFAAAAAAEAAAIAAAAAAQAAAAAA\n",
              "AAABAAABAAAAAAEAAAQAAAAAAgAAAQAAAAABAAAFAAAAAAEAAAIAAAAAAQAAAAAAAAABAAABAAAA\n",
              "AAEAAAUAAAAAAQAAAgAAAAABAAAAAAAAAAEAAAEAAAAAAQAABQAAAAABAAACAAAAAAEAAAAAAAAA\n",
              "AQAAAQAAAAABAAAFAAAAAAEAAAIAAAAAAQAAAAAAAAABAAABAAAAAAEAAAQAAAAAAgAAAQAAAAAB\n",
              "AAADAAAAAAEAAAEAAAAAAQAAAwAAAAABAAABAAAAAAEAAAUAAAAAAQAAAgAAAAABAAAAAAAAAAEA\n",
              "AAEAAAAAAQAAAwAAAAABAAABAAAAAAUAAAIAAAAAAQAAAwAAAAABAAABAAAAAAEAAAQAAAAAAgAA\n",
              "AQAAAAABAAAFAAAAAAEAAAIAAAAAAQAAAAAAAAABAAABAAAAAAEAAAMAAAAAAQAAAQAAAAABAAAE\n",
              "AAAAAAIAAAEAAAAAAQAAAwAAAAABAAABAAAAAAEAAAMAAAAAAQAAAQAAAAABAAAFAAAAAAEAAAIA\n",
              "AAAAAQAAAAAAAAABAAABAAAAAAEAAAMAAAAAAQAAAQAAAAABAAAFAAAAAAEAAAIAAAAAAQAAAAAA\n",
              "AAABAAABAAAAAAEAAAMAAAAAAQAAAQAAAAABAAADAAAAAAEAAAEAAAAAAQAABQAAAAABAAACAAAA\n",
              "AAEAAAAAAAAAAQAAAQAAAAABAAADAAAAAAEAAAEAAAAAAQAAAwAAAAABAAABAAAAAAEAAAQAAAAA\n",
              "AgAAAQAAAAABAAAFAAAAAAEAAAIAAAAAAQAAAAAAAAABAAABAAAAAAEAAAUAAAAAAQAAAgAAAAAB\n",
              "AAAAAAAAAAEAAAEAAAAAAQAABQAAAAABAAACAAAAAAEAAAAAAAAAAQAAAQAAAAABAAAEAAAAAAIA\n",
              "AAEAAAAAAQAABQAAAAABAAACAAAAAAEAAAAAAAAAAQAAAQAAAAABAAAFAAAAAAEAAAIAAAAAAQAA\n",
              "AAAAAAABAAABAAAAAAEAAAUAAAAAAQAAAgAAAAABAAAAAAAAAAEAAAEAAAAAAQAABAAAAAACAAAB\n",
              "AAAAAAEAAAUAAAAAAQAAAgAAAAABAAAAAAAAAAEAAAEAAAAAAQAABAAAAAACAAABAAAAAAEAAAUA\n",
              "AAAAAQAAAgAAAAABAAAAAAAAAAEAAAEAAAAAAQAAAwAAAAABAAABAAAAAAEAAAQAAAAAAgAAAQAA\n",
              "AAAEAAACAAAAAAEAAAMAAAAAAQAAAQAAAAABAAADAAAAAAEAAAEAAAAAAQAAAwAAAAABAAABAAAA\n",
              "AAEAAAUAAAAAAQAAAgAAAAABAAAAAAAAAAEAAAEAAAAAAQAABQAAAAABAAACAAAAAAEAAAAAAAAA\n",
              "AQAAAQAAAAABAAAEAAAAAAIAAAEAAAAAAQAABQAAAAABAAACAAAAAAEAAAAAAAAAAQAAAQAAAAAB\n",
              "AAAEAAAAAAIAAAEAAAAAAQAABQAAAAABAAACAAAAAAEAAAAAAAAAAQAAAQAAAAABAAAEAAAAAAIA\n",
              "AAEAAAAAAQAABAAAAAACAAABAAAAAAEAAAMAAAAAAQAAAQAAAAABAAAEAAAAAAIAAAEAAAAAAQAA\n",
              "BQAAAAABAAACAAAAAAEAAAAAAAAAAQAAAQAAAAABAAAEAAAAAAIAAAEAAAAAAQAAAwAAAAABAAAB\n",
              "AAAAAAcAAAIAAAAAAQAAAwAAAAABAAABAAAAAAEAAAMAAAAAAQAAAQAAAAABAAAEAAAAAAIAAAEA\n",
              "AAAAAQAABQAAAAABAAACAAAAAAEAAAAAAAAAAQAAAQAAAAABAAAFAAAAAAEAAAIAAAAAAQAAAAAA\n",
              "AAABAAABAAAAAAEAAAMAAAAAAQAAAQAAAAABAAADAAAAAAEAAAEAAAAAAQAABAAAAAACAAABAAAA\n",
              "AAEAAAUAAAAAAQAAAgAAAAABAAAAAAAAAAEAAAEAAAAAAQAABQAAAAABAAACAAAAAAEAAAAAAAAA\n",
              "AQAAAQAAAAABAAADAAAAAAEAAAEAAAAAAQAABQAAAAABAAACAAAAAAEAAAAAAAAAAQAAAQAAAAAB\n",
              "AAAFAAAAAAEAAAIAAAAAAQAAAAAAAAABAAABAAAAAAEAAAQAAAAAAgAAAQAAAAABAAAFAAAAAAEA\n",
              "AAIAAAAAAQAAAAAAAAABAAABAAAAAAUAAAIAAAAAAQAAAwAAAAABAAABAAAAAAEAAAMAAAAAAQAA\n",
              "AQAAAAAcc3RzYwAAAAAAAAABAAAAAQAAATYAAAABAAAE7HN0c3oAAAAAAAAAAAAAATYAABBmAAAD\n",
              "OAAAAVAAAAK6AAABGgAAAR0AAAEZAAAAtQAAAM8AAADFAAAB7QAAAIEAAABzAAAAWAAAAP8AAACT\n",
              "AAAAaQAAAKwAAAB1AAAAsAAAAHsAAAB+AAAAvAAAAKUAAABOAAAAYAAAALUAAABMAAAA3AAAAFwA\n",
              "AADwAAAAZwAAAEIAAABnAAAApQAAAFQAAABPAAAAswAAAGoAAABYAAAATAAAAOsAAAB6AAAAYAAA\n",
              "AGYAAACnAAAAVwAAALkAAACbAAAAuQAAAGgAAAClAAAAlgAAAMEAAADlAAAAfQAAAF8AAADoAAAA\n",
              "igAAAGQAAABcAAAAvwAAAGYAAABSAAAAVwAAAQYAAACRAAAAZQAAAG0AAAEmAAAAfAAAAFYAAAEO\n",
              "AAAAcwAAAFIAAABRAAABCAAAAGcAAAA/AAAARQAAAMYAAABwAAAARwAAAEkAAACpAAAAYgAAAFMA\n",
              "AABLAAAAdAAAAFEAAABFAAAAcgAAAEgAAACfAAAASQAAALsAAABbAAAARgAAAEsAAACJAAAAQgAA\n",
              "AHAAAAB1AAAAYwAAAIAAAAB1AAAAoAAAAEwAAACeAAAAUgAAAEIAAADJAAAAaAAAAEoAAAA5AAAA\n",
              "sAAAAE0AAABgAAAASgAAAEEAAACiAAAAQAAAAHUAAABCAAAAcAAAAEoAAABAAAAAQAAAAMAAAAA8\n",
              "AAAAxQAAAFQAAABDAAAATAAAAIsAAABdAAAAcgAAAE0AAAB+AAAAVwAAAEsAAABGAAAAfwAAAEoA\n",
              "AAB1AAAAQgAAAHUAAABFAAAAUQAAAKYAAABlAAAASgAAAEoAAACsAAAAUAAAAFkAAABFAAAAmQAA\n",
              "AEsAAAAyAAAAOgAAAFoAAABAAAAAOQAAAJMAAACGAAAAQwAAAEEAAACZAAAASAAAAD0AAAA8AAAA\n",
              "twAAAEoAAAA0AAAAPQAAAIkAAABDAAAAPgAAALgAAABRAAAAXgAAAEIAAACAAAAARwAAAEYAAAC6\n",
              "AAAATAAAAE4AAABWAAAAnAAAAHAAAACwAAAAVwAAAFAAAABeAAAAeQAAAFMAAACUAAAAjQAAAEsA\n",
              "AACfAAAAZwAAAKYAAABaAAAAmgAAAFgAAABFAAAARAAAALYAAABOAAAAXgAAADsAAABYAAAAQQAA\n",
              "ADoAAACXAAAATgAAAGEAAABBAAAAdQAAAEQAAAA+AAAAggAAADYAAABCAAAARwAAAF8AAABQAAAA\n",
              "RgAAAGYAAABMAAAALgAAAJAAAABIAAAAbQAAAEgAAAA/AAAAhAAAADkAAABKAAAARAAAAEgAAAA/\n",
              "AAAAPAAAAG0AAABFAAAAXwAAAEkAAABAAAAUqgAAASEAAAByAAAAVAAAAI8AAABGAAAAVAAAAD8A\n",
              "AABlAAAAUgAAAD4AAAC2AAAAUAAAADIAAABBAAAAywAAAGUAAAA6AAAATwAAAIAAAABRAAAAhQAA\n",
              "AF0AAABRAAAATgAAAEMAAADbAAAAYwAAAD4AAABBAAAAxgAAAGQAAABeAAAAWQAAAJQAAABgAAAA\n",
              "tQAAAGAAAABKAAAASgAAAJQAAABJAAAAYQAAADsAAABpAAAARwAAAD8AAACzAAAAeQAAAFgAAABP\n",
              "AAAAZQAAAEsAAABbAAAAUgAAADQAAABXAAAAPwAAAEsAAABAAAAAFHN0Y28AAAAAAAAAAQAAACwA\n",
              "AABidWR0YQAAAFptZXRhAAAAAAAAACFoZGxyAAAAAAAAAABtZGlyYXBwbAAAAAAAAAAAAAAAAC1p\n",
              "bHN0AAAAJal0b28AAAAdZGF0YQAAAAEAAAAATGF2ZjU3LjgzLjEwMA==\n",
              "\">\n",
              "  Your browser does not support the video tag.\n",
              "</video>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train a convolutional spiking neural network (CSNN)"
      ],
      "metadata": {
        "id": "jpz1dFXSJtna"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the CSNN architecture\n",
        "\n",
        "import snntorch as snn\n",
        "from snntorch import surrogate\n",
        "from snntorch import functional as SF\n",
        "from snntorch import spikeplot as splt\n",
        "from snntorch import utils\n",
        "import torch.nn as nn\n",
        "\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "\n",
        "# neuron and simulation parameters\n",
        "spike_grad = surrogate.fast_sigmoid(slope=75)\n",
        "beta = 0.5\n",
        "\n",
        "#  Initialize Network\n",
        "csn_net = nn.Sequential(nn.Conv2d(2, 12, 5),\n",
        "                    nn.MaxPool2d(2),\n",
        "                    snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),\n",
        "                    nn.Conv2d(12, 32, 5),\n",
        "                    nn.MaxPool2d(2),\n",
        "                    snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),\n",
        "                    nn.Flatten(),\n",
        "                    nn.Linear(32*5*5, 10),\n",
        "                    snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True)\n",
        "                    ).to(device)\n",
        "\n",
        "def forward_pass(csn_net, data):  \n",
        "  spk_rec = []\n",
        "  utils.reset(csn_net)  # resets hidden states for all LIF neurons in csn_net\n",
        "\n",
        "  for step in range(data.size(1)):  # data.size(1) = number of time steps\n",
        "      spk_out, mem_out = csn_net(data[:, step])\n",
        "      spk_rec.append(spk_out)\n",
        "  \n",
        "  return torch.stack(spk_rec)"
      ],
      "metadata": {
        "id": "79ObyV6IJyC_"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Params\n",
        "optimizer = torch.optim.Adam(csn_net.parameters(), lr=2e-2, betas=(0.9, 0.999))\n",
        "loss_fn = SF.mse_count_loss(correct_rate=0.8, incorrect_rate=0.2)\n",
        "\n",
        "num_epochs = 1\n",
        "num_iters = 100\n",
        "\n",
        "loss_hist = []\n",
        "acc_hist = []\n",
        "\n",
        "# training loop\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (data, targets) in enumerate(iter(trainloader)):\n",
        "        data = data.to(device)\n",
        "        targets = targets.to(device)\n",
        "\n",
        "        csn_net.train()\n",
        "        spk_rec = forward_pass(csn_net, data)\n",
        "        loss_val = loss_fn(spk_rec, targets)\n",
        "\n",
        "        # Gradient calculation + weight update\n",
        "        optimizer.zero_grad()\n",
        "        loss_val.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # Store loss history for future plotting\n",
        "        loss_hist.append(loss_val.item())\n",
        " \n",
        "        print(f\"Epoch {epoch}, Iteration {i} \\nTrain Loss: {loss_val.item():.2f}\")\n",
        "\n",
        "        acc = SF.accuracy_rate(spk_rec, targets) \n",
        "        acc_hist.append(acc)\n",
        "        print(f\"Accuracy: {acc * 100:.2f}%\\n\")\n",
        "\n",
        "        # This will end training after 50 iterations by default\n",
        "        if i == num_iters:\n",
        "          break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5KXKKtjcKRyN",
        "outputId": "94b8f388-4536-4ce8-85b0-16726e7eb422"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0, Iteration 0 \n",
            "Train Loss: 30.96\n",
            "Accuracy: 12.50%\n",
            "\n",
            "Epoch 0, Iteration 1 \n",
            "Train Loss: 31.08\n",
            "Accuracy: 3.12%\n",
            "\n",
            "Epoch 0, Iteration 2 \n",
            "Train Loss: 31.00\n",
            "Accuracy: 6.25%\n",
            "\n",
            "Epoch 0, Iteration 3 \n",
            "Train Loss: 31.00\n",
            "Accuracy: 15.62%\n",
            "\n",
            "Epoch 0, Iteration 4 \n",
            "Train Loss: 30.90\n",
            "Accuracy: 10.16%\n",
            "\n",
            "Epoch 0, Iteration 5 \n",
            "Train Loss: 30.96\n",
            "Accuracy: 10.94%\n",
            "\n",
            "Epoch 0, Iteration 6 \n",
            "Train Loss: 31.00\n",
            "Accuracy: 12.50%\n",
            "\n",
            "Epoch 0, Iteration 7 \n",
            "Train Loss: 30.90\n",
            "Accuracy: 11.72%\n",
            "\n",
            "Epoch 0, Iteration 8 \n",
            "Train Loss: 31.00\n",
            "Accuracy: 9.38%\n",
            "\n",
            "Epoch 0, Iteration 9 \n",
            "Train Loss: 31.02\n",
            "Accuracy: 10.94%\n",
            "\n",
            "Epoch 0, Iteration 10 \n",
            "Train Loss: 31.02\n",
            "Accuracy: 12.50%\n",
            "\n",
            "Epoch 0, Iteration 11 \n",
            "Train Loss: 30.90\n",
            "Accuracy: 11.72%\n",
            "\n",
            "Epoch 0, Iteration 12 \n",
            "Train Loss: 30.90\n",
            "Accuracy: 16.41%\n",
            "\n",
            "Epoch 0, Iteration 13 \n",
            "Train Loss: 30.90\n",
            "Accuracy: 9.38%\n",
            "\n",
            "Epoch 0, Iteration 14 \n",
            "Train Loss: 30.80\n",
            "Accuracy: 7.81%\n",
            "\n",
            "Epoch 0, Iteration 15 \n",
            "Train Loss: 24.74\n",
            "Accuracy: 7.03%\n",
            "\n",
            "Epoch 0, Iteration 16 \n",
            "Train Loss: 15.58\n",
            "Accuracy: 7.03%\n",
            "\n",
            "Epoch 0, Iteration 17 \n",
            "Train Loss: 14.18\n",
            "Accuracy: 12.50%\n",
            "\n",
            "Epoch 0, Iteration 18 \n",
            "Train Loss: 19.02\n",
            "Accuracy: 12.50%\n",
            "\n",
            "Epoch 0, Iteration 19 \n",
            "Train Loss: 20.31\n",
            "Accuracy: 17.19%\n",
            "\n",
            "Epoch 0, Iteration 20 \n",
            "Train Loss: 18.15\n",
            "Accuracy: 18.75%\n",
            "\n",
            "Epoch 0, Iteration 21 \n",
            "Train Loss: 14.19\n",
            "Accuracy: 14.06%\n",
            "\n",
            "Epoch 0, Iteration 22 \n",
            "Train Loss: 11.47\n",
            "Accuracy: 13.28%\n",
            "\n",
            "Epoch 0, Iteration 23 \n",
            "Train Loss: 14.06\n",
            "Accuracy: 20.31%\n",
            "\n",
            "Epoch 0, Iteration 24 \n",
            "Train Loss: 15.30\n",
            "Accuracy: 13.28%\n",
            "\n",
            "Epoch 0, Iteration 25 \n",
            "Train Loss: 13.14\n",
            "Accuracy: 15.62%\n",
            "\n",
            "Epoch 0, Iteration 26 \n",
            "Train Loss: 11.33\n",
            "Accuracy: 21.88%\n",
            "\n",
            "Epoch 0, Iteration 27 \n",
            "Train Loss: 11.34\n",
            "Accuracy: 20.31%\n",
            "\n",
            "Epoch 0, Iteration 28 \n",
            "Train Loss: 11.80\n",
            "Accuracy: 21.88%\n",
            "\n",
            "Epoch 0, Iteration 29 \n",
            "Train Loss: 12.36\n",
            "Accuracy: 21.88%\n",
            "\n",
            "Epoch 0, Iteration 30 \n",
            "Train Loss: 11.25\n",
            "Accuracy: 24.22%\n",
            "\n",
            "Epoch 0, Iteration 31 \n",
            "Train Loss: 10.33\n",
            "Accuracy: 35.94%\n",
            "\n",
            "Epoch 0, Iteration 32 \n",
            "Train Loss: 10.80\n",
            "Accuracy: 37.50%\n",
            "\n",
            "Epoch 0, Iteration 33 \n",
            "Train Loss: 11.18\n",
            "Accuracy: 48.44%\n",
            "\n",
            "Epoch 0, Iteration 34 \n",
            "Train Loss: 10.31\n",
            "Accuracy: 45.31%\n",
            "\n",
            "Epoch 0, Iteration 35 \n",
            "Train Loss: 10.01\n",
            "Accuracy: 50.00%\n",
            "\n",
            "Epoch 0, Iteration 36 \n",
            "Train Loss: 10.03\n",
            "Accuracy: 46.09%\n",
            "\n",
            "Epoch 0, Iteration 37 \n",
            "Train Loss: 9.79\n",
            "Accuracy: 45.31%\n",
            "\n",
            "Epoch 0, Iteration 38 \n",
            "Train Loss: 9.82\n",
            "Accuracy: 38.28%\n",
            "\n",
            "Epoch 0, Iteration 39 \n",
            "Train Loss: 9.52\n",
            "Accuracy: 43.75%\n",
            "\n",
            "Epoch 0, Iteration 40 \n",
            "Train Loss: 9.40\n",
            "Accuracy: 49.22%\n",
            "\n",
            "Epoch 0, Iteration 41 \n",
            "Train Loss: 9.12\n",
            "Accuracy: 50.00%\n",
            "\n",
            "Epoch 0, Iteration 42 \n",
            "Train Loss: 9.14\n",
            "Accuracy: 50.78%\n",
            "\n",
            "Epoch 0, Iteration 43 \n",
            "Train Loss: 8.72\n",
            "Accuracy: 56.25%\n",
            "\n",
            "Epoch 0, Iteration 44 \n",
            "Train Loss: 8.71\n",
            "Accuracy: 48.44%\n",
            "\n",
            "Epoch 0, Iteration 45 \n",
            "Train Loss: 8.88\n",
            "Accuracy: 38.28%\n",
            "\n",
            "Epoch 0, Iteration 46 \n",
            "Train Loss: 8.75\n",
            "Accuracy: 53.91%\n",
            "\n",
            "Epoch 0, Iteration 47 \n",
            "Train Loss: 8.99\n",
            "Accuracy: 39.84%\n",
            "\n",
            "Epoch 0, Iteration 48 \n",
            "Train Loss: 8.65\n",
            "Accuracy: 45.31%\n",
            "\n",
            "Epoch 0, Iteration 49 \n",
            "Train Loss: 8.38\n",
            "Accuracy: 50.00%\n",
            "\n",
            "Epoch 0, Iteration 50 \n",
            "Train Loss: 8.30\n",
            "Accuracy: 51.56%\n",
            "\n",
            "Epoch 0, Iteration 51 \n",
            "Train Loss: 8.13\n",
            "Accuracy: 60.16%\n",
            "\n",
            "Epoch 0, Iteration 52 \n",
            "Train Loss: 8.03\n",
            "Accuracy: 62.50%\n",
            "\n",
            "Epoch 0, Iteration 53 \n",
            "Train Loss: 7.96\n",
            "Accuracy: 63.28%\n",
            "\n",
            "Epoch 0, Iteration 54 \n",
            "Train Loss: 8.23\n",
            "Accuracy: 55.47%\n",
            "\n",
            "Epoch 0, Iteration 55 \n",
            "Train Loss: 7.89\n",
            "Accuracy: 61.72%\n",
            "\n",
            "Epoch 0, Iteration 56 \n",
            "Train Loss: 7.88\n",
            "Accuracy: 61.72%\n",
            "\n",
            "Epoch 0, Iteration 57 \n",
            "Train Loss: 7.52\n",
            "Accuracy: 63.28%\n",
            "\n",
            "Epoch 0, Iteration 58 \n",
            "Train Loss: 7.70\n",
            "Accuracy: 56.25%\n",
            "\n",
            "Epoch 0, Iteration 59 \n",
            "Train Loss: 7.55\n",
            "Accuracy: 58.59%\n",
            "\n",
            "Epoch 0, Iteration 60 \n",
            "Train Loss: 7.58\n",
            "Accuracy: 58.59%\n",
            "\n",
            "Epoch 0, Iteration 61 \n",
            "Train Loss: 7.71\n",
            "Accuracy: 64.84%\n",
            "\n",
            "Epoch 0, Iteration 62 \n",
            "Train Loss: 7.29\n",
            "Accuracy: 70.31%\n",
            "\n",
            "Epoch 0, Iteration 63 \n",
            "Train Loss: 7.45\n",
            "Accuracy: 61.72%\n",
            "\n",
            "Epoch 0, Iteration 64 \n",
            "Train Loss: 7.09\n",
            "Accuracy: 67.19%\n",
            "\n",
            "Epoch 0, Iteration 65 \n",
            "Train Loss: 7.10\n",
            "Accuracy: 69.53%\n",
            "\n",
            "Epoch 0, Iteration 66 \n",
            "Train Loss: 7.64\n",
            "Accuracy: 60.94%\n",
            "\n",
            "Epoch 0, Iteration 67 \n",
            "Train Loss: 7.28\n",
            "Accuracy: 61.72%\n",
            "\n",
            "Epoch 0, Iteration 68 \n",
            "Train Loss: 7.22\n",
            "Accuracy: 60.94%\n",
            "\n",
            "Epoch 0, Iteration 69 \n",
            "Train Loss: 7.25\n",
            "Accuracy: 62.50%\n",
            "\n",
            "Epoch 0, Iteration 70 \n",
            "Train Loss: 7.46\n",
            "Accuracy: 62.50%\n",
            "\n",
            "Epoch 0, Iteration 71 \n",
            "Train Loss: 7.18\n",
            "Accuracy: 64.06%\n",
            "\n",
            "Epoch 0, Iteration 72 \n",
            "Train Loss: 6.64\n",
            "Accuracy: 67.19%\n",
            "\n",
            "Epoch 0, Iteration 73 \n",
            "Train Loss: 7.33\n",
            "Accuracy: 64.84%\n",
            "\n",
            "Epoch 0, Iteration 74 \n",
            "Train Loss: 7.02\n",
            "Accuracy: 68.75%\n",
            "\n",
            "Epoch 0, Iteration 75 \n",
            "Train Loss: 6.85\n",
            "Accuracy: 71.09%\n",
            "\n",
            "Epoch 0, Iteration 76 \n",
            "Train Loss: 7.04\n",
            "Accuracy: 62.50%\n",
            "\n",
            "Epoch 0, Iteration 77 \n",
            "Train Loss: 6.75\n",
            "Accuracy: 71.88%\n",
            "\n",
            "Epoch 0, Iteration 78 \n",
            "Train Loss: 6.74\n",
            "Accuracy: 67.19%\n",
            "\n",
            "Epoch 0, Iteration 79 \n",
            "Train Loss: 6.79\n",
            "Accuracy: 67.97%\n",
            "\n",
            "Epoch 0, Iteration 80 \n",
            "Train Loss: 6.33\n",
            "Accuracy: 71.09%\n",
            "\n",
            "Epoch 0, Iteration 81 \n",
            "Train Loss: 6.58\n",
            "Accuracy: 64.06%\n",
            "\n",
            "Epoch 0, Iteration 82 \n",
            "Train Loss: 7.10\n",
            "Accuracy: 62.50%\n",
            "\n",
            "Epoch 0, Iteration 83 \n",
            "Train Loss: 6.76\n",
            "Accuracy: 67.97%\n",
            "\n",
            "Epoch 0, Iteration 84 \n",
            "Train Loss: 6.71\n",
            "Accuracy: 70.31%\n",
            "\n",
            "Epoch 0, Iteration 85 \n",
            "Train Loss: 6.40\n",
            "Accuracy: 68.75%\n",
            "\n",
            "Epoch 0, Iteration 86 \n",
            "Train Loss: 7.18\n",
            "Accuracy: 57.81%\n",
            "\n",
            "Epoch 0, Iteration 87 \n",
            "Train Loss: 6.32\n",
            "Accuracy: 71.88%\n",
            "\n",
            "Epoch 0, Iteration 88 \n",
            "Train Loss: 6.63\n",
            "Accuracy: 66.41%\n",
            "\n",
            "Epoch 0, Iteration 89 \n",
            "Train Loss: 6.14\n",
            "Accuracy: 71.88%\n",
            "\n",
            "Epoch 0, Iteration 90 \n",
            "Train Loss: 6.30\n",
            "Accuracy: 73.44%\n",
            "\n",
            "Epoch 0, Iteration 91 \n",
            "Train Loss: 6.17\n",
            "Accuracy: 67.97%\n",
            "\n",
            "Epoch 0, Iteration 92 \n",
            "Train Loss: 6.35\n",
            "Accuracy: 67.97%\n",
            "\n",
            "Epoch 0, Iteration 93 \n",
            "Train Loss: 6.14\n",
            "Accuracy: 71.09%\n",
            "\n",
            "Epoch 0, Iteration 94 \n",
            "Train Loss: 6.01\n",
            "Accuracy: 69.53%\n",
            "\n",
            "Epoch 0, Iteration 95 \n",
            "Train Loss: 5.87\n",
            "Accuracy: 71.09%\n",
            "\n",
            "Epoch 0, Iteration 96 \n",
            "Train Loss: 6.09\n",
            "Accuracy: 75.00%\n",
            "\n",
            "Epoch 0, Iteration 97 \n",
            "Train Loss: 5.50\n",
            "Accuracy: 82.03%\n",
            "\n",
            "Epoch 0, Iteration 98 \n",
            "Train Loss: 6.26\n",
            "Accuracy: 76.56%\n",
            "\n",
            "Epoch 0, Iteration 99 \n",
            "Train Loss: 5.91\n",
            "Accuracy: 75.00%\n",
            "\n",
            "Epoch 0, Iteration 100 \n",
            "Train Loss: 5.75\n",
            "Accuracy: 71.09%\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot loss\n",
        "fig = plt.figure(facecolor=\"w\")\n",
        "plt.plot(loss_hist)\n",
        "plt.title(\"Train Set Loss\")\n",
        "plt.xlabel(\"Iteration\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "3NF4Q5MbbOuY",
        "outputId": "b74d5354-5592-4adb-9038-2d46ad2dede5"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot accuracy\n",
        "fig = plt.figure(facecolor=\"w\")\n",
        "plt.plot(acc_hist)\n",
        "plt.title(\"Train Set Accuracy\")\n",
        "plt.xlabel(\"Iteration\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "NPon7wmhKbmW",
        "outputId": "b1b9cbeb-9bfc-47e2-d875-4368f861c410"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import HTML\n",
        "\n",
        "idx = 0\n",
        "\n",
        "fig, ax = plt.subplots(facecolor='w', figsize=(12, 7))\n",
        "labels=['0', '1', '2', '3', '4', '5', '6', '7', '8','9']\n",
        "print(f\"The target label is: {targets[idx]}\")\n",
        "\n",
        "# plt.rcParams['animation.ffmpeg_path'] = 'C:\\\\path\\\\to\\\\your\\\\ffmpeg.exe'\n",
        "\n",
        "#  Plot spike count histogram\n",
        "anim = splt.spike_count(spk_rec[:, idx].detach().cpu(), fig, ax, labels=labels, \n",
        "                        animate=True, interpolate=1)\n",
        "\n",
        "HTML(anim.to_html5_video())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 992
        },
        "id": "_IzrjQJ7Kqwu",
        "outputId": "63ec20b3-196f-4bb8-b3a1-094fd88b9fbe"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The target label is: 2\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<video width=\"864\" height=\"504\" controls autoplay loop>\n",
              "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAACye21kYXQAAAKuBgX//6rcRem9\n",
              "5tlIt5Ys2CDZI+7veDI2NCAtIGNvcmUgMTUyIHIyODU0IGU5YTU5MDMgLSBILjI2NC9NUEVHLTQg\n",
              "QVZDIGNvZGVjIC0gQ29weWxlZnQgMjAwMy0yMDE3IC0gaHR0cDovL3d3dy52aWRlb2xhbi5vcmcv\n",
              "eDI2NC5odG1sIC0gb3B0aW9uczogY2FiYWM9MSByZWY9MyBkZWJsb2NrPTE6MDowIGFuYWx5c2U9\n",
              "MHgzOjB4MTEzIG1lPWhleCBzdWJtZT03IHBzeT0xIHBzeV9yZD0xLjAwOjAuMDAgbWl4ZWRfcmVm\n",
              "PTEgbWVfcmFuZ2U9MTYgY2hyb21hX21lPTEgdHJlbGxpcz0xIDh4OGRjdD0xIGNxbT0wIGRlYWR6\n",
              "b25lPTIxLDExIGZhc3RfcHNraXA9MSBjaHJvbWFfcXBfb2Zmc2V0PS0yIHRocmVhZHM9MyBsb29r\n",
              "YWhlYWRfdGhyZWFkcz0xIHNsaWNlZF90aHJlYWRzPTAgbnI9MCBkZWNpbWF0ZT0xIGludGVybGFj\n",
              "ZWQ9MCBibHVyYXlfY29tcGF0PTAgY29uc3RyYWluZWRfaW50cmE9MCBiZnJhbWVzPTMgYl9weXJh\n",
              "bWlkPTIgYl9hZGFwdD0xIGJfYmlhcz0wIGRpcmVjdD0xIHdlaWdodGI9MSBvcGVuX2dvcD0wIHdl\n",
              "aWdodHA9MiBrZXlpbnQ9MjUwIGtleWludF9taW49MjUgc2NlbmVjdXQ9NDAgaW50cmFfcmVmcmVz\n",
              "aD0wIHJjX2xvb2thaGVhZD00MCByYz1jcmYgbWJ0cmVlPTEgY3JmPTIzLjAgcWNvbXA9MC42MCBx\n",
              "cG1pbj0wIHFwbWF4PTY5IHFwc3RlcD00IGlwX3JhdGlvPTEuNDAgYXE9MToxLjAwAIAAAA3+ZYiE\n",
              "AC///vau/MsrRwuVLh1Ze7NR8uhJcv2IMH1oAAADAAADAAADAAEPmDjyj2JJkegAAAMAAnQAaoSh\n",
              "wr4HIoEyXk+UD+ecIrQavLhNEraTs5/98DoQD5nYFfuMS9cYrgHyrq7TC7QdXlDZHr11ISK7BfwM\n",
              "0OxZ0bgoiAx2twMNmOnVI6Rh2mjDPmiR6/IbYBnVRqxJFs1cOll/J04z2/nYxAzgkLwjPcw7j1ZH\n",
              "9eVFowyTu6p/9Ih1deR8yI+EkJ/szNWRSIVME4J6I2bZkI0DVj/uXg1rTsp9ceutbx4wdFfnZVMA\n",
              "ohAGvpT3igYBBfoDBe4iwPevfmBEEBoHC50GxHGVotlKXxs07I4L5VpJFhwMsx/8RRl1p0V+NW4f\n",
              "kO+LRybHDI9ih/15VEZEVEQu1kC2T05RcUAFiqxlxH7e45JEUVYrCPxns1PoUAczpoFht8BoMBIk\n",
              "0cACFwov2u5LncC/9GQ6CXj0p2r8SU050BYbHFDawBdWkP7ic+qVUM9spTaKfHNcwfM5X44wdy/l\n",
              "3KJ/nSPgzoZY1jD3AebTT2TWaUvJYIDNbvIIJeE5g0YGg7G6pqV++lPh5niJGMoklNlBVYcADnjm\n",
              "I3nPmoEZeM7EV5Zgzo92ciU9MXJJ06qWWLStCwRq6D7J3c5QI3VwVtSRcPNpN0R9kjU4JINkAwVi\n",
              "jnuEpnEOiyaY/8xxq3e6if19T331C7up2282mi4y+QOrdZ/l8tV5uDpqWrCE9OOAmGctWOSO7m6u\n",
              "hPCFcVUYY7pnjxDeqy1KQBVcnSbBfftfOR2IhmQVYPmx8DtM1aHnnxD7wyIO6eTigNcel2VF8grP\n",
              "uyTvnwln9QA+mDtmynGb255D8REJd1NIASFHj1/4/N8iejXzcd2fztWseC5yGeL8kCTKEtj9yRgE\n",
              "bWydmBb0+DkYkGUqKqWy6V8iu2PuRw80Hv4m0aswNWf5BiTbazFRt+cDKA4kAQwcHnoo7uMZgenI\n",
              "pQVAq1wryRx1Acp9WRLrTrdM2CeW8ZU0aG9Ki75l/NYs1IOfXxQifPAoPyVgaqL7OVbZsurBZmjz\n",
              "8EuNnnNUzduegJYACi6Dg6zMyB8jS7utAo0L7pZEvmaBnyGqI5PfipoWCsDdm+IfwDpr/feT8n5f\n",
              "OgfutkSoq1wph8nchP/8EmaQsTuAHhiFevN3z5+SU2s8Utd7HA3UZyfuezVPNApZm7bDMuOMpiKV\n",
              "+lejXsFeYQt0W9GEPh8WMrkpbO2bx2XiIfWXkFP/ZIB1H/wmCAX9V3y3iVh82kSW8JMiQc/LrH0x\n",
              "cKKYps4wJKNKqG/H1RHB0zpkuJCd0EcfUfPa/3aWpKfoYpKkNJU1epszdfblZZrrUKU8YUaIw17/\n",
              "eSVI8xa1dWM0gIOotKzGKN02M12ARGrQUDjf2zGgHCQUn4Rbhyk059P2Ja6PKBsg7p2A4SABCLPd\n",
              "muiTk41popQO2Kxbrfxn1B2hIA+a+cVWkstKlVwO0/AckJqf570gxPnQaQxfi3ERFi54QsX0Obni\n",
              "urRJR2dXP7kju7hrgLlCXzvdUPDEvvF4zb0uhoYUdV/eqT40KRSGdobnVpVMdQRyk8rXALLbHtB5\n",
              "YPcbJbi148EcljiDomsNsXHACb5e3MzWmG4yXSkvt5zl7d0SfZWcAEc2LYUYntfoGGH2xPoQfV/P\n",
              "ddaUXmYlU8XKarmKNSIKe2J8aHOPkujIfacaB/gAO1D0JpnrBFfG/qjNwrqLB6sNIWEU03dWE9/h\n",
              "4u5kPzAsHRScNCnL4N06qEUPIzfwiX6j0+YZZpb73nl+hRFPOrspOTpcDL1YDbkDm6Ea4WMLSryl\n",
              "Cn8HWxI764iK3RxolI7t88VQAHhHuEWAQ0lxVq2p5oWwnyRGbFgbE3PKpVHxRp7BrWb0JGauVUPd\n",
              "S2RH29Y6FdY2n4rtbUUzOcVX87ez56aFF/NSQ1HEw9voBPheO3NlGpCn5nKGxkcjNbPPK1bJsZKP\n",
              "S3Pin47l4K/lEUPgDYbvak7j+w6IvLEgJ5MWOHoAAfDuWfy7eewAtzJ5rFs4MYdDc7C2G4/7nV9+\n",
              "NBakhpqBj6z6/3wBEZdhN8ry+nPfQ3oV46UrEjFo3bA3CexvbbSRxxsR0EXKSJEA4wkgidpuMMp6\n",
              "7KLEdoXI/DzXzlVwKfDMLpjoiFAGnPR4bm8NzQeeHTmhjZF6SOixrhoAFRVMSRKf6j30rv1ijVwG\n",
              "LefpFwTRteL+6hDsmAAzFBfuqb9DwFdJW7t2YFHfyeyEA7ck3ZlstyFHOlBmQDAnbvjiHdOUYAAg\n",
              "F6G8Ilcz+zfQu0D7NGFs9IlEC3ZBsys8UlBF4G//dk8Zf09RULjZ1AH1jo22ZHSKANR9kxdCp4ko\n",
              "2yAllBHD2klJ+2p/A6AMAAMFwhW1AC2OXxySvtTeE/YaH+h1RsFT2AgkVwJ8x4E7QkzoJF0mvzyL\n",
              "XfLew3nsHoajgGFM6EgWmLZwSQhA2/ybiL0NxNyYQ9hGKvrLJf2EncnA+akzqvk+/ktvD58aROfD\n",
              "VSS6vWfWl8eiOEtG1jQZprq8aVnOAAzhKQDuwYqmDrjeeinSODArjvqD+LbScu0RcLym0rYW5wOw\n",
              "Llh/9/cTOMxM7wVtu8Ao+Fo9Fh0THFCDC+WWfZByk9V/XEz3tcLDu1zn9OnCoNxi2cqxWObLujQw\n",
              "HNGay5HAZkCFPNP4C5owDie5x1xu9RwCjOV0+H8b/FANZsPRUoqkaKzTNtj/MmvW7yOZsi0qQdaX\n",
              "CkbEetpYlKDkxxfKuusJfPfbJB/OZKj2ZQXdcI/lHYF/jY6/tI6pky+6tvFyILt0C6DkUZkUS/x/\n",
              "9ANJOZbLyyW20/nqLS6YWU/8HEjIpY4bk+QWwBpSp6cFrsqcWY4/2jnHN4QgOHcHdmvpiSp9gluY\n",
              "pnh/gmYyhEJNmUDTSd34CCWyrLixiW+Ar3/uuhf+GuZ65ZRnJyCCABCl4Y3VDuK8O3zVSCnOv5sc\n",
              "iSsAFibO89KHTSGcRDa6z2drUKEubrc4NlJsxTmVrOnbVhwl90z+M1Q4uOmP3EO8j8pnmxwRU4OR\n",
              "TvcvGcXHAN2WcEQmt0C20lnLXn54fzw3diGZf4lu9JAEu/gS72s+cpp7vsaqzSVj+JN1rToBitLV\n",
              "dHNKvNj20YMqpJHQbY8yogcC2tMe9W8EoNB0exE4SWowQ0P/j05nVg3p9wANB3+VBtlsEQODsx16\n",
              "8GOztT0HdaN08m8jJse9KPct/8b1ZwnxmE5sCz1JUelmIj4V7peWlknnUbmwa1dTyFAEjowLBmdz\n",
              "Gf6c9xv0COD8BNusDwjJzrG8h2LpbXu2MY4//8c+vSfDCKgFPmjn84ZzyudgB7Yf3FUgyXqnrHPM\n",
              "a25MO+zNJU4wNh+PdqpXl0hgEfRC7FPrBa1I+4dchsDYfLM6oXW+iDwjqY7LyIlbDn9QSn8W0lxq\n",
              "Uz+ko2XrDcS6g2RYaJQ1Rr0gNUZfRPLlOrS+0q5ZXvPdTm3DjcEEvH3jpD90UP9Vze7OsOycrJJ6\n",
              "71AOCGJWFZeLzE+2q8t3vpnYVTHboPTBKtHu1K2flE/JYAG1ET6ARJluYAS8HXNA5b2AFHrxvyUr\n",
              "ayaS9Zwp7nL2I5zFKCDJ9GYPGcnxABKnf/6hMKOYEzvicLorOruBCcVk59PnjS+rR+taiVg+1kml\n",
              "SKxH9jvQ2aESXIScqpuU2215wyTWwYR60hO/e/gTZCwMmebBdrulCcMjnHvLxbIzuOvwQsiGXOGf\n",
              "2SdqD0vBcHLGXzeKtJv1GxfXBBt65Jof9X5utskZQ0X06qPf1ZzGfxNsyuZmvghlfwX+Nej5mA1r\n",
              "wMpVlI09Id0QpsAmgXx9qzoXskONJSP4/R7ONJ/3sdul+rPM4MqtxdA/5W9tP7qLl3apa2C5HStU\n",
              "06kalyWGqj1SYGlSV0JY+mlLVJHBt7s2Hoc3j9ojjod4qayG6hNyhFXzMRp59v/PChTas/8gYbQd\n",
              "5G2O1ZmOHFYZedv7G6dItLtby2ExFPxMNSplaM/XXk/+oWnk3CPn5weT4ghyhaIcfapHR3TxBon8\n",
              "ta65Al8rEFzxR9daELPnW65VrJGJ71VAHxW7bMhO0n2BxUrRTdpg5XE8RLMBQZ14r8YEkm2GMeWw\n",
              "wSTH6Y7gk/kKYqUHGNSsw9tp6QU9A5ocMvptNtH9ptT8p8IE1SwMJyxgsAABYy32wzBVazdfmWp6\n",
              "U8LANAQ75lmaKorXVEfnDkaiOdGM278SxVSE+chdhXUIC6tq4EsTBUvyKDNV5UmYsiAxTnkgKBgH\n",
              "i1PENKnW1MAewXfUjkN6fq9NoeXQDhDXQThoTmId45EU83PIYaRloTR/L/Z9QH7iJVn4gau+XSob\n",
              "sNdWx+FJ4sMbWTUqlzbY/u6+1ajaeAkCZkh2lURC67qiIqMSsEWajemD8ys1AH9LwcGg9/pBuqw9\n",
              "HzWm7czxeZX/oqLBQjQTFKSFbec47Y2JZwpyhID4ErPNwuc8hwjjm5ZFe3H71+GfXI5YaL4j72j/\n",
              "S85UhJCezB1m+QJjH523vzevjrW/xTvRtfaIlPLpPCVPUTOT5GcYPhLHFPJbXVt8QvrJsw3W7u3I\n",
              "Xfb6PAg98m1gUxAXU/28ZRYqzD1kArl5JRXem3h/VYNtoVd9DBMDs0kFebfTazdsN3YvoCB/XYLP\n",
              "pOy/SPlaQdZoMLnz9+imM3uR8RKNsX2uGSw+6HtaudzT2RbstDXWOQ1IaTtHWHKGDkMIN4DixW96\n",
              "VNqdOEq4haU3V0lnEAVcShOkvGyGIo1jj6tfc3HlwAEa1T0gV2BcBfUTgDshAAADK0GaImxCv/44\n",
              "QAGHpn0EktMf9fMkFSnI5F5j17wPYg7+H3C1+CGDiakB/tkj7+19wMVP+nFcw9UKdtSp28DgEqFG\n",
              "W43VZnBefXM9+ohq817MAMH9BXljhyyuvw50477HnqJqNxBDBLSexhdLT0LXS/Gx5mz4DW0B5q9X\n",
              "NIUECuyXikoEjDyH6HGGVMpX1sO5cbBcRCeLVqSrAUP/A55IBE0TWPmaW1sceslAfWNPTUk1IDe4\n",
              "XFIdZeI2vNpzshSevJCXyyeiAplfHYDNmyKtQIHYkNB7MUUVDUce9gt2kI08UHz3o05wEjMyopQ7\n",
              "qZMyozR5V5oVHWi2dlDVGclHbIX5LuuoAj79UnuNpEhQTKNMiYwS5p2qw8JZwn/3cBR3BcpSzKrd\n",
              "xlVa9b811pu+QJJHIv46yL+ZT+i/8Pu5dhzDOEpRwz0zF+sPyNu9C8zmPUMgMUSazbyMaEab5FSL\n",
              "wTLu0YC7KtsldaUpUAYBwQ2ytKCmh/6IjzNM2laW53yx8toKc2/0vCi+8iphIdYY65CiWVuoXIga\n",
              "TNSZY2drcoJsvWHyNuUPT+TfkUUQwMTORpxYJxLBd422/fREJJQyEXQubrha/ZDoMi/UckrJ1y0L\n",
              "tdoSTEc7YBowTlYKKFjjgqokAA/6SW7zANK4Bhv211IqLbsbpx3L5adTH9wk6NBetPYjrKmtniHN\n",
              "hdqeloLGBknU/FtQ+JvtRuf3KJw2+aSsg3baOFk4Z7lZdO4VIdnddFLIc41jwi9e932fZA3PNIEs\n",
              "TXORvs/mlOH1+yXk1oohcvu/QPio0LqwD+Qsi3wnvnlh3crgpOZPzCaJ42z8uJpCGo5qb97HZ3Oe\n",
              "W1FlXoMZj6tqiXWXQOUfhfk/qPMFGzDolXLb9OB7UW3qHQlMf/uZPPLqgfeAtyJzro+QdSBgNvK7\n",
              "RJ0wuRCjPIQ/pah85zfqcG31/B9+CE1ytuaciFRKXvEdvpOmA7iMwrEShQ8tV+ZLRyCUbSHaofnz\n",
              "Wuw2fULEu+d2i4+b+7+Vi9u+ZfPfGaOE9HdVjdbFyYH7nhnPG6KnsOX1njd6n2Q7er5gtwWsvu7B\n",
              "7TLwqCAAAAFOAZ5BeQj/AB/BNSABxpO/7nHZG7g9odi3SmjSP1Hr6lvo6XmZ627x8iyUEqFss7gL\n",
              "TTWFpt+vV0QQXJEN9FzPrv5VJqw4KUz3KysQ0Sb89zF77SMc1bplnKKW1HTqmIiNeL/HyDPAkfLD\n",
              "nF4/vGp4aZf0fbEK2GHXEGU7d4vO0AzD45cEWTqRUdc70qKAIlGZD+/QtaR0OWx3H5qiaGHzAlkg\n",
              "EDsaetDtm5EthyH7IKwYzN+1exRvu/6Jug7umNyDbdEca0QhibBMkaPBAJw0QymuIEOuD3a6w5Y/\n",
              "p8npZJ1KY04wEID1uqfHbtOD+2uW8VUNlIXmaGcnduz0O90QrElhW/s2Wv+rsMX4LQcudw4qvbCO\n",
              "NKcVaoZzSGwULO7PlIkptNgSi/u6MgIseiTLAHXCHb/jhBqEUABq02XP9PcVi0SNwPwt5uj2Q7AB\n",
              "KwAAAtVBmkM8IZMphCv//jhAAYnVXAi0ITi9W//3/AhYQfj70j43QM5FQzpiesac2OYrwNhU6wdN\n",
              "9TRpqUZ+eUqbpbW25m99CaMwCIOUOzwYkWtkny9aUExcRKiNxeNpXWcvlSPovS0yrKfmCIza4x2Y\n",
              "Tf9KUXa8kmmhcG4friIuhh3DeXoa19VYfHiHxH03H1rV95FS+/Gu0AmxQFuuLGLSkw2ifActKgOy\n",
              "VTOrrAAbYSZVxZubI/bRpm1k6i3npcoJ7Kw2Tin1m3d35hVr4S110qocJ+IlWVNGmLgL5aYUlIvn\n",
              "mapiU2baAHH7N67so4Zq1e+8/N3TVMQ74RKUn5RcrZ3oLLXJbQRYU31ed8xqHBopsd+dwHnJM16m\n",
              "7byU/hxfgKHwfs4rK4GSP4hEM2B1TyNt//z8mka5pnlhqWk+WLedFIlwu2N/T7liq/0WUyAtoZxL\n",
              "WSlNAl3pxunsRsi0/7SxHf4/slPIGGc29EJ1rST0fyNE+4uetI3EtzLcMQLE4QdTSDpLLpOsksTt\n",
              "yDKAaKNr932kBAvnFMAcuVS9ChTKthH5zJ+j01FjxA7JQUjxq3PaGjiO+bZ0gfS+80auJWjxSiRy\n",
              "6RInTJK4r868zzdIRv1CiVfEJtijzhiPvFSgIpkUChIQqNm4/0WLZblUeeigFZtEg5ilx8H0D8n9\n",
              "E74HqVjQmBDRlW5R7iZ5F08AV7cWPQMO9PINJF/odsuG9cJMQXFooXlt2+CeR4hOBW1Ec93NSWZ6\n",
              "PTfVIt6/JIqoO8quL2YCyBOQ2WbOUKTRcUEyLt0A8gE1jAT17H7H7b3e6VU8J9oH/NXTkVbJpwXx\n",
              "MgnSYYr0sX05icfxxhEb4Ov1K5teWns7Q2ijQk5iZnuF6ibx9edX75Q4MLxLl4ZpwIPQ3B8Xm4Pp\n",
              "XNTaojoT9y3ptbQkmjJTI3LIEKSNMlFfpqLGv0OWt5pYMJce+B/DFLGAJgDazQAAAXpBmmRJ4Q8m\n",
              "UwIV//44QAF+BeatABnJ03qYQ8//zaIgjR6op/xkHDO+bKr0F0qnWsBeP1q3/BZ2IWPBXRsXwxWn\n",
              "vVn1FuuRHFNGCnEW3MzuvrwgKVFT9t65y4zZ478XegZjtMYbd2mMd+F+svx1J9EszpKysTG20UKV\n",
              "aVIogz8oDSATjQa5+uwcipuxu0+iIGp3ntiLkPH1Y1EdbfHJDDawEVnKh1geLukZhGrBtao/UNzH\n",
              "GHUeBcJkODIiHlmfAvr046L1TR0NVyL3on2IDXvxob0lpJAhctJLequz07lH+p08DHcFXea7wYmr\n",
              "dQmjwILwKN61aLPWDqXEYEqOiuc4A4RTZcRhRbrmjHq+2ouHDvG04bUYE0RxVXWhEUaFpu8iyLoG\n",
              "KQaYHy0aT0ukX7jMGShu7IiIrM/uPgFI9m8CfWshyC5txelMoMstKkadGrmHZmeWRRLCxjD+PgLt\n",
              "Q+5kXy9D+vZqlAQSK1025Uh0yNCMyTF2Po3LFMEAAAEeQZqFSeEPJlMCFf/+OEABfUAvUAECgMu+\n",
              "1QF0kR6/z9ZgB1zozqjcdr05qc6vhz/CZLs3gDYJPp6Ni/UjYKv9JRge7+pRCkX9iPUkKBiexWj+\n",
              "btcYbkIwo8k72v3ELFV6eGc5mWvv9/AJydHuiIK3OdoLJgI9M/rXzRNBNPNCfokrYyHOBq5gbbpj\n",
              "5RR9dbKOfBvPihTv+fgWAbLJ8pm8vcpa2t0vzoYTi3+XoNsx4668YFFGYd/s7GYCHtHrqrDWooNB\n",
              "4W09+35VSiFD5ES3kypAoNjNTckxEWS+7Xi++oeaAlLHPVJNM+u9Sc09ls7LpP94suek/NqxVpDK\n",
              "kergcxzjm5h0/rWwYX4xHA9Bq7iXE3PTY2ofBZYexfmCRwAAATJBmqZJ4Q8mUwIX//6MsABiluGL\n",
              "8PvnmgHoAHY/170Vzsr6qCsbTxsX1w7pX+c8u2rg3VhKQYGOHJCe6aYY+MmwEWEvPuNX1rwXOL8G\n",
              "lcj8avTABSeF/19l8rNQuwSCpouDk5yM6F2kw0mRU8P99IEMpHS78U6UXS6yuNDi4TiutVQ9iKvJ\n",
              "NMBEnUru2/37IKXmynyr2XIVSE9/1M+6JR1r4cezQnJM5coYMGmLAQ/BLQ4RLGPaH0pH6A8hrZNe\n",
              "57LI23luTpvLl+50zyTy3YDZk0mm+viSEZEu9MKjlRRPt4zL343ai++/XRGV6PLQ5bbOIfkzo7g0\n",
              "Uty3F3u9Z+o2LO/Z/PUwftfVfrZBhVlPdz2JbuLAcrD1sgJmqZBmwBPc0rhRA72wuyBIH4jmTjxg\n",
              "VmEAAADhQZrHSeEPJlMCF//+jLAAYpbhjADXhZlHgz0+aABNHuo0flTnAszPh8Jmok3CwIBQTEvO\n",
              "Xdg5evoFE9MCe3wkLnbKWJVydvrVIivKDa4YcsD9zEmHCynL/7gETPn1PLxH5wAPjInM3kRZKdbR\n",
              "zINSQvLozhpkI8p/t7lBuwm6rEbJtWvoVw0OYRFdC4c3fC/lUzv4MEL5lzjwsaSnkXRHapEbiZPc\n",
              "MQyifhLuvzrVTnX8gwf3D1Hqs906EzdwyoSa5A/A44BY28PGrK24nz1DxpdiWSylaDWYKUcgGEDy\n",
              "EwqZAAAAiEGa6EnhDyZTAhf//oywAGKW4YwA2Z6AC5F2ENKEi28jij6RnkDqlFSnZhRKuPQHSz9I\n",
              "FjIy7DV2DvwzM4HQw6mdaLB5qDfb7aGq4HzFFfjCgSud3UCRKVG+iA0MtzZstCwpNYSqrpYPbtBS\n",
              "n9o+Hn4u18ioHUFe4Hcte0QSX/Y/eRBqPx7GUEAAAAGpQZsKSeEPJlMFETwv//6MsABjOE58SNVA\n",
              "COt2G7DXF6W3b6BPXb+Y3yJZ6y7iCtjIZ25tqsz+QNgJZ/TdeUpa+cW3YQj5dQc6InbFtUtKRBuq\n",
              "pqe5BBhGXIG76He4UrW+PpJhW+wvI+o6RCVr9aOiYKiqzmXvRRxVXz8g+ZIVZqRrN0NDsrZbpEM8\n",
              "oiwgxh8ZpowiMofEymIGCFAqQZjzAGYxW10+oHO+OPws6R0CaPciPHEyt5AO2MHklvfhS7oRbvNk\n",
              "hfwwVgPpYGaSGR+8hS/HPlY4aiqdn1KOq+JerDyb6AXHmwQGpmImio3ogQPHgIHKP96Ks0VHqS23\n",
              "s5sjyy99W8vUAYBEN1B7qMCsa/fz4Z05OkgOmxkBiI8RVQeNT5dKASWAhxlWqcIOmKlumkQl/dcQ\n",
              "gOv/0466mlvTqrqPUY+i6i90LPfiY78qrF8zoSTrAbRnSNf/Pi97CbiNzRexN4k+9K1KQqquqqg9\n",
              "kLUDV2xYCtN1UCEY86Mha/eu+66FtytrliglqdkgnnN+8C1t+0bu2mk6CRYasM2md/89et7jlh2r\n",
              "BowAAACIAZ8pakI/AB8MP5AAEBO1zvvYXBvjCMLVT5+EMbJ3NXnC/IE0tRX/xfRtdtgw/Pzsn+fC\n",
              "GZzjLAT6UccQwMlxqJLRRzewlZTwWw5GS9icHupBDFyzcv5S9ZVNeipbbiob2vPJPifJbOYvg+tO\n",
              "H/32dl3imV85dFVqHefNy3PcdcNT/Vt1cKMYUQAAANhBmytJ4Q8mUwIX//6MsABgQN/7ulC+QCyC\n",
              "Aa8wjhYfZ+YMFYy71qmsm2ycDnsCoX7ZiMMajphLLcUTn4SMhzqcNLXXcsdal7DGbwDRT/oH1EZl\n",
              "TemAc6kC+aOT8YHPgX7L/NHohOMo2SQyIkT9m1g456HDJ8T2NOy+Cz9Lm+LJvlSobqlO6Yp1te1W\n",
              "O2nDSa+dG8grF8oxWBycsWxrQhiZZxf6yw9vw5bmRxLd4ZI6ITeFikYbWPUp6D7gLIqy3ERAo03D\n",
              "dZbRKVYFZVNCh+OedE4psE/nw+YAAAD5QZtMSeEPJlMCF//+jLAAYEDf+7pQvkAgDmWG9Qw9th5H\n",
              "kUxMeFiRTxYD3VhxtttZ8Uty7jeiY8WFWa+SK9ZsQwDfRmJD/LrCOTWa64loyG5eQsFMoWFZaxBf\n",
              "LbnHYFzklWLTcelhkzxI37oJlhXaAm7bhS1gXAV17j9ccsGllEFuBLSAnsIdzo/PWST5i9VVM+4y\n",
              "L73mf1Q3SrgC+QU/C1ccA1sYmiTvb3XT1SB0o/B6IULOenvAKWtbklUz1vuJku0hnxBeIxpoWBPe\n",
              "IdBPTTqwuTI1J3Y6Fk/j7ne/IvP4HbJrMvrctizzLssJR9nRvGp66m8a8ux9AAAAuUGbbknhDyZT\n",
              "BRE8L//+jLAAYBX9Yul5XADXmV3hwISEnemELvE0dWxqjmBsqMgZhh4swTo3Q9+3L/rPfW+C/33r\n",
              "/JfvXZTaXjE15WJfWH8MHHvBgx4haAVozv+km3IxU2+wZLRPUgdfs8JM9bMkdgpUVUyPCKeMkAij\n",
              "AjVfy+ypRHpha3Kd6Oce12n1OhD3SKU9yKdI7NatqXO+4ord7EjMr/uMZwjTPepwDYLHw91Dzs/y\n",
              "aqyQk49pAAAATgGfjWpCPwAeXPYWbjZ7pzErvv61+LyR0I3EeU9Oympq7GKxSP4uTgRSkZMi4iFU\n",
              "eVfUOkH+CBUDIniTEqFlMr6BXFNwpzEOr12qIy4m4QAAALpBm49J4Q8mUwIX//6MsABgFuGN1EYp\n",
              "wFB84hY/4spT92u/j+2lMjNq8T2OBRCiXetXgEDBTy6Z+NELKqGrSFB3ULhSEc+MMBkaIe1+aB1W\n",
              "G8CcqRW9Qwo3KEh2C+ew7DITP+N5plIDLsItkjEN2CdXT673/OmnbgwNM3sW1ee+wD5AElwQc4OJ\n",
              "UoFy55gU0gK+eHQXXfQFrpDDPcA/ip/rZ0oFgI4TcKaxNYK6mb0a5yEa8tYJfEGABx0AAACZQZux\n",
              "SeEPJlMFETwv//6MsABguE57nN4wAnqSLay2kEl1goryUxrcqaRqL9qcYCE3ulD7dPxuv7pE3Iem\n",
              "+stgryN0SLmqS6297F+Lcxt7tESOlLaUkQdqHw9CUqbPuJ9YYXbG8siwb+jueGRTbvojf5fdKkID\n",
              "SVTLN7u8zWxOFHUXMzB5+xH381c113WoAR3EJz/sylFA8CmgAAAAZQGf0GpCPwAeZzYmRO37aKz5\n",
              "kbmsY3K6ks7BUYAM7KqKC84ORIbekcX6JQAlF9JwK85O5ktw5fw1AzEeJHSzDMETO1qJcNRsOjTf\n",
              "CaLCNIabkbHBKoME5S6+3aMvQpn53paIvhzQAAAAr0Gb0knhDyZTAhf//oywAF3UP/u6QeZ6AQd1\n",
              "f6NpnoYH0oEyKmq4hIK0bRHUAKCe4fbVUM4/k4dNFKGzgSh9xwv431051cEMS1j8nLfSuAwniKlg\n",
              "rllxvBBVDrE1aPesuV+VTJ9RRSpECTSPzijmKA8F/WfJEVjq41wNnP6PBXEWqm6SWFO7Ur+u9g5H\n",
              "2bwyBfFJp1p8zi9hNWSCgn3qF+RIwZDi1RPPbyQE6whOCpkAAACtQZv0SeEPJlMFETwv//6MsABd\n",
              "qf1i6SUHESAoFvFwrfWxiuXBb/hE5nrdp+tpcd0n/t/aHv+iyWkftAdS4B3BPrxJ9173uEamsFYD\n",
              "hKkv+XNdLt1i/2ihs3f7XEN20UkQgJb1aXx19nEaxO/PkmtJ4ybYDLdnshGHye2/DAgJpoIs8Ne3\n",
              "JN+zkjGYppqG4pO3UC265+91PW7s+l1qGX+D6L/wPFLZogrQilcZa4AAAABUAZ4TakI/AB2s9hZu\n",
              "NnunMSu+/rX4vpHRCwEFXQf948X8+xeKLKBq0g24S0bQHFWgXRsooNWWsvdgX93ZlCvXqQ8IWt3y\n",
              "KZNdoo143UaTNtMfFDZgAAAAnEGaFUnhDyZTAhf//oywAF2q4YticqiJ2hBd+2Q/cq2xHK893OyQ\n",
              "6/uLwAHB1LX6iY3po/53Gmb3ANiweACnHwCyMkUkZIobbTJ7ngk7qFoJ6OP0IMTQGndgi5YZs9Sy\n",
              "Ee2wTA5FTum+j2Ll66Ci0S88T2lrSpgCL0WyaGDL0e7Rpdc2DO0+bYnsFRFH80yRb3JQ2l1Q00pC\n",
              "SX8NuQAAAKRBmjdJ4Q8mUwURPC///oywAF2qHVi6SUG9rljQAhSvz60RCio6/5rE6NsOzNCMrNsA\n",
              "XHyu2hWgbqs4O6/dio4JDyeOpGFtclUB83W4X+ESKPGpazcOdMngjBo9ZQCacuBXdkgD04DcEmW1\n",
              "ha8wrVmq0G8W/GzzXYJ3cs+JRmpWimwfSxn0/KnfkaV/rblLQbCa7R/peAclTP+1jQLF755jxvoi\n",
              "YAAAAFMBnlZqQj8AHbc2Say6kJUeZC3tuM3pRzw+SVghtf6FM3A7OkvcQtjolqj8H9umk8hchauE\n",
              "2xZW0puZJo9xm94uMX8MTnWrz0jLbfI1IPWDoF4PmQAAAOtBmllJ4Q8mUwU8L//+jLAAXjmXHmkK\n",
              "YAaU+A1jLbVSIwmTl/9qzEcd3RGAdcq1oz+7Uze2XA5Aw0tZmpc7WnjZtj/gZ/GBRLzfkej2V/km\n",
              "e3WSZ99J8Rjc3fM9PejdrSpiTUK7CPKgSdY69s4BpJ9a8W3eGCtTps+FQHcDLm6jp1kEJGxxLDxC\n",
              "qJIauhRmp9YPHpjYHkQY1Q2AmaM/BXJ/UM9h/segSNm6Ty+o6ODAwvw+hMFZjIg4FjJufF07VoZm\n",
              "f8+C9zAStkk/cxWhC9+ygp3yVzUIvwDyJ2xFnQn38OckFl58p2+ofCkhAAAASgGeeGpCPwAdrPVU\n",
              "s/Wagm1yXoxxdwQUsQ5F246fo1YAG2Ukj4VLP2owcUfHHwPHknIvcHj3c8JtQlLt/G7aZ7O7eF3P\n",
              "wP0+K5NwAAAAlEGaeknhDyZTAhn//p4QAFqsGtcATfvOYO+9u/gX3Uw/ToyKyp4e63d9eflFEtxb\n",
              "V3dqIWhfwZ2nBVT6I2fy4wg+IaASX1Lo1HM5+xtuFrR5AYl8CRRQhfUYwhGftbjKkEiTWwG3jLQF\n",
              "Ni741vP501cTNjFNMtXZV+JXaFNxGa6bbx1uiWWDTdWJe/9+Ydleb2U0CgkAAAC8QZqcSeEPJlMF\n",
              "ETwz//6eEABaq2FSDXFrHB318WFI6EXW0mQDCM7FdtSCaflKAa5B36AnYlOhVqJGCBqu/YwU/BIb\n",
              "FXv0hnay+C5kUNLNCTmRohuD1W5GS2EXKhR5QS3LPlE4hk20eKEyBGdJ9I2I0CjI5xNHBXSjYHJ0\n",
              "NJPPzR79N9tdxDP/lZM1UYbpWT8X6r2KjPBQ+6FXr9DVhFtRf1fsUpkkj3813HTkjTyTaZNRg9bu\n",
              "if/JYW4eX4AAAABNAZ67akI/ABz89gfFLd9UkJRQz2S+LKRdbIa2YIr26m10W5YzdsCrlO7ZZX75\n",
              "7tE40OSSBty1LRyOvNMJOrh6x7gtIOiMywpavzesJOEAAADtQZq/SeEPJlMCGf/+nhAAWquhviAF\n",
              "XY0waoIS7gKXkP+6U+OxzQvTqxTr30Yk7qcd5ClAu/9u3ewgONWrO5LIw8KZ6MQ6/XtFKZS8bTdB\n",
              "wTjFP73IWExffWDVHbOzv1EoBu56KIcVMLcs4t4ZTWGXQ50NwAXA+s6TBSNJFPP/bHjrVqkjZzKb\n",
              "EmaRskTqecoCeP3hh7+8GOpnJYeuofrL1mvD4SR1TS3JUoXCZ+7ToPRTD2NRXSyFkXNIv2Geohbe\n",
              "+l4oA5PNVJqT92rR2CzdKUl6O3WnIzAmyx+FN8cU+idXeKT5HpL8XrE/SXydAAAAYUGe3UURPCf/\n",
              "ABiGn2pbVvu4T6VmGdg/vXDSROHfKU/ShS9o/TWb4L3YI0zV0ETzm+ypnfAEr/U80V2z1NV9nW56\n",
              "TzNnLpBKIRa6MEbhT4ggpsbAx//4GGNYwc/i0MBzEXAAAABQAZ7+akI/ABz89cF1CEc8DRm2d45d\n",
              "5RyqGIHYqR7cHT+oj3a7KBwTm5GWF2RxcagpS5cuhvJU6WJlVPSjHqMDX7L87qqhbq2YixQhG1FA\n",
              "RMAAAACoQZrhSahBaJlMFPDP/p4QAFs5lzrOJBFTiuW75WEKeM7Wua4p/9YWuAb1FaE7VD304n1l\n",
              "3X48AaF5LPaSms2I+yThJOJwSGm9A+62SGpaXbBP6EGVG+FKoVY8XG6EnmjhRzr7fmAhFpHmoYiX\n",
              "oVhWpnH8pNbwD/NNsjkbltKitv17YKvNBmkA8LNiQgF+8Gx6DFs5oXk5j55y4XHjYrvl79BNlHvN\n",
              "ngnzAAAAVAGfAGpCPwAc/+P9WIwLtxkZ6AWf5x5vIQ9MaxnpwHHV8lralOej6Yiq1WMraHB29alg\n",
              "oEXV4UJkc8An+cMlCc2kkhkn+QMVjnFOfYR+ZAlwuNw6YAAAALhBmwVJ4QpSZTAhn/6eEABYa7tP\n",
              "iAFXY0CipJ/O3Jgjg/cl54ASD84WjAy7tavwlPxpbbWThW7pVoUraiwMeq5exEjCp8cVlKokAlmN\n",
              "n0YZuvWwPBVLeuNehEKWTU0/X1AD1PXMdhmKu9Vkw5ggZfNByKL1e9svZCbydvNjPGtjqje/7p+4\n",
              "DoTTJguNteFEkd2eoCv93BnP+6SWYmBiLbTi7hq66oswOxyvjVNVG7rV7gXIQBdqChFlAAAAW0Gf\n",
              "I0U0TCf/ABfmdphL+CotFU8WKM6cgnEqy17MgucqaU42VTELl1HIEITHYyIg5BflOeQO1DFutGDg\n",
              "LmRS2h2GfHk8SGccVwRrTQL1YVw/QyYADFDXkYNaxHwAAABHAZ9CdEI/ABxY4an8ZTxnnFBfLLUU\n",
              "HVUBMPmAcVGGU/rGCu5rY5dHwEm1mAtYjLF+XzO7WdDB4/1nbXMUisWJ0NTqm4tepN0AAABFAZ9E\n",
              "akI/ABxM9gfFJEJFt232CTXPycjgmVW3mD8XzkeBRQxhl/T0YtTRb9s8cE/yLNgkLWWO7d2CjZhx\n",
              "DBtyw3HU/JOBAAAAe0GbSUmoQWiZTAhf//6MsABZuZcgVcQpgA0nq0VXaTrjdzEDBJkRwz+rGZgl\n",
              "m3IAVF6L6so8BsnJaYjqlMWVNmO8Zm3L9ZGzk2anx/wBfMI6l2cAmVUG9tAb0ASLSJwLNixJfbIu\n",
              "sbPxjmmZqU2j/ZtSFIHtWoP57LOPgQAAAEZBn2dFESwn/wAX5naASy3hpd/yyprtHYiay9TknLie\n",
              "Cw9LFJtckUY2uQ89FvODxp/HRh4KR8Ek1GRXIbBdZ/OGho7GnZExAAAATgGfhnRCPwAcWOFufQtE\n",
              "d27eVdUnzonpPL0WgVH5tOZDSbX4o45wHGjRfbdb+Zh4HGYkVYfvp5wUeWxwg1JjHv6pV3IBaGc2\n",
              "Uju7V33JOAAAAEsBn4hqQj8AHE/1P1lIpqxVXl0/RUdIhBlk4SfEsl3HrWDsz7FUEtRYlsBsrBX9\n",
              "vPnXSmaikPgcgC/2vGhetooGPE7oPiJQBJCyCJgAAACQQZuNSahBbJlMCFf//jhAAU+ODHcYAiYV\n",
              "i9SCWL2Uhr6mPW76+AInbqFc7s2IQD18GKvqSBF6ajRphrhLk63MDjxXZ//9PMtb8W84rof3kQsW\n",
              "AaBXTboTM1DIRWlOwW0TdAMFJpixL9R5ymXdp2ElcW14aJxGqAMPefzfHoMHqFx2XMPkT5iX6r/p\n",
              "70SS6AypAAAAXUGfq0UVLCf/ABdESODALr4USmg6B3P2T5ZwsW2dDUVtPwD2WmQxNFgXUB1WAJ9Y\n",
              "LDG6LxWWKc00HjTDt6tonBYPOCylB1oCp9JDNFZdOccRtUARS57/g7HroPQ5YAAAAFsBn8p0Qj8A\n",
              "G1T9/YABFWAWDrHzRtJM+FgNHLKHFBrcQV+fliJ1ItpO/tdWgWrhX5chuMPflH0ceXToz3zDRh8I\n",
              "JpQR6ncWugABB8SStVA8qMMCs09XcLIwiw7oAAAASgGfzGpCPwAbn99GmLBCRbdtLR8rTQN6apXU\n",
              "71P0DXDbK4eaW253jlxNwiu4f0N25mqYppMiaNiJ2Vd98SbzeAEcOTi5ysmjmuFpAAAAUEGbzkmo\n",
              "QWyZTAhX//44QAFPkCQegJn5B4JpKdAgk7O7IP8AIpCsYmVM+gMGT1q2gGBk3vakrmcme+u3U8gj\n",
              "tLzLginzVkBhCDOnoKOW+wnxAAAAdkGb70nhClJlMCFf/jhAAVH8zhJxqqdmqeQcuoFuGqSOCtuH\n",
              "Qj0oZ/nhdFdALfXW3GRUXeDescantN97SE64K6VwTF0I7AN0XJwu0KPZkN/LdNlYVhm9XCXKYcjY\n",
              "vKEjoEERJUaxzYGyizxJ0Tj11kuaZg4lK2EAAABWQZoQSeEOiZTAhX/+OEABR1IoHkNZOwl88iRk\n",
              "MVsPxBmKHgTTeUc6WRjp1WbeA4YQkiibqc1GAZulozezLNjEjAlGooD8XYPHcrH1VBFLa/UXSD6B\n",
              "4u4AAABUQZoxSeEPJlMCFf/+OEABR1IoH8j5j0o/vVjaMeGhD4Ha59ilBvnO4TjxJlby3gqrrn/U\n",
              "zogrtO1YHq/ZgKp+UvdCNpcs5F9Ngy1AxBAuzueMkFJAAAAAfkGaUknhDyZTAhX//jhAAUdOqwrP\n",
              "ADPpFZybKpoLfCXxtSDOF9T8aTrI59Yh1LzPgoKv/ZGCVZfF+/Qp7DywI8N52FgTYX3MDHctqMhB\n",
              "twKmgnSHJBc0z91kRLsbGhxuZTGlfddf5mQFuaqfN+Or/NYNvrjQ1Jc37BlsSvAxoQAAAFlBmnNJ\n",
              "4Q8mUwIX//6MsABUquSyABxdf//SLWXkcQ/xXoz48d9RW7gJbDQ6R0Bx1gPdfgptJS0YeGs54VVT\n",
              "G3dZ16HwGdXdxcIelfrTF3hwr5r4kYV7MjcvVAAAAHdBmpRJ4Q8mUwIX//6MsABUquSyACO2f02f\n",
              "MNwjQm6PMBRtG6h/RCrgTxh0IFKSagMubOEZ2+GO8gyNNRBY2nceQqhw7OS1w8/4fkatEuEHR4xZ\n",
              "LdxshuKbe6M7nxwOEzuJ+5c6HfzqUJGWb6TGJzGYvQhhJUKTgAAAAFlBmrVJ4Q8mUwIX//6MsABU\n",
              "vNcbcAcpf/390BIYd57+wsAnqdteMPUmgc6Uoltgixs1KOOmEjlRm9tgKbVsLNB+4LgBJ2nTbXf9\n",
              "cUkO8PAPFiGtoXZ/4Jgz4QAAAIlBmtZJ4Q8mUwIZ//6eEABT7Bv0ADaaNvkbzOHuDlyyEymps63b\n",
              "xy27RDmtZaXo6lQZKkPBGPkrw1bH29GE3WDl/1eyvzqVDqd7RrXlcD/BzFxgKkfltKL8v/bRNJuT\n",
              "/6R+Udv8uUF3TbHkVRl7moSYHdXWC937lMWX9sLgSj/LB1a5qfHIghOfHQAAAKRBmvhJ4Q8mUwUR\n",
              "PDP//p4QAFR338E2+sCnx//wpQy5ldwLcS2Ndyy4Hhxx17E/TRnJuv4ALItborJq28ST1ZugmzhL\n",
              "3JwBswBz+nJ6t07la2YixXB2G06l/vwzohDeBcGGroRnl1rEtVpJLVMPLCAwNuSZ1AY2N6c7bHOL\n",
              "V645FRyBkoB0xRDEeIIw3JfW9jqdO4u39EsG+MpmRzDa0TrW6bsNUQAAAE8BnxdqQj8AGwJ3P2Qa\n",
              "xy1fMFMXxLKnoMpVH18X0IC2Yf3Rf+/Lz22NLeCj3DFEvzvAs/qvjjOzGEDOg9I7zakeVOtlWKMc\n",
              "+beVIyrpnGFBAAAAaUGbHEnhDyZTAhn//p4QAFGrtNS79yVFek0R/IgIkUe747ADDaAI07o6PiIP\n",
              "Lq8i1ml/7cK57yHhyzTyVMEc3X7vcQzCpujg2kemfcPmO3RAUCenG4qfqbxHEVrS1H9AUbNZeDKP\n",
              "c6H4wAAAAFZBnzpFETwn/wAWGtu5uQtkxdOt7Tt9Z+QrySIL0y4wagXkyd8swFH2s/5+lcrJBpaW\n",
              "CGdcd5NxDHmK2XL3lqYN4oSUVqlcXT5vPhozVTC69f9XSTnUgQAAAEEBn1l0Qj8AGl4LDQC0Me2g\n",
              "vNbvq51zjqsHSOJ6fUTAgjfAz2uGL0DdNbip2C3HxGYhO7yOOuLAkmVQ/WhXvHJBmQAAAEEBn1tq\n",
              "Qj8AGmifglEgptWcH9KyD/lnHJ3IEd0u9CeMWrgcTIewwlryqFF1wTLKPckxA/Rv9FfgaPOem/nL\n",
              "XgDjgQAAAIhBm15JqEFomUwU8M/+nhAAUeWoDMbqAGjJ+hlEu/T8n1TiapzZdaZFA2md4cPARRnP\n",
              "ALAWUV40RxX7i6h9wxHzzWMlqi1IdO8ANNG8JtIttXz0hH1urjcBrBZlF8DUfVZemZ8eLmCu6CXA\n",
              "1OFQyemmdW91BeKLZO5rwzBpb7HhDXWnKuxPoIL5AAAAOgGffWpCPwAaX96os/WaOeJpBhmWobjC\n",
              "tiSHBRkN8SUAtXaZWCRHZbW+Qd1d/VoeGCMM9HuYy6h56oAAAABoQZtgSeEKUmUwUsM//p4QAE+U\n",
              "SxfcAP2mrIFd4dzwxvy1fU59GXw1unn6TUtW7sSHT3s185Psaniiggle0TtaOjDysxCCEzHSa8bF\n",
              "qUa4RUcoozeIElaaYK0AMaNOwvvEhItsaL0AC4gAAABFAZ+fakI/ABm/30aYsEJFt1u0xagkIYW+\n",
              "9BhbLniJuiblNu5QiBVFyGNPmJeZdD8g8GDSYx0yCSJrLwArGnem8ZRbwtSBAAAAnEGbg0nhDomU\n",
              "wIZ//p4QAE9rjlS+tySuTkYPeBrZoSBalC7eFlRoUst3MWPq6JfpBm6EuxRL6OEqrSos1/S7TGgr\n",
              "Vw+Ss7RNIaYRj5RK5eOsVgNzVbgWhRdDrgrJvHKL16Rhk1aaZcuWv3AecVXYAEXNkFli1Yge2vZd\n",
              "LAC1SGqqivUQSYFdf+/2A794nQYzIUrmvSZxLvCwFulDFgAAAE1Bn6FFFTwn/wAVlEjhOB5CV3Ia\n",
              "6YXibUouyf47gJT/NkOp17HO8oL7MF9bHO/PhDRcKNiCbfNsiAFQ7V3zy6wRPFyEUu3McCE6Hgrt\n",
              "/wAAAEIBn8JqQj8AGb/fRpiwQkW3W7TFqCQhhb7TsX8+2CrzhUU8mAfu7niLZtoEgCQ4bKHq8YPx\n",
              "F3kjSVA7hPN68teCTcAAAADNQZvGSahBaJlMCGf//p4QAE/+LwzrzaAGlC4QEvQ3sX+VcLL9/9qt\n",
              "oG9MP35FCe738FWINsVmgoQwk0LAdI+LdvbXQTzDf3wqiUU8SkAd6+d13GsdgHWCV06axjTO5oK7\n",
              "enDCheSNiW8Z3sNafNjhyrdT9YiPZkzAcfvnv41lx3AHLzQ83mnuMXuIchF2+p21Hnk4B73Z2zLZ\n",
              "knBNYWoURDt0nbIGFe3GmLADvCBoB5ukChMB0P6xUgk+cQJINkdRVhpXxwhLTpdFqA5XiQAAAFdB\n",
              "n+RFESwn/wAVmrdqW7cFTw3fjYEf3B8ji4s4EGh1lRg0fK1ihk/MDZJ1B1IxhtwNQ4eq1mhbvqus\n",
              "wDCZLHvcpnNL7BSbMLCczgLGSXwDp2o6X8xwEXEAAABPAZ4FakI/ABnCZf1dul22cTw3Pot34ER1\n",
              "hNqH0NOZrwOWRNZacY8EamYVZ7MUuYxeOgoMr5+OiHG9Bp+9k8vSCOlcH5w1ij2q/n3Tn8TJOQAA\n",
              "AGVBmghJqEFsmUwUTDP//p4QAE1D1XdeQWscHIaTjFwzzZOGn3Df0hCIjkZClyxMf+SZj7tKSemI\n",
              "lFzHfr1+H+R6eYsKUMA9Kj6sFa7Fsm1OQxaoPE9trVSNvWW9vt17AJDQDLu+RQAAAEIBnidqQj8A\n",
              "GR/fRpiwQkW3bqYJyWqTePtamuB+U1Uqe5MJQgZTCjl7YaIwCkOQCf2gFIoeTnKwU03GryFm9eX4\n",
              "F3AAAACWQZosSeEKUmUwIZ/+nhAATUQeMPqPgAz2mLLN2ErgatzJIeVpjI6Q4WMh70eQRY/mmzDy\n",
              "vB7GBZMlC52/FksZAMVo45s46rkRn0odhbWA9sRMPpndbbpGIs66MUHhLWbjfkJewFBc9+iwocc1\n",
              "yAp+jV6BbUux3IhqDv83zCcTzNCfC36bEWxCrAn7v8WawxaKxbRW+yYiAAAAVkGeSkU0TCf/ABUK\n",
              "lWsnWZ3heqwSV4WKoVpV7bLQT4Hh/nCQAeNJpK5y3eW8c+TSDKv3otkHqf0j+i5qR9WpRJ3cCYlK\n",
              "0Yu05Lz9sfJXhU8Qw1Z9AC9hAAAATgGeaXRCPwAZKccE11AAAcZlijnJcVD9tVuTjh+8MFUjyiS2\n",
              "jyYgeEAMjCcS2nl/ZUePw+IyPJYvp+CLztTcs2UVg/HRqLdbLohNwz8xgQAAAFABnmtqQj8AGSif\n",
              "cMmbdjW3Fc8O+55QKi6FW2Kgz/z2udLoTsSk12uhZKDvZ7Gdh6XHB7GaXWBY29ISMop0+59r2xTA\n",
              "gqq2kRYSDOb7hQYEwAAAAGNBmm5JqEFomUwU8M/+nhAATb57DJc7M/sJ+V8e6tN6OdO4DnmKwSiA\n",
              "rQz8VsQhVmmH7dnxD8NNzD1yobYnC0yssjVrW2vVFTfOdd1Rmv2rTJMG0CvKsr+Q4cwNYl7r5vsF\n",
              "ibkAAABAAZ6NakI/ABkiZf08D3XGNayjbYd/Axlj3qiUmJNtnf8u/5tYN0rRuktdOAfvTUnriYbf\n",
              "3g3XnLuy3ONBDe3hWwAAAHpBmpFJ4QpSZTAhn/6eEABLQ/nd9NdXKVhbkqkGkoyXhtGQVSnytQWd\n",
              "/HCo9Et332E4agUUwZ/i/xJkCl6JDztgVygoRkAdv3ZJ5exG2rq/it8/Evrwwla/2wPUF2brI4Mz\n",
              "LwqE5nDTq1oyBQfM8KPMrg2mV9FnK/Q5YQAAAFJBnq9FNEwn/wAUdEjbgQxOo/9vTSyn9sVEzqJ7\n",
              "JC4/dUiTC3jt8GIr1Ng8VHw+P9iXeADozyBOTnHDjTI6TwF9FzYkfoVdSxZ5tGgbjJk/NwdMAAAA\n",
              "QQGe0GpCPwAYf99GmLBCRbdbtMWovbrySvGHc37ZQ12S0l1RbqXIGIt+5TcgNfDCn3j5XAzlSA54\n",
              "Nqjk8sTiuMnTAAAAsEGa1UmoQWiZTAhn//6eEABLvnvaH19Y1eyAIv0r4Wj1gs9MsuxHyaBE4xQy\n",
              "Y3XJUMB6GCI/+ldEnPyooCQu3Cbd4Vd+CbMQzTXXhRMEFSqBXSdusD9gxHYSmxeo99wVX3D/RWEP\n",
              "hVDzHQ3wb8S5yLMKqFDXk2x4VmK3fc8gLyGexN46IjY+HFIqOeqNhYUqazdtBYl5bqujzrB5n0wS\n",
              "jZtIHdUZkA6rkI4af44bKredAAAAe0Ge80URLCf/ABR6micoAQE7M5sR4fDoEvDTupgsfKkES1La\n",
              "71fi5qTEbG6/y4YWIi1KaE6ru1MV9wQdncxxC7cUEg21DPIy+nPmWbwjQzXlM5/vb8WQ4342H8sS\n",
              "PwlmBuVUk6edz5HIR0Q/v1RSA6VskQqVKrmoyN8PWAAAAFEBnxJ0Qj8AGH4K5h/CnwAtu3XoeSi9\n",
              "z8USFeN8QQcCJiavhesp+VEJIONbUIibZm+v6w5WfKpqXRDKvVTswu9m95dqxtV50AodUcN73ECA\n",
              "fkAAAABNAZ8UakI/ABiCdz9ZHpCQEGfX3B4rZkjIxV+6l2VDGLprWruMvtwJizsRd9oUGitbsX/I\n",
              "YGGuvC01dhvx3CSsjIsyTAOhCU4wn7tWsoEAAABOQZsXSahBbJlMFEwz//6eEABJQ9U9NGFBHbDX\n",
              "6Pem318yOaOgoTAgvfRlc6UY2PG4k544EpuKn38tPTeX5yDoE/33xmxCTAIGn5O6U+pAAAAAQAGf\n",
              "NmpCPwAX399GmLBCRbdbtMWoJCGFq7yjosQwnaTukCbxiU4omkdnhOMoXPFYejYW/dnI8IA+k1dP\n",
              "rYaonHEAAACYQZs6SeEKUmUwIZ/+nhAASUQJbdewRyABxohE2LFOj7aorv1zN/TovzzWvTs8wnNy\n",
              "nJUvm0vL+cj4yaLRu3glKynA2Vyyr1QQulcyKXZpaBpLdbAN+OIEBC0hEZSyPSBDMhFg0nzSWYcc\n",
              "M31xIj0VKGUgbk/LJsthnWG0Gxv+PFlGW68R4GUDPk/ep1CwzKKDb8qmcaY9BvUAAABRQZ9YRTRM\n",
              "J/8AE+1O8lw6/gAF1boBEc0GlZWy0aXC1Lpelu3uaJNVGjcDJunrwinZeso4wOA274Rx+Z8ZNTq9\n",
              "DdNUWbgQbxQzMlO91OuAE+l3AAAAUgGfeWpCPwAX399H7G/IAWLxiwCs82XYFzdawGW0NqWslDqJ\n",
              "uFC35G/gJHVfWskhlYTZDZ0MUIHjTU9N6hSZAA50IJCtZWWdEBWM6L5U+Dk4B/kAAADzQZt+SahB\n",
              "aJlMCGf//p4QAElEIhPoAwAC6hg6vNQ8WTidGSGmQy1z7+/lt0snhpZPpJ/LxekI5L/9+hmz03z6\n",
              "lOMyCzoe30jCzABvvg8o4+cCc0ppP6zVJgpAkWwubI7IxSb3NxHOin5hAcw+YH+2mdcKwcRbFXcM\n",
              "vRrqvzdJ4q9wvHEZLd/5DQdpTnlySaZ1086w2t70BtyzmoBSo7qx8m99g+XNDOq7ZCFhHpXZyz0Z\n",
              "45xtrAzfPFhLo0qGG2MBAuA5lL0sbkz0PULpEhXDWvHq35t9bzJBm3DGblmm5OcMMNPml7BowMe1\n",
              "ajZI8Qk1x32AAAAAikGfnEURLCf/ABPmUjavVx1XIgAFjOsBj86X6ckN2nEi3OebzC7v6LrkJjk/\n",
              "wdP9pdb6elangQ6uv//EWzwUr6x9AYnXDD1d96Z4m8ba7JbSfJgfR6V5r2DAEFSCuR3sV7tdogHt\n",
              "191tuBCXk9uONwyn/OsNqgWCY+y7hvSOL2Rb3DrNtawBaU7CywAAAD8Bn7t0Qj8AF94KMgGwoEul\n",
              "br81a5BfrlcKx6ptKVrO8CJDT9NMaJCIetUqbc2n1ZxgBa8ml7Hvz3TzTEMuZGcAAABRAZ+9akI/\n",
              "ABdCn0f5YHcIHWEWPpXWv06RTvbWzvjIkDg1V23A6v7nd21viI9RV0TH+pqz5MlS7FIkXXzTABcN\n",
              "24n6cu8a8X8GQgQ1W+CPCIHhAAAA4kGbokmoQWyZTAhf//6MsABIFO64AHG3JdLZkPV8whmtPbQ3\n",
              "+yUmbk+NkIyTrdNPoItfYJ+RpW7T9zMCjaagoLPF4VxMPpCKbwEbuPDtTdGOHmOB2fOm3O9OwClv\n",
              "0SPzBJhrfrPx80Te5enMjmNm0Gz+2JJV6KskHueoFO4A86aiLy03RRM+dHlKAp7OyC6hWm3GG1XW\n",
              "+jq8RZ+Ug41XqtL8mHpKGkGZErIJs1VL1wuUu1gIHlymVy8by7WVyXnYxQnRSSS1H7BQGWKmCPLo\n",
              "HJ+Rlmoa0BcGPRlTVyqjIa/P2IAAAAB3QZ/ARRUsJ/8AE1zaJygBATxTp2y9W1ON6TA9xjKkUYTd\n",
              "rfoBfTClEGMZoJhdEurFp04vKfbCwpZk6FRTRBdMihoQN4Fc0wvbAsMoG4BVRrLnnBpldrEvsu2V\n",
              "tnQRTjptSHI8nX4q/694oLWtkY5FGKtngi642YEAAABRAZ//dEI/ABdAyv2GEOKAG1o62iQMfU/D\n",
              "TY20j2PW0O0gUwrUe3tUxgDAm4fRfCTvHyobH7Iq0br7cnX4W5NoggbR6dPwVu7ccz5Cy458GAfk\n",
              "AAAAYwGf4WpCPwAXQp8eWFn/QgxX2e0JSogJLIGSUimDC9/cs0bL71WBenmxaQgKwO6gcE1rO3AL\n",
              "9wp+IiUHUodSol3C6eJEWiuRkUtTzvGVQ/S1TDhwdoqq8spzS5jDi8WLgULG2wAAAKRBm+VJqEFs\n",
              "mUwIV//+OEABFvmWwgaazAFgUv3RFBq/T9QCywRMGvKccWdkTP4EETLtrGspvh/ggPavznD/e4tR\n",
              "YdcEgUysN4HBTtkT+4iuhL9rkirkXplu9DcQ7Gtnw5JqXy9bxSFFyJfTuT/Tha9bUmUO8HC7Rji7\n",
              "BemGV1IBYc+pE5YQslp4PWYtdQy3zsFcIAfClkVFDsD4L4tzlaQpP5KfSAAAAFxBngNFFSwn/wAT\n",
              "WIj3WZVTTOFRjL5kme+s0nlEry2gFk5223nKDC3YuZmOCzdme9E8MPgX2vS+9F+2GtQmvjkiFo5m\n",
              "9G0mwtvZnwLM7aEeqHSPN7OCPf+bhr1TAwAAAEMBniRqQj8AFrtfhXIbXUnRZZtNGd5L7UkUhbJD\n",
              "MhUIHHmAcQkmSuyZVnPfIMdrJhO7NlHvB2UP2/KpE7REbtpcJBQRAAAAcEGaJkmoQWyZTAhf//6M\n",
              "sABGAPlj8AHF1/Rgtgguc07OS97eZdMedJg3p9JpuKCs5MH1MsiLDb3/z79DhfLLyHADMD44ZSY1\n",
              "+eUOOVKQc7y4ORLNFHJfWK2U0dRi/Bi9rijqL7RVj5ZymJLsp1Xf70EAAAB1QZpHSeEKUmUwIX/+\n",
              "jLAARgIKrOAEX7izMov5U28y6bc6TBvnMTTdWFZypPqZzEWISyU/KqaI/qO+Nb+Lhk4Kyk+1cqyy\n",
              "uQlAz/iRiaOO7XHyNISztEVqtzJ96auvogQZSURnKwy8Yee8NFZwTNi+ap13sxixAAAAaUGaaEnh\n",
              "DomUwIX//oywAEYpYYXHrSv8/U79CLJ13KkGtGGYYmrUibM6YoXTmUb9BVUdGY8gtsgtqPYAGyOE\n",
              "oK99AAhKUOj4EWRrFjiP8slPXr0Y1F/6Z6CSH/xz2EFegqqBv9yJoaJOVgAAAFFBmolJ4Q8mUwIX\n",
              "//6MsABGKWGJQEjx3o5dEwCmJ3hbJYfK4vsx7GUrBOSZ6aI1S1sgqzAG1hJ9warFaP00UowrWIQr\n",
              "X29obLX6qwgDLiXy2LAAAABsQZqrSeEPJlMFETwz//6eEABFYcPt02CUt/+ecWUYY4Qlq77Hrnly\n",
              "SXRi6wt9E8XIDFukKBmJfNRalbsADYbSANApgqD3oDptBeS5dDVZQFlxU+3kuJ5Cf9DMORq1U2M4\n",
              "6vZ8scNTGzbBjUqBAAAAVgGeympCPwAWqAss+5q4hhOgDXTSPJWo/PMHUYgWuhd6ItEjpVhM+MsN\n",
              "LyXTf6Kk+tV4nSGr/iUnae2wLPJgtt76Z02IqUeV13qYPTTr0D93a6+xOk3AAAAAg0GazUnhDyZT\n",
              "BTwz//6eEABDQ+pyQAVQ4Tjk+mBBRHuy7KnepBTKJudSPpE9hmEAP/ID7gGtG6PQVUGzwj+bMMHh\n",
              "5BJmzPwWS0BGqAsZGT69jLjGGZRBzu5cbp0DyQvqNJkHB8bLgo7VaD/PWMbV/BNN3aUd5JYbp9n2\n",
              "5qXFAU5BImoIAAAARgGe7GpCPwAWIp9GmLBCRbdbtMWozLrsc0JrN0+lglQvz1GXiWgfsEGRqP8X\n",
              "mEQKpKKZrwFA7wxdVOK/wPChcpepqv2FGVEAAABdQZrwSeEPJlMCGf/+nhAAQ0P53fTXVylYW5Kp\n",
              "BpKMudSO4U5/fcsR24Lv8wRDS93z6qeXusXDolB15ImodMMxircyynGL6eSV9lqnyFLwJZgTmc5I\n",
              "//JuE6ihmIuBAAAASUGfDkURPCf/ABIdPGzswxWJAdvVyyh97WK1Mk+4ZDz1h7bJ3VezLJarGqQL\n",
              "5rPtbfHKZD5pISgYxCuwcf78qxvNM1GS00LFh3sAAABAAZ8vakI/ABYrX37/H65MQDl6Wp/joSV7\n",
              "E5AX6vCK2TWxQmYBPClq415xDMJeUtHvTrP85iO20R3RLaTTWK+I2AAAAINBmzJJqEFomUwU8M/+\n",
              "nhAAQ7pvOs4kCV4bzJabaG09x86fVTmkDAhLEMjDiKZsqcXDOXvNPZAaB2d8BkuetHdIWaEVV258\n",
              "yk+2oARHewAQ8iAYTFW89ptVes+tSFt//Q2NCyPjgUJHERLNJTHp3zWpTRN1Bekw4Xj3ekGe0eOb\n",
              "dNkBqQAAADEBn1FqQj8AFiKeqLP1mjniaPvYf5ybW5F/QKicHWbIwMKLzKfoRhQT62tMRkIscB0x\n",
              "AAAAeEGbVEnhClJlMFLDP/6eEABBZBsX3ADPkGruJrGNwRVYyj4K7MXeF/Y57bziYgSY6J4zX6M1\n",
              "lrKnBRxYAjBPO6DiCByS82Yqb9qzV2vob+cO3tgcAp3OWLR1hfz3XmMNv4JvgZ/hxcuVcTtVFdFH\n",
              "+VnM2h7kaZhzQAAAAEABn3NqQj8AFZKfRpiwQkW3W7TFqNKm3OchQp3+ApXo9CKcrj2reWTEj4u8\n",
              "AND8jhpvMVqntpW+274RhknwuIeAAAAAcUGbd0nhDomUwIZ//p4QAEFD+d3Xppb9LqaRfjNt8IPD\n",
              "w3iy7RRe9hvGZGtRndJcPn8MTMsluPMMUvdpzmAL/u3YIY7iiDSGEbtT5ADcpfG2SZ/tkmNtT0C3\n",
              "nMBEL4WIUlJgEVY5oDbl13zP4921ZQOPAAAAS0GflUUVPCf/ABHWiN3R6InrAlespX5B/C1M/0uo\n",
              "ZdHmMI07sfhZPoJQYD88dQYJWPZay7AKW2AxzpjhccXfrhuJmCZFvLYgR3b0gAAAADwBn7ZqQj8A\n",
              "FZKfRpiwQkW3W7TFqCQhhcG3awoDkC9a6rKZWMoMPshemMU0XZbt+PQTYUYiizCo4VCgXsEAAAC9\n",
              "QZu7SahBaJlMCGf//p4QAEG6bzxOmacgBA38/GfqXUyT+vHbzl6CRBhkUkNVC7Q4WIkJjAnRVEL9\n",
              "Hn6MgETvNjGsWap7F7Gg3lALVIKEjDM2xUhTwjFqoDxHC2olyX/xOrG54XAPrn/+ti1lLkEbaVQX\n",
              "S26FdkmM+CbbcjG7F9Lp/1zEqRATT0zyS5CY+z7pqhelW6PPVRKWpwb2cll2m2PsgTf0hYejrVmB\n",
              "vj+Uz3k5OhF/92u7tL5pwDZhAAAAUkGf2UURLCf/ABHYiPdfGL/4lwrcta1MpQ9GVCJKgNa97yv/\n",
              "XhGD4oVQ/0QhASulqLoBv8tAAPVqQjhbxocnl6i9lnXaX52QvJ5WSWPnwdgVZkAAAABKAZ/4dEI/\n",
              "ABWchujR5ie1TFGAKLHD33xZLy6gRka8TI/DoDtPZ0yyIhSo13IdKkOTe/3nojAwLLOPFuGkb9GE\n",
              "fk0q8qnCdJ8SYEEAAAA+AZ/6akI/ABULX4JRIKbVnB9tDqWOnI2xpbTAexpUqbRkIbnF8yBTGh1a\n",
              "pGXJWnVs0GSDIkLGDnTUJClgaEAAAACJQZv+SahBbJlMCGf//p4QAD9eiznE59iLUU1esBm2+ELw\n",
              "yKQVr250fT+hSB1E5kW3GYMmxqN+EtsUApuGngP8VNdSpomBXPrqOnGKftM5yx88xhUq18rIsiNr\n",
              "llq8A89IO3Ip+ogz+MDgGmHxtbtt0x029g9TtLl/9y4a1QNFn7pWBYL9pDcfcX0AAABMQZ4cRRUs\n",
              "J/8AEVaI3dHoiesCV6ylfkWD8c9Ipc4cSCEYt+nGLr4SVc5EXAoL4euoCBuI373UOiQLjDf+72RW\n",
              "/xGcmBkgKGf1G7hHTQAAAD0Bnj1qQj8AFQKfRpiwQkW3W7TFqCQhha+CalZNJ8DcQ0x4tsoYRuK7\n",
              "NsyvkdmcR62s/NgrXFJYObVUjwKSAAAAlEGaIEmoQWyZTBRMM//+nhAAP15/8Z4SAIOTiVuuha/s\n",
              "20lP/PghoMtvyNLCeqsI3YU/QL0PGdAO9TMXjceGsQfc/HPaPZTLcVBTlECoAm7bMIXCGyi3rNyb\n",
              "iTyDXrXhnBCUOTsOWq11bGbgPXytfLZxTsfL0lHgQFpsvD/pQTxtUy9Xtol0gd/2sPyJFaTglZtq\n",
              "pmQAAABMAZ5fakI/ABULX3DNX4pGLLucE1NLurDWzyWKl25Y+VsJx8fTgACbehPzhh9aj1qeyxt6\n",
              "RKZK0I09lQaJrpQk4hWJ0g5hWGWowcESsQAAAItBmkJJ4QpSZTBSwz/+nhAAP7wnOs4kCWqycmSE\n",
              "f+XudgZnNN+2BkbOBazxxXwwpLunNn7gBLSf1W1gMbxajkG6LMmU6cVCJ8dTC32OnCsNY9FSY5NO\n",
              "zKvGftEXdO4D+p4wHwY7nELGGF0lXoYzweDyuIXMgi8YC1Yj9FqzHdhppbJj7I4y2T045eASAAAA\n",
              "VAGeYWpCPwAVBSX9PA91xjWso22HfwMfQj7XM8XuLsHMdDa8JqaOr813/QH1dr8Uvqp/Jp/IqPu8\n",
              "2BWCtr4fujxNri+zwOunvnTRl9Ta3kErKUuwgQAAAIVBmmVJ4Q6JlMCGf/6eEAA9/os6AA0mLvx3\n",
              "C1wwdlctzf12N5Pyr01MN2lhnfvOxccrSyU5F9hSnFXrLinKdYtFyskCXZRoeiYaoqEEm+1MxwBJ\n",
              "8BYbHTlqC3xlvvoiDRpgjlP2V2fEoqMqttyg3YcJmk1K7rbt5OyooDUCeQvMUINvoh4wAAAAUEGe\n",
              "g0UVPCf/ABDWiOKbppFoigkGomMKvMuFja943YslvTS8RR0FpTpbeDQuTeczwtReLI9TFKWik+9e\n",
              "z07TWZwE37p9gapFY8Dtx77aaqHhAAAAQQGepGpCPwAUcp9GmLBCRbdbtMWo16Q4WA4yHuvF/x2N\n",
              "knBxbceHi3/qmXTWDNp+xpkeF3xRjvRQkQUrC0ovG+a1AAAAfUGaqUmoQWiZTAhn//6eEAA+XCc7\n",
              "526SwArFEmtxgIm9Yct5fksHHEj5bxK/g3I/mnn8IdFGKauXzIUzrr0FKrcI6n9GQCRCf5J0n6yd\n",
              "ua9f3007upU6CZi5OPQxsY01W8K1MiCC+iocEu+Df8ZdWLC+00zlvUrDMATSUI8rAAAAOEGex0UR\n",
              "LCf/ABDWiLU1a3fEuDkS9ne8gVCQPqSyFc94AeFWiLu0rmmQdJ6CgyyfOUZqKDiLcndBAAAAQwGe\n",
              "5nRCPwAUcMrmPYFXL/2Ry/Q3OL7WzwzQweA6F0oCu6Porqp1Dgr+ZuzZp3z0qp1M6LKftFEdMlwZ\n",
              "tlfUMqhakvAAAABCAZ7oakI/ABR1Jf1wbRpWdhYFle7nNgfwpfCsmUizCqxPstsqmghhrEp8/CAo\n",
              "hwK4NvA0vnr/jcIaJtEWU+jFoDFgAAAAsUGa7UmoQWyZTAhf//6MsAA9WtPgaxIAOLsA6HM6el4K\n",
              "/pYwqTHx3oQlkFw7egmm1hum1L1pkR57TJraC6xva8/4sICwwtWYbFwXHfoJEHbQmnAWnKEa0HXj\n",
              "dSOlV0/Hm3NfKFrQwJBihiAHsoBgZ7md4/x0ikoTM0JrVY0qXorrgLmUZIJ2Q1kEb7pRTMCtlWYc\n",
              "biySFbK0Kuj4cpHdlZ9w/4Dvfwc+wBCvKyI7EOMv8wAAAGBBnwtFFSwn/wAQVpexAANCHZe/wFrg\n",
              "ostVuFzGeufdyKofq+gCl5LZcpcwkrB55OCv4nUAVBpEiTOFSfcO9Avx5O9KLr72AXSTl0YqBTWz\n",
              "xydjA+aETEp/cWuZRWksSMAAAABCAZ8qdEI/ABPshxJOtCZZDZG2BiHfYQM+f8UkW1NvXIBmrwuL\n",
              "+jyyw2oMxZuzxkdJcyaKQw3Q32wVhWgz5c55KHRAAAAASgGfLGpCPwAT61+JIZsilQNnev7EHAkH\n",
              "kNqQl3s5MyMX4cTK5s6u2vtXW9NMleEHxx1eHgKyAtptNr1bpHFYFl057CseNar3372hAAAAR0Gb\n",
              "L0moQWyZTBRML//+jLAAPPxs73HK57Ip1bl2th7ATwbez9wV6MdZDs87QaqVWR04BA5W2nlvmMon\n",
              "vFOxC8vpcOAVl3mVAAAARQGfTmpCPwAT4p8JyJjEL2nUdODq+yKqaAsbJRMBsUJ7dnqdwd4KPv1l\n",
              "wci+Hp4ZiLxM2shjf9a2yWYQNlfiVV8wTWkO6QAAAG9Bm1BJ4QpSZTAhf/6MsAA9XCdBmADXP9Tv\n",
              "sny7c6yWeKFsEKIn4WgjgEM7Fsjd9ucj4a6ANmbsFxURAmFBAffkiwwwLv2A88xyawy9FCfKwOBo\n",
              "7O24QrPO6ObMRz2i2ZpLAd59Fdze0u6JrLSAq4AAAABEQZtySeEOiZTBTRML//6MsAA7tmS10BI8\n",
              "d6OXRMApid4WyWHyuL7MexlKmTO3liw9a8APeJTQLKgUB972JeZ1/lJlk7AAAAA+AZ+RakI/ABNM\n",
              "uz5e0LmL1kue/2/nNC8v/pE/ZP4E+Qi68eAEjjgq0T1Pzy9MtlvxQJCgr1BrouSfMZrBsRcAAABW\n",
              "QZuTSeEPJlMCF//+jLAAO50MqcARMLcXGnxhviue/sLAJ6nbXjD1JoHOlJyTEB73KnWE7rHhCNiS\n",
              "mozJqVOn/Yp9GfAE2na9WGuoMWctMrk1LKttG8AAAAB2QZu0SeEPJlMCGf/+nhAAOz5EqnAETDBa\n",
              "/2xq2FOhMR96LKqtyOMV6BIlwD9KO0zSncN1LOAzYB0AdayAjtHzUtgwT2b6eOXkrapN5x4e2bUD\n",
              "fqZznkxb6jlIG8xE8Gtf7LNbGxqYScexSBzeAmHj8kIbHk/nwAAAAHtBm9ZJ4Q8mUwURPDP//p4Q\n",
              "ADs+cLgW0gCI952y9YzI8W7Bts8loa+6Yh1PV0FwLu01XCsLIqVY4Wk+57YjgimI916p9at/wvo8\n",
              "8xS8pp/bbuL/yYx6okDDg8C8lu4WNghy/s1Ki/MDt5TH/2Ffpw8osXLUJz8TFwqzhX0AAABIAZ/1\n",
              "akI/ABNWLuC0TGIXtOo6cIDc1TY28JUa02hqacxNsEaUFW3e9kXoWBzh53VBChZvyfZpuaXNHqND\n",
              "0gjCzo+7Vh3TOLKAAAAAn0Gb+EnhDyZTBTwz//6eEAA7mufgaCIpRg76WyDokfU6AZWGEjh393cX\n",
              "cp//jZMtEGTey0J3I4qFOhymBKHk+ty3zEJFg2JUdA3F0sO/ga6OvPbeeK/I4a/98UV6xDmVCKtu\n",
              "egMksVo49/4FGgyrXG3imIvF5vpEHPV73BXby7cVo+zGRreeGv8TTExoMNsGFbg8Gf0dIdVRZl1B\n",
              "HjRLwQAAAE4BnhdqQj8AE1fxrrL9avhDkJ9iyOTRplUU8cSWBmcW3+Qch9l+/NWfioeWr8i3sakp\n",
              "CVGIonXKQF9wYduvu8Sp63wzhidbbC1q6kUrb0EAAACuQZocSeEPJlMCGf/+nhAAOerdjgBCawtm\n",
              "GhiTJtkJTCUdlNkQ2Zs50iqtDvhTlJT2PEqYSc8RR2vr6Lp0efuysm+KYuOzgW120TwMkHqRZevQ\n",
              "crM9vA/+/bSwAL4NwwNRV2rVRsYd9IoHxJnv8s8KUIcIxSVwZYUCixyY//JX7i2jyhDaxxn6F5B0\n",
              "buaD5zuNuwhvLHahvGvIRUw0FMW3UwxVtFtF8tTY06FbajUgAAAATEGeOkURPCf/AA+Gt5RR5Wtl\n",
              "GsJeoZCCjXQGCow3mY2CyPpMJdZmTQxy+QMRcr1zE7YIMPXdbIxLk4kXPyar0JKzsfbX2O9NIMFH\n",
              "0ScAAABXAZ5ZdEI/ABLeiOvkAMwqf56nICveKGDA+mtb78BHynCikQmblLYcmI1AfRopvBLaDxtv\n",
              "O5MSPJKU4Mk7/FAOAQy8ggYWRT52O3JKUWOlVWJDye2ecKGAAAAARAGeW2pCPwASzQsxJtKhmvm/\n",
              "EJBha1N5ijbyxzuxVDSWuxYRTAsDmgOurxMcOdBNHiTOQcAx4pkskErJH7I2f2tlHGBBAAAAwUGa\n",
              "QEmoQWiZTAhn//6eEAA6PwnQQKwvtlCFwDeHjpPAMSJqthPvdl9ks6nEPmZ9tZkiG6HJzod3jK6J\n",
              "V+fWAR3Kr2TcdtbcJEEwzqflrUkwiQiZol7JN1fDWSa/p8REjBMHx3lfIHp95fWQ/tEm1jtzFjOt\n",
              "S9rbvBvytvJAVrXYW/QN9fJ317H75Qp50Osmrxjll6IuozEtnsj3Mt2o2SnHeDosku2h0hxLNSsk\n",
              "9RaXcp5tFO/GRct9YffOnxyrCEEAAABYQZ5+RREsJ/8AD4axUq9knupMj80PfBxTSDBnSpGUF6/a\n",
              "RdmlaZdkDj2GigOwwJTeJw7XuB7ZBTnTWa10OAY6AVHcqbhllv5Jne5binOWrxUxmsLVRMIl4AAA\n",
              "AEsBnp10Qj8AEtTi9SeAcS/55xuRiupoXzELGNYAA6AcrPdQfmnfBz7jd36wRtl6mIPoHDAbxhxr\n",
              "/AU9DSJzAGC7jCq8GMahTUGgMWAAAABaAZ6fakI/ABLX8a6y7YafvNj6AYPGr6eImVD3SljOQ+ud\n",
              "MohjmeRMqcONIU8K3EkZ/9HEcN7c2Lx+fmb4sko5Av/hbXDcdmcmuU0XP/UCwVcaLMMfvNFEpvZh\n",
              "AAAAj0GahEmoQWyZTAhn//6eEAA4loD9w2AEtTAPDgKFkJTC3ejoyDrv49lG1As2QjIl2AteFNvP\n",
              "lZmNXWN1GQIP2TKxaNgYimbcOdaM1lSJGBnheoN1aj+9M8Wec1DTcql/nvHuuFIxFcXFXhTi+DMa\n",
              "E6Lrr/5F8VfKhmIhTy+5vVdbUcS/tiovI47EnkhdKEauAAAATkGeokUVLCf/AA8onptW8myRo/pG\n",
              "+AcMxcnvU/p9Ohp4q9AD6nmXZ/hVJJgQ99OG/ckbYqxERx4N8QC9j/h3fGm2I17+cENlPawS47hl\n",
              "QQAAAD0BnsF0Qj8AElTjDhPDK08S/27N1X4T3y/D0YLVQxBF/NKDRWVmGcxfU6B1CRef8IrSCk1+\n",
              "S6p4dT7MsQO6AAAAOgGew2pCPwASXV+B2lAjobVODythLbJqlFscbhoDVg30/sAtmaobQazIezX3\n",
              "d4RHAlk6q9nJ8B3k4EEAAACOQZrGSahBbJlMFEwz//6eEAA4hzpX9wbM+AhP8E2qZfEts0zQ3IzN\n",
              "Pi5ALpahhT7FEnfLLBQKqkkr6xlEXSXFhu0M3AHvSyo6MXahg19c+FUjbNLtug5a/j2h7CEjOfIp\n",
              "byHIcrW4yjZWb3hkzjIsJHIHRTnRe2OwojflNeDKRkQbOYcZJKf/oRiRCADugQAAADABnuVqQj8A\n",
              "ElYuqln6zRzxNIZ6QEgAE1la4biyIApH8OpVfDwVhxZoCswvHnrqBbUAAAB6QZroSeEKUmUwUsM/\n",
              "/p4QADc/1YkLNkQQAczmHCq3T5fsPJY8qmW7kTIFLg1ZIwX7nmzaMvZi7JF52uaHKHRF82R38kz1\n",
              "Og/wwRhpf/inm20Cm7fp7XafnM63KVF0DYNE5TtGRItDkFh6/XgAJSC+JgDdHJOkVDTqRZkAAABI\n",
              "AZ8HakI/ABHWLwPikiEi27wddZlV6X1JbKswk/V4/HbzxHOesPFLWjrQIuy7r7cyzoGwvvCl450f\n",
              "REecoAVjTxayE7mt+LAeAAAAgEGbDEnhDomUwIZ//p4QADcr2mpBwtuWfRnYBcj1TBNPrjAr6W20\n",
              "ec4dw6eBrLvDmsN0O/Hx76JEfjuIVFAUFf/kHH5s5GiilR5piec8eqpWiOdOJT8HicU8RGxKmnt6\n",
              "0aRcN9BXzTaPWf3+mBx5QNoCPcfy3DE7p4gZjqCTZi44AAAASkGfKkUVPCf/AA7Wt5RR6InrAlh1\n",
              "mqcakbka5vL64RYA7tvVk/BEV3uo/fiYyHl3mNvN79xuv7DkOedC9SQAyIsABGWx6oTzgl/hAAAA\n",
              "PwGfSXRCPwAR3ocE1wfrqVPvB94z7lQRUgpMAo9v7J+U0SRC/IA8Syg0MT4VaYEbfJQxFnj/8sXN\n",
              "yKzWCrzRbQAAAD0Bn0tqQj8AEdYvA+KSISLbvBK0z1D8VThFtWAHyLsGIC7z53VSzQjVfMJcgEvo\n",
              "C8cPKSRAEdG2aQ2mdy7gAAAAf0GbTkmoQWiZTBTwz/6eEAA3fwaMuS2gBpQyXaFFv/onx+5PWkF6\n",
              "CZuSQj1XIp+IBknoaohmvmpUYrA5dJFEyhdmQdIF+Hts2idr6ugOEAdhT0VI3FkzEQq7QN3Oj6vx\n",
              "Hv9nIbnLzl4dufIOexhzZ9k3v96tMBIczySQ8zjQB6UAAABmAZ9takI/ABHVvjvxSgB+PrdCV51m\n",
              "CrG+oIweypS1hgB2P8pvgAltZJm4OPHfmZqguFjICUj4w4JVhDoxEArFuyRRlitfZBI5hLbb1L+f\n",
              "em0ouR8fg6s+of1/v4mCNwkviZ5mmwFJAAAAUEGbcEnhClJlMFLDP/6eEAA167CoDAArQ283SwIM\n",
              "oD3wc6ndiqEbeIrCVxgybFyOGsL7Jl+knY3BC86YAhfuKKEzIQUfwVL+TlkmWp/rMUWVAAAAOgGf\n",
              "j2pCPwARVi8D4pIhItut2mLUEhDC18E1KyaT4G4c+msTc7Kv9rv1ns9Zg+GkonSxzd8LZoinD0gA\n",
              "AACOQZuUSeEOiZTAhn/+nhAANevdoNpiQAaZF/XiF0Nza6QfnkcTo/p0VWQjDncpmUFI9YxXwKJy\n",
              "CZFzGOn1Q+PK0ZKzIhQuUm4ACyk8RSgSAsl8WWayqFINRrfvn+/7geZPdWDoRz5Q/7x+8cgQ8RRo\n",
              "hExZEVn17iy/T4LC/UtypQRbl6+ndvGzbFJ9mdBznwAAAGBBn7JFFTwn/wAOgztHdmEd4XqsEleF\n",
              "jVsDZBsrSzaoiBpuDJHput9CHz8oSR2a2GvHR6L7zW0ZpQio8OVdAGzbWkd2/4ghVv38DoO69XQK\n",
              "dLTP8icTqWh6bRcjt3HvGTEAAABDAZ/RdEI/ABFehwTXCLMj5rJDYjMxrR4wttOaxtsSDEsowbX0\n",
              "BWygsigVLby7kerxusZt4YwTkPKf5PYb6XMVrr1QIAAAAEUBn9NqQj8AEV1fcMmbdjW3Fc8O+55Q\n",
              "Ki6CWkhrzoZVcEyWg7kDu9kRua6b6x+xVvKHKFjiNI3frPxg2Nv2rsNgnP9bBfwAAABvQZvWSahB\n",
              "aJlMFPDP/p4QADY/BsHF+hLS4z8r5j4U46s6R1yLmxPyEDyhggs4dCqRm2DBdth6K9QAPQ9triN4\n",
              "9N6O+kDRJuM13AAQBkwp0U1Di8jpBi8O9Bo0w3dlRr7SDCPJr25LULqd8HYTHyxhAAAASwGf9WpC\n",
              "PwARV/Gusu2G4xrWUbbDv4GMse9USkxJts7/l2/NifFw2jEOcA8u8uhGxHTqsU66c0VqcEBy8HZO\n",
              "K0Fs6dzGctSmkjpF7AAAAJJBm/lJ4QpSZTAhn/6eEAA0q8cqUR+LlKwtyX0b0+FBDcBIjLhHzUpT\n",
              "22cm16Ye63bd3izjExcRlkYluxHR3rJKTuM9hY8bUdT8A4q3WqlG2DUB6UyRmh5Ap4XK2pyn3XVi\n",
              "WBxcsJFw8yhM1Eu/DAJLNqryBka4oY/8gGxfrQDyFRTdRJryuDTWsuwVxZpMJArFlQAAAFdBnhdF\n",
              "NEwn/wAOJreSAQxOo/9xXUUlAAPLK1Lcfrd0LQGqC4nZPeN6ragz3woYkHpOf640IhhI0C/mo5E+\n",
              "7hBhGz/XqGN0amBXZdpvMmHvfrijhM+/ky8AAABEAZ44akI/ABDWLwPikiEi263aYtQSEMLjaVV3\n",
              "/QfjA59HMvYfn6AH6YHUlm+ct8LC/hKdKKTevTZLSrsSrCNu8nqLLaAAAACuQZo9SahBaJlMCF//\n",
              "/oywADVe9nBf4ALAmvtrd6eR6ZEi7EfJoETh+vOWeDjYBNDTpAKF9DyhLLZFsYq+2gtGe5sRx6ns\n",
              "KgB3y98awNOcNQSW7rfeVoQgseQs7bPxMsqxScFSapsn5UKDyhjbZuz7OJK2TiNzd2tURoaLaF3W\n",
              "+8XBWxxi+dfEbfreo66JuByK8Q7QGRMKL8s6Vvx/7+miGzppePE0wjZZrBVtB/ZFAAAAd0GeW0UR\n",
              "LCf/AA4mt3WdRrok2tNSLgARuEsm0sEIrclCMF5bOcGgxRRMwak7grbrAmLNJ2GLaOdk1wfZQcfi\n",
              "5+5/GrJ0SzRhm+bg5lHcLu9KIONQHVzx2DNyb+xZBCn70LaxjYUkmns5z8z81ZLWCNDu3vYQqFK8\n",
              "AAAAQgGeenRCPwAQ3obo0eYntUxRgCixw998WANj9ouE3uYrZTtidsx9kA9MogaavWYIUPWTdrgC\n",
              "5NGYuMIRnXN1j5gekQAAAYwBnnxqQj8AENfxrrEGjuqlAC0Afxx3wjmsqnEhcH2/kEjXSePjaImk\n",
              "4yDx0ySpdIdgRaAw/F+AIF3xgK/tPAkEoojFepGqGhmEdOYhXdJH/fVfg3mBYrNcCBDlmLR3Hlrb\n",
              "w2eaVe4xKXxZKTYAtm7Zl8537MybzFAFpciR7uGxj5rBr63VZOkPsrdmplOGy0JlAzIHJd1avXvB\n",
              "uGGTNNoCLlC/nygMuThxAFYOQ85SBHyZP/UZCJ1rZQaXRjcychocpMoq7gF4FZm7oY8M8DLklNB6\n",
              "jRdYctnKs1GSuyalod0klexSXOlCzrmcQZcjHEOR9vfxDNZjKHihv5mbMdlNDly0JxzxPcqHIMx5\n",
              "j0c0nTVMXDJPU7PiezzNkMkV7hqRpO5ykQIh4jNQMmQ84ABuVjRS0If49tycQZSXemmxDuvDtAKH\n",
              "bM2oUIfOngf05iwAaIXZ3IYCZLibddsJo2aVECoeMzchTwpGxWmwolRAqHjM3IVV1U3LasDI/hUQ\n",
              "2zuQwMypwqU+3fVlQTcAAABiQZp/SahBbJlMFEwv//6MsAAzyv6w17v/jXkBeS+YTSR/gKAzyh0C\n",
              "VzLCH226Ni5pfrq5QhwCkN6NLKJv9jgEyhKyw8EzLBD6i5ajS73+lmfmjAfhr3lEfRQn0T36PGXI\n",
              "TKgAAAA9AZ6eakI/ABBWLwri8objejUU/ov/HV7aB9DRmQpR1F+fD2hI6jAL/sBOAnBgmGMzNIAa\n",
              "OyoAMNW7Bu4b8AAAAHhBmoJJ4QpSZTAhX/44QADInoEebfMx0G8AHeH4NQef7REYVcdqqOmlOycY\n",
              "YTECEqXzF4ssrqqvpBpPCTfDDRsNFok+fkeT6YcNsVPCliEJ2dc3lE3Ml/9eQkoX5UiStvbk6TlF\n",
              "LoSR+NXxyyTw2n77les3TtfQN6EAAABhQZ6gRTRMJ/8ADdNPvwfYj59/wAtV9PZ1tTLhwHitip3S\n",
              "M/mFDjyAXaTrOedxDV8okPh4/H9Nx1eVb3CJUVQbXaHY931VIHl69eOZ/RY9ZWM/X+dv4XeV8raN\n",
              "ZboXs5ngqAAAAEwBnsFqQj8AEFYvBJ3CxACxa6rN8EBIpNUYxdr19WWlcx8QfLwT8bO7FhpO8AeM\n",
              "bDDt7okzHtrjkD1K1yqRQ4Bz9qeiKfjtEZClEFlBAAAAaUGaw0moQWiZTAhX//44QADIoBIPQtrA\n",
              "TkLwI25tHUdeOR7KW68FleTFDCftZNCZKWdRa9MgcF+fG7ksope03q39yvG6UEs5EitlcPUMAylJ\n",
              "e277laqv+0mW6ZF79s0NhI4BE7STP+hFwAAAAJdBmuRJ4QpSZTAhX/44QADI2FC2fDJXe0z2OFUA\n",
              "Gw80h3UYw2AAaTC0JWVj6U9HBTOC6TsKOdnQVhmm2LElpRsl7rDoBGrBxa39F+BNFpE7ML9GcbZE\n",
              "btFWSa6lJnF4B1esSIQMr9AG0O/EcPEQpw0rJnMrYjuIOtGbdotcQxcwU8E3KFEV0EEwCQbGoeFd\n",
              "rqXH/pgDwELNAAAAQ0GbBUnhDomUwIV//jhAAMQFDq0AE9wypEkNakYxombidUOgAdqDVrCMXd9u\n",
              "Kz5/4XEe99Ay505G6fL6evfn8JKE5UEAAABYQZsmSeEPJlMCFf/+OEAAxAUOL38he25jFgviodiA\n",
              "8DSjoFQvP7kVt0FxE8L4tnLDOYhMqhKQizzIMYO+AYOOL8PzasNCOi0teJmxPzgiOErTJ+/dAVg3\n",
              "oQAAAGtBm0dJ4Q8mUwIX//6MsAAyi3JZABHW6++Rjan1QbvjbNQvmZoQ/WLARpMVvAemjParcDS4\n",
              "yjWmh9yLQP/Brtat6IwSE7LNhgqqvqYtgykIf+NTpRFpm53LbrUlJNe8KSYoQoe7EZwTaKmhNwAA\n",
              "AGJBm2hJ4Q8mUwIX//6MsAAyis99sAmu+A8q/nR/L2DeV1iyBuq8YVYpcgAvd3y2OTxAAhuvjlzN\n",
              "va/o8FlBxwi1s1FjjSrz0cL/C8QHPIo5X8oUaAd2xxyWAlzNJyWmPFgVBAAAAHNBm4lJ4Q8mUwIX\n",
              "//6MsAAyi3JZAC0bgNT6AA26zyXW6fpJdz5y8gNshx2dxzb+9dlcarYpH249MIQSFKOD4zFTJr4z\n",
              "wVJ5fUe8q/O2nq3tdhgpcfmilrNRUL49oPMV/GAk9+YBLb3E5+Byl7EHh/Nf/fzAAAAAkEGbqknh\n",
              "DyZTAhn//p4QADIsDfoAG04B4eH9K629MXzg/Z3wOdLh76P6dfhb/OzgfcrbxWK8SVs3rowW63Du\n",
              "nclThB9313wMuOA3SqHSlWbFg3jM675BVGrRs5thkyxPftdTDG4nz9kIFKvNkDWFoMZiOtj2lYwx\n",
              "epKrUkb9lO3e+w8YTSijfzV7wQaqj6QdMQAAAH9Bm8xJ4Q8mUwURPDP//p4QADJ65+sH3WBTrDvc\n",
              "FV+Drk+lqi5OoOXC42edLuBFRlGCDNCrlH5IEX+S5bE/EjlrWvJWPAvEdZHz+2ynqG/S9+XSD6os\n",
              "C3XxK4Mh7POeLBawM0D7gRHgUKn1GbLMa+RIB/IWZc9RLybOIUW/WwEfAAAAQwGf62pCPwAP3/H+\n",
              "nge64xrWUbbDv4GQbiIqeuQvgMjOV2VY+d3n2GlYkN24/y4ts0Ywrl9LODTUJIqDV4efIMjUSsAA\n",
              "AADLQZvwSeEPJlMCGf/+nhAAMTrn4GkaACL953Q5e0rhmdUJfMKkx8d6ysRDO4SmsUlCbwwNlmgh\n",
              "E5qq1f1E/OuGzOPT7tdeBBtGVGG6b30FgSebZKzBf9OLqhaju8pqtLptbTQdnwYDMmli252Vl/E5\n",
              "0NnAQiusxhSZ32ODLbWmOcZ0/SANDzi2yAZQtfuO9QtNBnBcEjh9JsDZHPVFQ1C3sGqphyGwGc/R\n",
              "Edx/ziJMK6+oqKY6GR32jbuvXQPwqyBABpVoCXrpemooZMEAAABrQZ4ORRE8J/8ADTM+MUAAz57u\n",
              "WGAjPE6kL71pw/40zwM49ASNOKfUDC0W8Z56pP8KuaCwR/NNUGCOXP02ksljeshVg/IewpO3cmz4\n",
              "g29VGdIMGT5ZrVJAVDZfNioc+HHEhmsatTLuIlr63EEAAABBAZ4tdEI/AA+McM8fSMUj5qrs+m3p\n",
              "A9E2vV5zaIcvGUZYg3zpKaWM4mBp0//dT+J4DU/zl16wt3kwDzBW8Hj7F3EAAABSAZ4vakI/AA+L\n",
              "mxMgm3Y1txXPDvueUCouht3TW/hBSb4U6wOv0olKYeX8U6es8AgflW6OS/Pift5CluJG2WI8S5Qc\n",
              "feEGhnRxJR3vzh5GQWsj5gAAAFdBmjJJqEFomUwU8M/+nhAAMOuwjVNoZ8eimXAUPKna4PvRa3SA\n",
              "9J9vzCfBpIHTs33xq0/Dec20CR4I8iW5ZViNnqSvXyBl6nIBeXsL8BrO0qhBVh95w+YAAABHAZ5R\n",
              "akI/AA+Geuxp95kOlr6vd8UGOVStWraVOW1TLELigFDj9PzYR0RfmDzIDFF4SskLvVKxsmK1lbo9\n",
              "GJbGM+y2pwO93XcAAABwQZpUSeEKUmUwUsM//p4QADE8JzrOJAlqx5kti1bLiC0lGxObGYnLaDp5\n",
              "c7Qw4yohIYAVClk1xce+oQw05bDBFspwYA8AnuSMKg9qHV/HZS/yJyOvBnnGYQLrtGdjLuVQqx/z\n",
              "79kMgvIwRQ9p3SAe0AAAAFwBnnNqQj8AD4fx/p4HuuMa1lG2w7+BkG9kS1DMNZiPpAhNGeillJFb\n",
              "XmVd76Yvilgda57KxiBZ4QxcLLMZ9bmjXUcTpuFCdaLPWsQaD8F5z0xk26uuxXGWliPjgAAAAJtB\n",
              "mndJ4Q6JlMCGf/6eEAAvq9DfEAM31rmPADCCiPdl2VO9SCmUVBY1Go77Dtp3Pr/4vUDMG4WShQwx\n",
              "nOvhVwU2BQeEvdWvtk+5AVmvZBeCXov24C4mnubLfiqXWJyYot5z656Mr7EV6bVHaUm/oyf7a0Gz\n",
              "CqigeUa0Mb5qI2fZdseOOCgzyyEPAC9phP8u2k42m4i+R3VzlGmhLwAAAFVBnpVFFTwn/wAM35Bt\n",
              "wIYnUf+3ppZT/jAI4HXuwz4Wo+Pw+ogyvIT/+aT4OUrfSPEKcT5nrtGCQZBm3nLlapKD8Ta7K9m5\n",
              "B+Jjd57UJQIx9EtO5pWwAAAARwGetmpCPwAPKACYDtC5i9ZLnv9v6KjFidpEgF+COrAXaFMhXO+s\n",
              "G62UomsxXsxZnjgwcZmoYZtjZOWcXmia94/3i6kt6jjhAAAAp0GaukmoQWiZTAhn//6eEAAv/Cc6\n",
              "0WkCWrq1jsTwV2a/cFD3mGPv9LYMgrnEFyG81L+ALVKIGXnkRULoDSJuHeSDV+zVz+cfoOnvJqam\n",
              "P+lCshQbm6tW6T7iqa2GJVpTHUkCPJDF5AAku4V3J98+CA18s+2zbtr6vWD720dl1sCyVM2YAJnz\n",
              "uURSITTl5vQU/uJJnayXjzUG98+xoEZMLzQEGrpbbYoZAAAAUUGe2EURLCf/AAzfkGaWzDZxoFWx\n",
              "JavAfXZ/x7IIrJ1Ek0aQAuWnJHzPj3Jk9m8KIYZpcggsR1E0/quxUF407AsPrTEb+SGHulHFbZfF\n",
              "x3TB1QAAADEBnvlqQj8ADy56qln6zRzxNH3sP85NsUEWNxOfNOzU+LHskGxes8AOEQFt6HJd1oHT\n",
              "AAAAb0Ga/EmoQWyZTBRMM//+nhAALooli+4AfsMeTxVYs5fU59UbWuxz23nExAkyI8yUjoQrfRWr\n",
              "W0d6BZ6JkUxLzZ7npjfSti7Om2Qto5RoGA+TnUVOMxdt1TH9GNS/oOL1R0w6SSF9B6gGGYB45S1k\n",
              "4AAAAD4BnxtqQj8ADtZ7A+KSISLbrdpi1JKGvDdmyu9PQC6Y8Rw0KCIcuIqZn3UOg6neE6hsBWhV\n",
              "tBCFox+VNvyGXQAAAKZBmx9J4QpSZTAhn/6eEAAudccqQblLWRM5D+mE++apy2tg/EXwFKD7P9sD\n",
              "bVixURxUVLKpWo8ZpXw390jowMuj9g23lg1UhmaJbBo6g/LqoD/5ab4oKpJeArhhvBs+b1xuBtWd\n",
              "KIk2NnivYjjgSEsE04os/l4Nn6fFr4PGQ26QbydezIM6+dlxYQ9NNS+E8d1+DdHnC2aJf3O93HUJ\n",
              "LSqOYc9bMoB1AAAATUGfPUU0TCf/AAyPkG7o9ET1gSvWUr8jjJ7Wtt7jPIuqLBcPQwMIvExOh3UH\n",
              "vnH1Hsh100RX4tElpV2lrB+hIC7/Pn/bFEL0PZP5aLaAAAAARAGfXmpCPwAO1nsD4pIhItut2mLU\n",
              "kumR86S44qZoFeu+PFOxZkurIxHZukz8PKjeHgyQN/Nx1LNUrGjEth9cNRNeEscsAAAA80GbQ0mo\n",
              "QWiZTAhn//6eEAAuvMueJ0zTkAH+oU4b6If+CTJvkXGSjiAjVZ5UdmrMUUJ9gIv8NnurDCgefItU\n",
              "uMaYGlY7RtfHoFFmkFsfrNDeyffCAqwF13HrkxOQiZ4DQi9+8d8GBfpuUn8wKHefDP/aNMMQbMYj\n",
              "YAJsQxRgQFXvb+QHqchzVW6jXy67axcV0eelUJRA/DFj26TU+5VPJJQqpWjwZhM/f6xBbIiYY+XV\n",
              "v+4F9HATDbIkqO7n3WQeRrG3uzkbRvc6wrKwzold6KPsjokdvgwHwS/UMH1Cx/Div6QBthDimwfB\n",
              "FuaG3OxEBfmLSQAAAIFBn2FFESwn/wAMj5fGpv0ACBpfn7AtxZ7vmU2UH7urqcv59H8dY33tjjH8\n",
              "fgYq9u21yn8xEqL3iQhMoOECIwwzDFXYKb2+RaMRvGvjSB9IwMgqnrcul1/fhZE0kKqRmp9as1gM\n",
              "9Ll4ZkTmLjoxk73z01T7JRNsE6rrvCzXthSQw44AAABEAZ+AdEI/AA7Vg3MUFzNkY38ApKBv81O0\n",
              "dPh3bPAGn14o+wujFxfOxkVJnBZLQehLRuLE3T3afs95gujEkqse1ayPj3sAAABNAZ+CakI/AA7X\n",
              "8gAEACBPsRaAphUrbOZbVbPZjhZlztm42nO6XLoXaQ7fgxRosQcDb8iP+bPqmT9X6MlqaW5sUAqi\n",
              "iM87PBWVUMueA44AAABlQZuGSahBbJlMCGf//p4QAC1Vxz8QA+2NWpQ3K/i1EZXqoZtTzVWvmflU\n",
              "pI3SOAnAMfp9meDGYXAqfkcNUQ5D6WW5flH1/CK66QSiJa01LhHy+KRrAD0ECWYOLknl2DvQ/mtO\n",
              "4XkAAABDQZ+kRRUsJ/8ADD+Qbujz8G617XurvlNWhm8sa4uK/jo1NkEQpdt/URov4WsWIptQoTaY\n",
              "OA2AF9HUxUk4rTVLtxvz4QAAAD8Bn8VqQj8ADn57A+KPjAsKlePyP2M0XyY2MBh6rtI/28C/X/vA\n",
              "jkcCjNGDQo1hpgcHRrTtQy7zPw+rY/wIZUEAAACvQZvISahBbJlMFEwz//6eEAAtVb2DVtIAUMd2\n",
              "GD4ITCT2VEfdJpwXqzwO+WmrUWBNBA5fjCB+K9UT/TT97Gky2hYKrs8mPh44j1u1N+MqSax8hwI0\n",
              "EkzhbISmLDWV/vY/xxHsSJeoiKZzZv5iuao30uiEyrrQn/D38EWYEB/MtppoM/HjPRiZ7VpeYhFd\n",
              "w78FuM203kKMykMFqqp2YMowPP/9WB02ykWQwaC8E2q+QQAAAFQBn+dqQj8ADoObEyCbdjVXQ+rM\n",
              "joYlzOd3LHApiMPoXddy4C8/6qz5qyyJoYxffqb0aVoI9wNch+gymgHSn6+iYsfw2mpgJO1RWzGm\n",
              "egEfm2X9njgAAABcQZvqSeEKUmUwUsM//p4QAC2cy51nEgS1WTkyQ1MY4MXhdkoeGcOfRTyKRwmo\n",
              "LAFHUbIkTPUI4AG82+69TNdAS2+nrL/unN8WtHrK6mjKRSUFBU0KWwcxPgfkol4AAAA/AZ4JakI/\n",
              "AA5/8f6eB7rjGtZRtsO/gYyx71RKTEm2zv+Xb8yjTylMb4xPzpABeXQBhcxGh+WyzLRV/wbQEQ/x\n",
              "AAAAnUGaDUnhDomUwIZ//p4QACw1xypBuUt+l1NIvxm2+EOpBEcLn6AWqfw+QKL9byPWxQ113aCC\n",
              "xop5eq30LpDMxwQmAYxeuQ2wGDilq86Er4vKLSmkgvjNxn9qYjK0+ZhsJStMZDEMzRsQBamQAbp5\n",
              "5VLemNL/JHXvk2lC3rfgK1TwJquyUQHspPjd0ASIkTiJjSwK9B/M3t0esHQdM+AAAABHQZ4rRRU8\n",
              "J/8AC++Qbuj0RPWBK9ZSvyOy2HZK0buaTT99gxf7mYM+6HdYcxwcbGnjAESJkXx4QSb2T59A+jSh\n",
              "NrsybapO9IAAAAA6AZ5MakI/AA4mewPikiEi263aYtQSEMLlPIund1dD9p8K8wR10Uw6V7CqTOj/\n",
              "uLrDNkZyQlPkuGWO6QAAAJVBmlFJqEFomUwIX//+jLAALNzLkCriFMAKEICbm5dU2L6AzF+94fFR\n",
              "x7veW8nuh6mUiy92Bs53ggsqaawxOzWL956eyylNrKMJR2PmYjj1u8mif6T4kq1ilxjkQjPBwiEr\n",
              "FzwPuvs+VQmD4SRWwKd/DfuHUMD1BejowIYloVAfp2uVKX/IljRThNF08uteHXhhSwAz4QAAADdB\n",
              "nm9FESwn/wAL75BamqNa+Io0gPecQhgwWaiC1zVzmdrXfG6n7A2pSASTZ4GCPoAdwpXYfAM/AAAA\n",
              "RgGejnRCPwAOLHC3OX4ntUxRgCixw998Wa+CwGT+QKF27JPZbpfsgD40+QPbarNifDzMnvX3zlbd\n",
              "izjzZQ1EG86NC9r8ekAAAAA+AZ6QakI/AA4n8f6eB7rjGtZRtsO/gYyx71RKTEm2zv+Xb8yjTylM\n",
              "d6LIsnEIgNwP626u9Pgovz91P5qgIVMAAACYQZqUSahBbJlMCF///oywACt1Gqy6yUTMZbtEHBZ7\n",
              "2jUKvQfn2uyHRrbXriecjpaBO3tMM3lUASTSfIsKWkoWNK2d+H54aQyZTE5wnDMm3KWyEIogajFY\n",
              "t3VBnZp0ENKb+z84luRlf+CVx54P8VwOt5B8kb8jv+ysuVmkwIUfE3ypOuQ5kpGf9QC6J6HNRzyy\n",
              "zXdREOtQHIEAAABQQZ6yRRUsJ/8AC6IkcyjptWubSh3lY5jRol+MxDUuDVfS4pgfiX9emhH4hUQa\n",
              "waKX2UoXo3x4KugsBQHB6yobl3ytBCCvnlKpqDoo8Uc7QcAAAAA/AZ7TakI/AA3P76NMWCEi263a\n",
              "YtQSEMLlrrwTHBlerIlpM+rclQQlMmaI/vbTjOj796o57mR8dF2pvzS4a8lYAAAAikGa1kmoQWyZ\n",
              "TBRML//+jLAAK3UOrDX7fiAB1bWscnqjC9ASE5EeQXcB1hW0zFVCumTMvgmk/ugY+AGI8Rma9w/0\n",
              "UxrIWzolkb2qaP/6yyD7zn5yi0k3lwFZSE4m4nP6M5bPbd3MRzuo40p1jsI8gZshgz0VwOR2um3M\n",
              "d7DK3/ZtrvMFkf2BccvPmQAAAEIBnvVqQj8ADdRPwSiQU2rOD7aHUsdORtjMsROWf+F72eHNImzj\n",
              "RT+umC4rqF2VEQN1jwBnqyB5Ok/HDlOGpkJQqYAAAABvQZr3SeEKUmUwIX/+jLAAK3Vwgt8+ACw7\n",
              "kqG7M1svDvVcsXC0rz/2xtLHRaBNpOlmiWodMLwJc3an59buolPnmS7Lmr1l4T7OmRnRE9ipCFve\n",
              "tazQfXItz3NDLaiSh4FRGkvxftYWlH2QhaoK0HjBAAAAZEGbGEnhDomUwIX//oywACu8y49AtHwA\n",
              "RkJXYqXaibr0ErjP3nJI/vgKAQfbzgs1sTPLOVy9jn6Ub8LVZmKMwjDXFbzURkOQw2dFMzesh0/N\n",
              "+iOtQPhWVIgIO7hV+m4AHWtSweMAAABYQZs5SeEPJlMCEf/94QACes8cuAA43lxvBzZHqm6HAIPP\n",
              "OJ2ZZxZv1HfmoFiaRci9Q8Hv5Qw4surbGCaJOoILfarOsvV8474xHoZxdygyBWye1dIzh+w2CwAA\n",
              "FFBliIIADf/+9vD+BTY7mNCXEc3onTMfvxW4ujQ3vc4AAAMAAAMAAAMAADSY0H4zd2rDhiAAAAMA\n",
              "JgAGUEDwO1+YT11btDVDXiBfpt8IsiXMbYiOydZqPhu+TIJJQdezCgF93loiP21TttBsPG0AyQCz\n",
              "6AZ7GwqGEg1/meqA4frj0+3l1l0Hlx30NbPB2qMn534iffDL6EVENtykQMiYGj/KV6GCNTABOA+N\n",
              "fWdVEUHFGGLpDHPCDpAU/19qkw4zkVPtVN1T+XTnOG4z4dQlgr/uC9qrON4Rq8Em1fABxa3Kh49A\n",
              "uTC0FDTS5BtJaF/M8jD+yuwiPdrpKkWR5v+d8b0B9dS/BG/+qn5A7875Np3VoYqqL3SNE84ZDRC8\n",
              "hrAT4oLKzrud/wO3LNqRG+AbW3CSIyM/f+loX1KqQR3UY7wvxAgAAIk3CSxp+DQLH/26qYCStD8O\n",
              "dTqNS8S+kVv9xKmbN84/yB7NWRgngctvfqZTJUmf+k6imBJHn7wfRUjop33NclZCv/6/amX0nfbG\n",
              "Qj+cwzu6VzZKBVF67az5NiuBAccaNG59+qBJQkRJMkUrsz4KDPNQkpoCAL7+6bOywLE3x4tXHUwg\n",
              "GHRN/79KE5LPwnYdLKYPiOSLvKRUtmXFufkcrN/1HOeWwjWrhoAn09IfcrlLatTajC4B4G4wA67G\n",
              "6Bz7yB077ngZSnE8Hksqqa+v8DWDI2CLVIW6QAc/js0KhUnNq9yecCjtvGdFOaeGc86T8osiFNK3\n",
              "efIqyrUNIaWtNR3JGIbtxUIuemZUT7MKAiug0N4Pn6t0/4tF6a6MwBPrAfTmz6Hs1qUpRRDP8/hw\n",
              "srG/sVT1sxo5x+Tqj4I2ypyPEVVX8whKMingVqv9+uaUkCi7T+20ABrhTXtkuBGkjVWOhOfw/MUH\n",
              "AIkxgo8aCteRLSAwy/fyuF7zg8jJUTAI24dG3ux2ErPxhftu1iS2zc0kiKfZqWIa4BWiOyfHIc+S\n",
              "TpRTQcFoCyYO0rgER9kv+jdzYDaJv6tMOzFO2R2qNeaPq0XaKNdXgJ4sEqjvN03kpH4aBLzY89eC\n",
              "4L5L9ozkGkqmSfWnWo+VmlYsWzLfXOiXp6j4JRV3NK66nH7UiwFw3Z2elSJhYzPNyg7C+5L7EE0M\n",
              "rfKj01n5WGRr6GchPXLOli5vzvg4vb8oAX4NmUFz305KYjAxIKsXmrZSpkxOg8oA9q9KDBq9iDku\n",
              "dQENKpq+4xnRKSN0Vg2FU5blM+h7RS6wRXA/MQDyT5LlOAXmhgswril3e49+vYrDcF/J4o+Mo0RO\n",
              "lu/8syP0SS/2z2Kyyx6oby7Ya4Vay8XfxUfUvl6EYoHW+fI2feVAYluJ9VOaknL8Ep2c8gApSSRD\n",
              "7PWk0xDaW6ADm6uMOYtZ6dPPtj1vmivoBCvuTGUB8rin0YSaumlVEIDpMwYPvwcse0Y0lQe5m2Wj\n",
              "HqwiyWyXk0y6cBalzoCRCnVvbX97mqSl4nT5ko5ids//A83h9OcymR8NQwcvS9+7TPdbXcEtCTQT\n",
              "q3vOVb+CiPVHgB6PUIIZNWrJ5jvO+oa3tZs/fZNuJiV7l80T6xm3kBe0js3kbVNspuJwj3dFjb+T\n",
              "2WbMyOGAOS0+5jewL078eEactKEDmwFclOOkLaUNjiulxs5ItMOVBAV6DPkaYJt5Ajv5ymShSSzg\n",
              "7S6p/9W5BzDKyn7/HoYF+3JUSY+MLvyVLJNiAx5Ql2QIigGZ70w8MWXzAVSQ52MaMvBVnCCCI8MS\n",
              "Jk9pdobBd8MP5UsXigxNSK7/TYaFEiHdJEsZNg4WGilHKCV2wLZM2nOafmOIsfYv620ycTBQi4cn\n",
              "QHgif2wJjTrjK0j3CBU6nGspki7wviQqoNu7IKwADNuXrF2qxJVJK0mdsJC0K/IVQqZqZ6yX4ClL\n",
              "3YNWaE9CtSDdDqffE420riJaNCmnMQ7nXwzv+2+wP67DP149NSmIOqCdqt9V4t80/EwjbIGoyoQz\n",
              "DeO7Lgb57xGdIHL5IHyzJIXa4HV/ZltYziSf6DjQVEcE3/rUJmwHhHZV+CPdfDQP7Ue3K/pOz+sb\n",
              "RNd1ezykY6VVJ4pbILvqagfWoEMnAH4OpD1ctjBtpUF4e+jOAAB47RdhXDL6XqPtWvNN1wPF8HvT\n",
              "igdgQ0qwOGoPVETlKxDBhk64gltyFfHEb5ZE6SjhgdCrejBJNrkL4wheyDarLP7lY/1aQw7NSmVi\n",
              "Y4BOrsofFY0AaKPb8gXWZFBmO+mAC1mqABe6bPzcVNbQlHOcsaCpJudaiesCsBW+DFWlGqR7V9zL\n",
              "n9PC/Etc1UsUPUVzdoYKQJ1wind2uspIRZxDfpHCt5GZEUmbntOIj+PTrVkbkffa098q3qLB6YAt\n",
              "cTKCjjux3vCijS8Fms9WfeVYMg2/oUnbNAx9/xLQH1Z4iNQdA5inxCV1owajRhcWWdr+bi3J2I2N\n",
              "G23vyxKqy/HDs+yJZgh6DkS8WBreTC9pAgc+PNHr0MfjmlwErbigwpIiZ005zinnr4tH3L8seBj3\n",
              "3MeEiboml5jfLnh2iW6uW3QZ51cQKw3RgI72aMIXSibiA2AJA/948UaKvcU5x/+YKjaaM8GDnjeh\n",
              "b0t9jB2zo8Yqy0+bTY9WTdNjvO1n/8Ttm+MtyOfvd4QakVK5ijAf4XTwAi0rRXb9Lb2K0dND/8Da\n",
              "z7Ugw+kW9psEoqkTQHLGHQznuQO+9FDTiXV2kvJSNWIAKmnPo+1mQK2PCpnGUmKesPV2BJaMG2PD\n",
              "JpdJu0EMVjVlt6nGl9fTN+kyWHJAJC3HLm0bxSer6Rs8Kqru/9qgav2KNjOTkoe0LqqOJ1oo1BuN\n",
              "gxgbrVln9wTexO+9m5STHL4hYE4Cvhyi9zdhhoV96ypvTX/6GFfpusZkL3JBhTsnv6UGU8t2blUW\n",
              "430lCotTsi0j2S2vLordmlcP1sDtR89KeOA0NaYUi33kfyQAC8EY9XgNqMDOFK4al/vuAuDuBdSN\n",
              "xShLjP9H+4iNglZm7xY6vrcSnyWbBoIQnubyNxFaQldQ/9QNvQPmP2Iyy2T690Xd2HXuyN9zjyoy\n",
              "BEgOTazWjMIxMG5dum9/Rd33/mvE0Fq+cOgoBhmFXjvKomPsgj0MOr6iwjEI4rf+eyDNC1x9XMw9\n",
              "iMA8yUaZPeZ/xl/o4QeQ6s53dwVfEgZGGEbwP9YJeQv3SNO30iQWDF/F+40axiD8mT15pZa0NEJM\n",
              "JbypqTWJNPOYjYYENdT0PBTFSudXdfCZJL61j2kSkFfdaFevCD0SH7c00Naa7ZMLYL7heRy78AAP\n",
              "OKootwgMz8q0eqsB09TiEHe/uEvan772Qp+TGVBo/sgSYtiXSbCvXIdDDpyixF3Cnws2BH2wy4J3\n",
              "9OHX/y9xmAbdovVSlXEOHa7N7U56BLwqRkC8eZoH38xL+RyKqJGHMEKxdOsQuNGfRutZqPxTHQpV\n",
              "AHHK70yXj6dMuFj+sEvlewpu/EbMP4HhNwHsaYrd9iyFnHmv/x50WoHSKNForqsK4JwjC4lZUFvk\n",
              "XZ7D/72w9l+nnxtNyy/yvDmH3yFFGgPf2d+FekjeB/hL5/Nd5RJaxr/Ffwf5n4Ti2cgUMxw4flRX\n",
              "mOJ5CDXnXN6czlVkL53eTQVkKrmR49w97mEh5JWUmRWB0QR4mpo0CiCE/AW5tMsjO9WwyQMRm0AA\n",
              "GpMd6iTDjvCIdCHeXPjSzgwp4Uw+5nPJLwRFyyvmZpQjeiEpSvDZGgBXhFGkDkJPs6uUOfc0udAv\n",
              "O0fIzIiyV4WsoqJe3ykHzP5R+c2qiDe8QdL5WKl0fufFWTFR4H2RCgP4IcAVo6NdtYb0ohuzzqYQ\n",
              "oz4G/18lUv0A8x3yviYZpuwUAdumK09eTYTRRGi+LiooRIPwF3bY/bSpD+nRN6I9D+zWGQ+/EWLu\n",
              "s3+Nzfx2ROSAvW3F15GDc2x8sdd7t6VmXGUJmMLLGxvga58rvl4GkyoQQp70fIwSoamJGaTkv7jT\n",
              "HEOpTFIndgiBbR9PA/vCtv34R+9DfJnqqJAvEYmRiXtGt8Gixr5+PBAkfACZyboovlEDWdFohZ/w\n",
              "i8gYxmnK2s0fiPll8QGpyi7K578mJZm8juxQxx2V93/eFd9/xfWVRmFAyhCQny5Y73Nsr5w457+c\n",
              "ibnljlJRogBgT40pYDkLMKW77racNTQtk+WzhzMLzEapXRA6ow1q/J7dozWRnKwbHPgkvJaN+LPO\n",
              "XYCqgfLqpptUaHF08sxocjJ+EEVKU4o6ZX/6o24AXfrkvh59DCh6wreGoe+1+4VKgmeAuT5vLIGH\n",
              "osCNmHpZUeWY7B/2OmziryCLPgZs4OWRVCzTpyy/fk7WR6JeudIbF1fhpywpQZAiLGUicHTRg14q\n",
              "4hTZI58ADyIi1wFROfcASMO2+kV+wA7QiQmPC+oasZJVH92QE8RrP6BFh4UB0BUqPWm248tmbnwA\n",
              "P9gGBz3TvanO1C81j8K9EWaCry1co+YSiV0Wd8LOuMHOrUcEw/GIFGn6B41DTdsrJWTv4DHVd6II\n",
              "TrmOEjIYl+9Ztk8QC0Jtn24FdyB3TRj7yT2pSk7n+tQAYUug6gndEqmgle0JiIw4mVsePj1oq23H\n",
              "OYYN6QSLry9PQEqnamglSc0IYkJ4sMq9myTMX5lLLDokJTRpQ2ESShxSUSHtO+G4iZt+BVPFghWr\n",
              "f2vKmBvyV9fg+Q36gkhUT9aVLnppzuDU/vHfQ3mfOEbdgAnYvf6H/3ohW3NMddIOPxdE8dt7LoOS\n",
              "Iqmi9ba6jr2jK0wVfLSKWNLl0Gs760Ebk1NvaWOfdKtg/n0rC4nzRZLwgNOEFj0W0L/ELIGwy93U\n",
              "/VP7Vr61hhw2Xpt+5++1AJl1giGcj/X868e60KJdeY8vvqHQh3gijVUnGtAecimhHbFMfd0SNGn/\n",
              "HTAiMzVa1jAEnxQL/MATMy0EenusJWWPJKXyIPLciRJEESJyL6PfRM1lIh53nv8fj5HI/YorqEZ+\n",
              "arL+hMkPA4HqwqSoyribhWKRLeAOL36yFcqiAvEJyU33yVxXBc8uN1j+QeP22lGBYFjJ+UOPwti9\n",
              "Lr24zA8Sy/D4DEJJTxTsVwA6N4BRoigJnxUk8RTsjOBtfbaLJRORIq4/1I/vKSK5ArL56VdZ5Nkx\n",
              "tPpef+2BMDa3Lax9NE9DA1FG49/5nog2JypxRGkGgIccMRu3QT4yP12tH6DB9Xo/atEbDu5vGvFQ\n",
              "UKtQ21o9y+KGwHxRCCrzIq57g+PAQMcI4fdDRgV6eyTUdykKw6wmZX0zO2uH79N9qORbtFNDW88s\n",
              "S/5q4V45+z7us8RAp6k6auX8nKL+a51bEf2UHerZfP8ay7QKl2HD0z9YiWbdxrlpMb6yK7BpoFBx\n",
              "ZfYRFpdaCOwQTgYTx+JxkGdCNwrWxrMWS+qYu1WzZNT7jgJGEzMi7sx4VLWO/GHeOto6t3/w++L9\n",
              "5VaIrbF1RDLvKE+A2AC97DEbRllIgkGmJHYFX/fZdhFD9ODDx/fY4lINUFwWZMalRgkyZtuPJBNm\n",
              "OJsQCWUyOEtTbd6C4x42MLjOd5ySk/h4z5SRVbdGB+dz8vvKp0yGy91WNjfrsJRFaZM7euY+hJpr\n",
              "pkZOEV5PiCrEa5Hm2fyD5mXrPqSsNr6TPmpwYzRODoG1hOc5NNmX55dBr7kOOrB8JGtA1vJN73Ic\n",
              "fuvDa2goZqecYtTDwfQr0P/2HAkEsoVQWqBZC5Z9YsSptSxQOeLZVkWB50t4lJ/XX1En+y9RQZwy\n",
              "cBT8Fa9SLfC/+34AUCTgafp0dvCstsvJLbrSZZF/b4Zxz/veJj9y4lAgvffNOvyDBK1geKjhlAfp\n",
              "eNlISOH5FrRFXPiL+RpqYaKqGjVkNLwX6ABfycpUADriL5ZMHjj4VmyaZ0UH/OqPycPWmuke0xkH\n",
              "yP7t9XyN1HeQ1+3eB7LQrILlKL+aV/iX1UOsd1Z/UtMb+FLiC6d71T/V75PlqvMcbyxCNswqsRCk\n",
              "iThBcyb1IMErNjDcxS8eR/QTCc3C+H2E/KlAEH+9ytRyYooLQBR76qZpd2N2pDE0bBPB95WBu0wN\n",
              "44NkhXLteQ9N8wUt61HwQWP9K7hnSGwfb+2GBchho/cHCCZ3uiUuwq7E/8yiN9xVEyv0FbNwaY7l\n",
              "uwVJQYSJZCUfOnXCVKSmFdy2vQ1FEmzEecVN99huJMpD08SHmIRrMGrJf5JcTPfBseJ5mdcLEMYR\n",
              "JytiKAIsniaeNZTpO21Fgbjf9JfnJQsC8ottGeSrbY2XDbDEK9qRBKlEPe60HbUINqp0otsgOc6N\n",
              "HgqOoOoLgdmMFKiflfcOgERdw38t3NOzZjlfU6Oq+js9eeuT23GXAH/TYMXnGaOmtNDNjBy/itUc\n",
              "r9lpNyYWaXWzh6FxNoiddv5sM6oT5juPVfT8C3ykNdchw7UDibURHTbAiRQg4T4tUPoESc4Xr9ON\n",
              "g3/bmbaomQ+174DkDqV7oaNG5pcCGLA0S2Cun4HKjz7vDJP+rtp7VXGsQiMiJrDySQQSaDF6XI2A\n",
              "tWA02ZLt1N4xw/OU5hpvdKJLJxqOrQhO+2HJPwRuXDYgSN8bwMG+VLgR5GTB3JFr3yB/B5c4ZdLx\n",
              "1KO+fCokiulcpWvqlRi+LJMgDto7us0VOh4cOuwhpE0Y0sAHVLQlaz4aa+O5t4vrTNhx5M7uNjRV\n",
              "e6rbz7w72uwyq6wXQF+HpgxjNpcdw2Xqu9Moh/16DZ14TI+jFDKSJ+Z3ygwVYocHwtJjuZaWAr91\n",
              "qHs8mQPY+khAuZ0OPCp9/izHA+gq+/DM7t6P0aPtP0QdbqqT9sFq4hpqf381+kMOlSQ5mY0wAV47\n",
              "0qiyi2ZFFd6eZ/2fCGfCa6S3znMuzDR9iGD21e6PbgI/tNXZjT3ik4iI0tEz9/6PtyyJ8lvdHDkD\n",
              "3pcXfINmyOTgKAzWk+iXZ5pzjbRUQbCVPIUXH++q0zaXMaxQarnnZm1wp6Y34daQkIz3xECjW6YO\n",
              "5s38dYtgU0AAAhC6oBbRAAABOkGaIWxC//6MsABGAL9lgZ3KSul4QEDqw+wQD/jztoeXewvkQZY0\n",
              "IcY1KiEsm4ehw9Uqe2sT9sVlBBWqEM8r9HIARI+tEEu0vd+2m0i7t4VunrWRHalEsIjyFy4bIm8g\n",
              "3FW8glRia12icF0lC9dmfpsz7yODxQwEjWKcnRNZ+Ayg/LmWq6yxLfD5xpnDara/b0DPi46gq1Nj\n",
              "R2WzdMnOQHUTFm3Zeg7F17LIE3mZ0WYOF35naI2K/65WpWgwS6tZPqlJn99pzhevn/zouYtImMPO\n",
              "Ew2R7LVa0qeWYm6sHiBQVZfcD+5wpw0TvuttmPR92MOfmlgkBtD4cgy9qjsMzec0FcyzZrRVQNGm\n",
              "4zuURrtCM68ltc4KwPUZHGoaKc/qedZ5+cjQsCZFlE59o1yUKyc9uD3y5W6OD35AAAAAgEGaQjwh\n",
              "kymEL//+jLAAKl5w6jwAdISuxexDOircokwbmV/2Uu8UhdEaA77359N3Sl1GtTGrlgQiojXq3BtD\n",
              "KVx02wScY9eDQoMphC7IpM9AZoNssVYNKdKHDkLFRbCNzEZhOdTChOxWXDARR24mKirbA51qHdLI\n",
              "zPujTXKzF79JAAAATUGaY0nhDyZTAhf//oywACpsQMSgrmIAL4JG/4iip3oQg0cRkiLOsioDsTnY\n",
              "hhrp5qJCUKqOhWSu5ZAHtqicxouzDxLk+I/ZogqKhAO5AAAAP0GahEnhDyZTAhn//p4QACoMjyBT\n",
              "ymYvI11330OB3UkQK5+GySsWdFHuVykvqwdAWKOmOhPauSjOfKMQr1IO4AAAAHdBmqZJ4Q8mUwUR\n",
              "PDP//p4QACoMj5I4K7Dz7DMAxvkSusHhFZXr6bu4MLci59iIQwhvWN2wn2HcLdjIb8hGW+Rwty+W\n",
              "cqN9IN1OAAZUKgVJCxUBve7xoZNC9OslbdIPhSAn6AAmhgFHztwOTvaxuG1I4zOx+pJJwAAAAD4B\n",
              "nsVqQj8ADYEy/p4HuuMa1lG+REUsLfrEt4YukRG0Ku7WI+txxbMC+NOxwU8OiFlBYM2qE++EZiEe\n",
              "8ZAf4QAAAHNBmspJ4Q8mUwIZ//6eEAAo1d2nxACrsaBRUlDeD3cR8esHAuW6xDerZIGgyNI/OOXm\n",
              "CQoqeXwA+pMPxtXYRYF8a7Zjuszu22uDi3viRuBEdjSgKLXCNCv3jsidrGAFhsgZolm7qIF3cPnB\n",
              "WKPCqDKgJ5iAAAAAS0Ge6EURPCf/AAsVSruARUeMVpUjhS3guft+9dHwQibWdv25KQQpJ/BOVjlE\n",
              "mQ/P+xGDXEJdw+qQPslbi75wvGoPVfmZBHG6/JqD4QAAAEIBnwd0Qj8ADTTjji60EdyYFLpRrIQl\n",
              "06a7bCP3aG3iV92KTblXJmKXq3cdt30l4qwmCvXEL3Qy1btLgvSNGlKMtoAAAABDAZ8JakI/AA0v\n",
              "77fBtCeprmccOwQM1kYdqYJYpGQjHXmK87ntkME6iCTE+kEl9mp91wTfFFc4w3CLLqgVX3v4LoCj\n",
              "4QAAAHpBmw5JqEFomUwIZ//+nhAAKR8C0CHEgktNVP7ffcFxFlEVeme8yfDoD8s2T9vMP4UaSZr1\n",
              "JCOFYgFnkMurhER87Ma8GT3Q7WJEiQVYBCXNmtn9JGYCaCyBZToVgHKhLM02rEx9R1I2ENwcQcOL\n",
              "xcrJWzNaCkGLbpJG4QAAADpBnyxFESwn/wALFUqwTx37V5KNfyK6rKMP/bMpZ/xH5EOlflFBES4f\n",
              "qXoGMLo1iFrR8uVC62+ZRhtxAAAARQGfS3RCPwANNON0aDEp3alvgBnO2+KEtpAdiswZFtF9TK5G\n",
              "gJoTolB26vIhXFKfsgBhi6Dh5vajhkF52AtCemXrmhB8QQAAAEIBn01qQj8ADTE7n7ykea21cHRU\n",
              "Z9ZRrleQ5rwCEo84EG9VKxQ01rjAFrQKWtKkcs6lVuxrQWKsLUaUEiPdGddAgYAAAABlQZtQSahB\n",
              "bJlMFEwz//6eEAAn2+C2MsESFvMxIIAObZYfwzUWZkluLiowwRttlNERKIViY2Gg+lBywwmPPEva\n",
              "TtA3zXz2zQ8QDLx3ULwUbwb/2SpdfdWxWd0c3hOm0Db+sgEjhFgAAAA6AZ9vakI/AAzf77fGlt+3\n",
              "koZtt/XvlDnj7mclg9C7YCLI71toFT90g8BGr8PDEWHxx5gjpbsDjwkDHwAAAJBBm3RJ4QpSZTAh\n",
              "n/6eEAAn/xeGgnfMtOb5FvvNhqsZwNKx5OxGACq+IikBfr7KVopvop+BhrAeSeD6jZ2azxxmKLOD\n",
              "/w4LW9hLeGtaEe+CIEVcZqyxnuWT2YDz2XeBnY8Zlw7M8i3uOVaFHZWoLbEvoVb2Y1gErI5nFFiM\n",
              "5ntvXNCbw4aPvm3RpLwsnn+5AwIAAABcQZ+SRTRMJ/8ACsokZpqp6Km/4SHEBqDQ3RvWIgJ3zcE/\n",
              "XtDugmQHdfqat0gd9YmD3vXKQsiXCu6qUQ5nrTh/pJnnWC+e+YsAqRTiksLPVzmfpVs0WsintNMR\n",
              "wtIAAABEAZ+xdEI/AAzk444dNMFbRludWVzdapiqScHblv2V//2wXuv02QyJT7EjyWLFk3DPi3Cw\n",
              "uhW+dNL7dHTf0w+A6Cm7Am8AAAAzAZ+zakI/AAzf71RatE7FBZv+ylpmcnKzdBza8hngQ8tu8Co6\n",
              "46dgQ6URW44hRIz6pCthAAAAaEGbtkmoQWiZTBTwz/6eEAAmsg2L7gBWac1fn2t/199hkEXvFQrD\n",
              "RTKP5RlzR+dvtzUTXFhVSBMfJgqAe4OalbEFk9t427gtCi6NHVOPYDUcJ3H2qnIYQwmpMZqB1PzY\n",
              "5jeyTbFAX+vBAAAARgGf1WpCPwAMlE/HV8SKiToP5VKZFJ3hknyNaVUPdHeRv3Ce2eRSLuuALrkp\n",
              "h6y39S39XWiYWCD24olHXzqqAFXUZoDQxYAAAACLQZvaSeEKUmUwIZ/+nhAAJqIMfzfsiNhWBnHo\n",
              "aC4AjkIrjZ0ztBdoxlJ47dZB3CWALvsWCbM99gZZX8OLj4eMkWJPEiIudyWPyN757e2JmBhYOtxD\n",
              "yKtOY8mrImaxPVLi9/L3NdmREqPN4LRbPtS7irLKF7UlT0RdUtrl/KMkEPlwwJhTfyudN31JwAAA\n",
              "AElBn/hFNEwn/wAKgiRzg/Nk7FHZqDMEPd2cbDsueF5pjCVfQPz+6iXZLjIIKwdYwE0VCg3YPWNW\n",
              "xqM19t7eubFS+KWPvX4P1cR9AAAAPwGeF3RCPwAMlOOOLBQDmJvXvuDvPaNGkNg873o+a3cqvV7n\n",
              "cdJRcg/jvILy8mvTzK8faVbnNdEwhv5roYkDZgAAAD8BnhlqQj8ADI/vt8/F+K6OZfcQrtvN1PrX\n",
              "qvOuuV7dSNT4OvoulVNa2p3YzVDYHLZ2b9t0ecGQk4Ol7+8oOOAAAADgQZoeSahBaJlMCGf//p4Q\n",
              "ACbfJ4C6UALqE/8IlW81rn/BADPye+fcNCRmK69TPDVDrSy1qBUAT76c5dd8n+Tee4joPpflbgrJ\n",
              "OsBy/vPY/LYgsU5m/C0ib0gmItB8Crlq3rKAUr9C+S0CpG+hIZWnT7Ze2qXuwjrIH7kLKc4DfvPu\n",
              "9TKTQowxOzrGaKB7d+jFWFKZMBMdaqFeQwmB6KrMcNmSC2Qk7kBRqoFQNCjvBcjBXzvC2lFM16e8\n",
              "LCQYKFwSbxHTPiaBZrV9OwXasPIQNPz35oRp0x6o+e0jKeNmMnMAAABpQZ48RREsJ/8ACoMpHjW8\n",
              "APyCNfFAzZP1mHlHz8YZtx8mvM3kIMRgZ/U73Qprz22OV+rXzlumEcozf+u3pwA6lLTLD4IWZxiT\n",
              "UvUeuw7ZXTNG75HyJZS1hNn/HGGNlfL1gHpolb6ngKSAAAAAMgGeW3RCPwAMlONYJ0D8z/Az8hap\n",
              "bm3dEFZt2RVZDBkYrNdTq8tiaws8hVYOiEINjwJXAAAASwGeXWpCPwAMRE+/h1PgBa3kx5annXS0\n",
              "uuShGeiJw93/GePG5myso+E2ErUtRhK+qn7/tTnhVnWOicp1W5U33gSX/FeL6pkt0mC7gQAAAI1B\n",
              "mkBJqEFsmUwUTDP//p4QACWh6yIAAtrrr+6W6JJEu38xcjMzz3a8hnPV7I5CYZQf96gc12IFw3IA\n",
              "l0qeYEBST2o69i8gI8YSyLJ9gKCK5oBDLzMPhNeXtHR7b2GPPV5AGB+6ZKam5jffI4NlGgLLPRkA\n",
              "4kilfCu1MjIxVYAHWFWBQFvNc6g24ztUwb0AAABcAZ5/akI/AAw6JJkLNB7qbt/0KOFEL3T8z4tW\n",
              "+EJJkiItQE4OQyv6+cBwvn0uu0yRZZ3hLa/9QdNKaErwh2X9xae/S2Eg99u8ryrnyzgGojDXgL9b\n",
              "F30ql6e0FxEAAAD6QZpkSeEKUmUwIZ/+nhAAJd+1NAZmHWj2HAN4eOk8AxAp5n+GnkG4IuWYr5qJ\n",
              "I4l+ge2BbA5hD+Mc4Kos7b0jtVwQCSaLtuw6WQthmMyZm0NdPJ7aZzFwkSX788eSGreTozBBc+bj\n",
              "GxSsqhl0Mn9SlOTMhaXUBm44p3bamGsuqVzwZ1Fw6bQ1wTlXfqyVZDHzKXqE0Jh0rh4K32KRkZ47\n",
              "JRQ9zgh2wXGHjX7Q4l6dH4sZVEAdZBTV0/ljlQ+bvfWco4VlWzpymQ87PAQwfuS03w/O8sYL9xW3\n",
              "YxYj1TQjaSwMyjXo/lBQUdbADaYhmDw1l1kKUJ+lmieBbQAAAEVBnoJFNEwn/wAKPqe+GyDIKfVU\n",
              "2+aoaxlTcLBN4H6cfgvQnaLBtK6Oj+CCtk/Q/+Kc9NYoegOHNXnEu2G444tCcJIEGpEAAABcAZ6h\n",
              "dEI/AAw/BXMojYM0LxOaqfzfv1JXGQhoIdv+gxMIwqfL1KojUjMAowOp5o3UHlUpC/qfgmCLi0ae\n",
              "dVN/fWK8Y7JnAHgsbAcyEYNrD7lERHFIynApmBiwEHEAAABoAZ6jakI/AAxBO2WBSb15SbubXvhA\n",
              "C0/3ilbGE0b823LgmfOZhU1loXIedZZjKbbABWVgxfi5ZfW2sGXFpn0Ck/lEnnUHmSnsTXzcXOb7\n",
              "9v116R/XDSeYvaTs7XyxUQDcpqnKdED7DugAAACrQZqoSahBaJlMCGf//p4QACSofdHxACrsaBRU\n",
              "lDvUHgNGiw7Dl5ekMeO/ERD9cUPp2VERKqpKnMTijv1zVvzMnvWfdo+p/pvyaF0PBg2TmLj3phC3\n",
              "tVnBLvVZcFiV6lEKAwKrPHOG48qNmq0vOiKEopFLdVpRBar8Vz2meor2KHhXXIj7NTljRJjf9cUF\n",
              "v5V/X+UaDV4V1gJhSKnexVVg+ARMThM26G5HEZigAAAAYkGexkURLCf/AAnyJHOsXl8XynMhJzQn\n",
              "tBWJkNo1LhHdM6NLBzC8asypUdbrTTtVPtxS36D41yHPRX2Th8hxaj8HjhgBZi07q8yI/2i1dP04\n",
              "q9e6487M/A9AwrAiAh6kIwb1AAAASwGe5XRCPwAL7wWOoXzkdw1bm/xA1rjc4d+FXiMBhcM2wmSv\n",
              "Mbkey4qKH5xoc6tLhQySM6ijCb28cuVs1cPix4h8hxaAIuilq+D8gAAAAEQBnudqQj8AC+/vuJHI\n",
              "BjdifK4dAQYg3aYUwjP1P0GPOQredeoYH7UPuECiEE+CLu4mfhcSYAhFRTRCEM6XHmkQwI6YEQAA\n",
              "AMNBmuxJqEFsmUwIZ//+nhAAJLmkEC2AAcbfnamFRW3b6BO7a8nyZEezuR+JlwrKCR6ZtZHZXbcC\n",
              "LKp7wxH7yw75QysxsIRCFTX9lQcqc0Y0ZJQ1/yfNxxQkygAbToSpPcpzIhjgt0DDKBqYNLY4yMXc\n",
              "zgWI1YfxHcZs3abbxdl1MjVZGyo046O4IxXq3CKgJxZwDuO999MZ3Jl705W3xcrovXquajan73vI\n",
              "6taVa+58nbP8L+Tov0dTWrxaogt1ImcAcKAAAABiQZ8KRRUsJ/8ACfMpHi3qlFFeAFq86NK9t03K\n",
              "V1NFvx6iKhP0GeaBhOFf/84kuhpnY+agYYGEMI0Mb35BaphpvqljtGaSnWOWh2nENu95nDho97XI\n",
              "ojH/ecetn0J75JZAR8EAAAAyAZ8pdEI/AAvvBRkA2FAl0rdfmrXKlaJ10DffeBavW/HzsK7pYisS\n",
              "PE6hQGvc4+ye0QcAAABBAZ8rakI/AAulr8EokFNqzg+2h1LIugOXWhD7N2u4nIosA2VbeHQpjQ6t\n",
              "UjLlDeujMmMs5yIniVUNoADVljIAg4EAAADcQZswSahBbJlMCF///oywACQKd1wALqFvKEXfMg+Y\n",
              "Qy5sVlsFy5d3lAqddLvo6QZZL43cOtmVY1fsXVV5aKvp30b5k/PG17pGrhX5gpZxxnF4pPx1mqJK\n",
              "jlDenQZ+0FDqNCCQQdXCUM8ShweCq84I+vj8NoiAvv/aSJQKvMt+X0dnG/9Y2izNQeTQkwgNzF2v\n",
              "qM+h3nYIdZHFjXBp6lQptolH85jkSHplwW0bQufa01+odzAt5qLBWFveKXsOCMSABLsu1zfvnDev\n",
              "EnqB4+pa43tS/K0n7dtOGu8ScQAAAFRBn05FFSwn/wAJrmq7sssOeo+1Brf4qbYmuCwbN2rNZgIQ\n",
              "x6lXgQKPJIIs8H9sySCZpYtf6pUZGSbd0Td7wS/4qSOb9m3WgawIsO39EnnC32krg3oAAABLAZ9t\n",
              "dEI/AAugZY9OtAyzhS+kT7MWwQa31Mgrj3BYk+qVl+AYpWujZplhptkfzoiTQwDTRoLL7ZqYoEzN\n",
              "tzROvy2IFC3Fqif44AH+AAAAPQGfb2pCPwALpa/BKJBTas4PtodSx05G2MyxE5Z/4XvZ4O8zX1jm\n",
              "4rpcZ2foKkOxK4iUPJp/v1Hy9kt4K2EAAADeQZtzSahBbJlMCFf//jhAAIt8X1Fcvg+xfADi4seL\n",
              "9QaubvHQiaWczFeWOA4SDJkBm154svu1NY8YoQ+hBoHWRaGqvuzpE6awIKKwc/QYqUk8FCYk90ko\n",
              "kIptPgN+cffxvXQoEe5MISqm08OAKJDH/r7SPrasjSKjJ9tKVWSepX3hblq8eN1X0oNIrKXqBKGK\n",
              "PBvaDeB4EMO1aVw7sS3/y3BWmQ+p911mnRT4kGVxkPFI4m2oDZ3xiqKlMu7t6oITYzvRLdLJyZI5\n",
              "7PC36eABzS8UgKjOLSimepuNaN39AAAAYEGfkUUVLCf/AAmue69hS4MoOC162+YaNSdirYBoruGp\n",
              "msgbocbHBO0pESqm2uQgGro/c9JzIsUrMp7MoiLOLnP9eqt74K504M58G7wVHxzjfrVcyXXWXArD\n",
              "7xE/bXkbUAAAAEkBn7JqQj8AC6Kbn6ykU1Yqry6fmHLyVrb5eNsLgSGxDzIptRvAjeC/RiSuHswu\n",
              "3j/KdyV8tli1d+Oj4OR+nGAaGG880VuCgC7hAAAAVkGbtEmoQWyZTAhX//44QACGyXcViAK0lPoL\n",
              "hhMUO5nv7CwCep214w9ciZwLX/HSpU3D+gXiO8+olGo1ZU3j8wGO4Spmmu3r6hF5YZd/KQk/eSKV\n",
              "NDrhAAAAXEGb1UnhClJlMCFf/jhAAIZ7WwrAER3tB1hhMUO5nv7Ct++p213A/HLuqG0Ww1GBWayV\n",
              "NfuE1K/8O43QOw7BKMtwIwEs2lXE13ubYHXSimZDynHraPJ1VjbAH9nxAAAAnUGb9knhDomUwIV/\n",
              "/jhAAIZ43KoQBEd7QMThnYMrE0aZ+9Pu2EhZ3DInDL+2UFhWktXVplF2YVSl11i2sbCQBGcu8n6o\n",
              "dVEkvCU8Uu0jmggsPdjYO4l85phiVnZG6dyz/4Z7iB+R1RdskJo4LjWkPrLdjejynJAhuNej2JfQ\n",
              "13ZEhDsjvfEXIVztyfEI711Vo1xm+wlL+6AaTJf9qPgAAABaQZoXSeEPJlMCF//+jLAAIxSwxHmd\n",
              "kS5joAOGY6eipCpgdEzmKqROmqTeL9UfJMttPa/uKzpFIFaZlELvjt9fr60Kf1fv3zpaXqqEKRxH\n",
              "FE7UOVnywP9LYBBAAAAAXkGaOEnhDyZTAhf//oywACMUsKbQsSXP1QADlnTIQSPIe2kpeyyixtSo\n",
              "ilFTga38CMCDLN9shF4QnAfHYuD59FRzFcIZE6A9dEp/eWqZhQJtAY3IYd23s+dntfh/a1MAAACB\n",
              "QZpaSeEPJlMFETwr//44QACHdcAAaAPRzcBa/Eq4bcHnG2jY06p+3CevLfW5X4w8d1CHJ6w782PO\n",
              "1eM5wFE74iYUQQyb0JEJ7m09AH49ZC4lFMLZUszTa/Z8RDOLkLKYTMBD6oBYYLpOSUU6fdcHvt8n\n",
              "ap93/TnMJNQEnfJcS5ZQAAAAQwGeeWpCPwALWpL+nge64xrWUbbDv4GR4f9UPkxdmGoxzw3yXT+J\n",
              "27Hv1R0cVs3LI5cCGFyfe9HV5ZOGUjlBmPYCAkYAAABbQZp8SeEPJlMFPCP//eEAAfqfWgmQ8F0T\n",
              "0FDVaVUYEQzaqbNR9YXln5zZ8qajIHQRe+6fkpoSVj5ceCAlVZcAHFIEuGQ8tSOEciQS0PNBYu9Q\n",
              "A01HDzdDAr+/QQAAADgBnptqQj8ACxFPo0xYISLbrdpi1BIQwtfBNSsmk+BuHPprE3BAftewjUGa\n",
              "k8/wUAjPWiJyONMCbgAAEA9tb292AAAAbG12aGQAAAAAAAAAAAAAAAAAAAPoAAAeXwABAAABAAAA\n",
              "AAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAA\n",
              "AAAAAAAAAAAAAAAAAAACAAAPOXRyYWsAAABcdGtoZAAAAAMAAAAAAAAAAAAAAAEAAAAAAAAeXwAA\n",
              "AAAAAAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAEAAAAADYAAAAfgA\n",
              "AAAAACRlZHRzAAAAHGVsc3QAAAAAAAAAAQAAHl8AAAIAAAEAAAAADrFtZGlhAAAAIG1kaGQAAAAA\n",
              "AAAAAAAAAAAAACgAAAE3AFXEAAAAAAAtaGRscgAAAAAAAAAAdmlkZQAAAAAAAAAAAAAAAFZpZGVv\n",
              "SGFuZGxlcgAAAA5cbWluZgAAABR2bWhkAAAAAQAAAAAAAAAAAAAAJGRpbmYAAAAcZHJlZgAAAAAA\n",
              "AAABAAAADHVybCAAAAABAAAOHHN0YmwAAAC0c3RzZAAAAAAAAAABAAAApGF2YzEAAAAAAAAAAQAA\n",
              "AAAAAAAAAAAAAAAAAAADYAH4AEgAAABIAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
              "AAAAAAAAAAAY//8AAAAyYXZjQwFkAB//4QAZZ2QAH6zZQNgQflhAAAADAEAAABQDxgxlgAEABmjr\n",
              "48siwAAAABx1dWlka2hA8l8kT8W6OaUbzwMj8wAAAAAAAAAYc3R0cwAAAAAAAAABAAABNwAAAQAA\n",
              "AAAYc3RzcwAAAAAAAAACAAAAAQAAAPsAAAgQY3R0cwAAAAAAAAEAAAAAAQAAAgAAAAABAAADAAAA\n",
              "AAEAAAEAAAAABgAAAgAAAAABAAADAAAAAAEAAAEAAAAAAgAAAgAAAAABAAADAAAAAAEAAAEAAAAA\n",
              "AQAAAgAAAAABAAADAAAAAAEAAAEAAAAAAQAAAgAAAAABAAADAAAAAAEAAAEAAAAAAQAAAgAAAAAB\n",
              "AAADAAAAAAEAAAEAAAAAAQAAAwAAAAABAAABAAAAAAEAAAIAAAAAAQAAAwAAAAABAAABAAAAAAEA\n",
              "AAQAAAAAAgAAAQAAAAABAAADAAAAAAEAAAEAAAAAAQAABQAAAAABAAACAAAAAAEAAAAAAAAAAQAA\n",
              "AQAAAAABAAAFAAAAAAEAAAIAAAAAAQAAAAAAAAABAAABAAAAAAEAAAUAAAAAAQAAAgAAAAABAAAA\n",
              "AAAAAAEAAAEAAAAACQAAAgAAAAABAAADAAAAAAEAAAEAAAAAAQAABQAAAAABAAACAAAAAAEAAAAA\n",
              "AAAAAQAAAQAAAAABAAADAAAAAAEAAAEAAAAAAQAAAwAAAAABAAABAAAAAAEAAAQAAAAAAgAAAQAA\n",
              "AAABAAAEAAAAAAIAAAEAAAAAAQAAAwAAAAABAAABAAAAAAEAAAUAAAAAAQAAAgAAAAABAAAAAAAA\n",
              "AAEAAAEAAAAAAQAAAwAAAAABAAABAAAAAAEAAAQAAAAAAgAAAQAAAAABAAAFAAAAAAEAAAIAAAAA\n",
              "AQAAAAAAAAABAAABAAAAAAEAAAMAAAAAAQAAAQAAAAABAAAEAAAAAAIAAAEAAAAAAQAABQAAAAAB\n",
              "AAACAAAAAAEAAAAAAAAAAQAAAQAAAAABAAAFAAAAAAEAAAIAAAAAAQAAAAAAAAABAAABAAAAAAEA\n",
              "AAQAAAAAAgAAAQAAAAAEAAACAAAAAAEAAAMAAAAAAQAAAQAAAAABAAADAAAAAAEAAAEAAAAAAQAA\n",
              "BAAAAAACAAABAAAAAAEAAAMAAAAAAQAAAQAAAAABAAADAAAAAAEAAAEAAAAAAQAABAAAAAACAAAB\n",
              "AAAAAAEAAAUAAAAAAQAAAgAAAAABAAAAAAAAAAEAAAEAAAAAAQAABAAAAAACAAABAAAAAAEAAAMA\n",
              "AAAAAQAAAQAAAAABAAADAAAAAAEAAAEAAAAAAQAABAAAAAACAAABAAAAAAEAAAUAAAAAAQAAAgAA\n",
              "AAABAAAAAAAAAAEAAAEAAAAAAQAABQAAAAABAAACAAAAAAEAAAAAAAAAAQAAAQAAAAABAAADAAAA\n",
              "AAEAAAEAAAAAAQAAAgAAAAABAAADAAAAAAEAAAEAAAAAAgAAAgAAAAABAAADAAAAAAEAAAEAAAAA\n",
              "AQAAAwAAAAABAAABAAAAAAEAAAUAAAAAAQAAAgAAAAABAAAAAAAAAAEAAAEAAAAAAQAABQAAAAAB\n",
              "AAACAAAAAAEAAAAAAAAAAQAAAQAAAAABAAAFAAAAAAEAAAIAAAAAAQAAAAAAAAABAAABAAAAAAEA\n",
              "AAMAAAAAAQAAAQAAAAABAAADAAAAAAEAAAEAAAAAAQAABQAAAAABAAACAAAAAAEAAAAAAAAAAQAA\n",
              "AQAAAAABAAADAAAAAAEAAAEAAAAAAQAAAwAAAAABAAABAAAAAAEAAAUAAAAAAQAAAgAAAAABAAAA\n",
              "AAAAAAEAAAEAAAAAAQAAAwAAAAABAAABAAAAAAEAAAQAAAAAAgAAAQAAAAABAAAFAAAAAAEAAAIA\n",
              "AAAAAQAAAAAAAAABAAABAAAAAAEAAAMAAAAAAQAAAQAAAAABAAAEAAAAAAIAAAEAAAAACAAAAgAA\n",
              "AAABAAADAAAAAAEAAAEAAAAAAQAABQAAAAABAAACAAAAAAEAAAAAAAAAAQAAAQAAAAABAAADAAAA\n",
              "AAEAAAEAAAAAAQAAAwAAAAABAAABAAAAAAEAAAQAAAAAAgAAAQAAAAABAAAEAAAAAAIAAAEAAAAA\n",
              "AQAAAwAAAAABAAABAAAAAAEAAAQAAAAAAgAAAQAAAAABAAAFAAAAAAEAAAIAAAAAAQAAAAAAAAAB\n",
              "AAABAAAAAAEAAAQAAAAAAgAAAQAAAAABAAADAAAAAAEAAAEAAAAAAQAAAwAAAAABAAABAAAAAAEA\n",
              "AAQAAAAAAgAAAQAAAAABAAAFAAAAAAEAAAIAAAAAAQAAAAAAAAABAAABAAAAAAEAAAQAAAAAAgAA\n",
              "AQAAAAABAAADAAAAAAEAAAEAAAAACAAAAgAAAAABAAADAAAAAAEAAAEAAAAAAQAABQAAAAABAAAC\n",
              "AAAAAAEAAAAAAAAAAQAAAQAAAAABAAAFAAAAAAEAAAIAAAAAAQAAAAAAAAABAAABAAAAAAEAAAMA\n",
              "AAAAAQAAAQAAAAABAAAFAAAAAAEAAAIAAAAAAQAAAAAAAAABAAABAAAAAAEAAAMAAAAAAQAAAQAA\n",
              "AAABAAAFAAAAAAEAAAIAAAAAAQAAAAAAAAABAAABAAAAAAEAAAUAAAAAAQAAAgAAAAABAAAAAAAA\n",
              "AAEAAAEAAAAAAQAAAwAAAAABAAABAAAAAAEAAAUAAAAAAQAAAgAAAAABAAAAAAAAAAEAAAEAAAAA\n",
              "AQAABQAAAAABAAACAAAAAAEAAAAAAAAAAQAAAQAAAAABAAAFAAAAAAEAAAIAAAAAAQAAAAAAAAAB\n",
              "AAABAAAAAAEAAAUAAAAAAQAAAgAAAAABAAAAAAAAAAEAAAEAAAAAAQAABAAAAAACAAABAAAAAAUA\n",
              "AAIAAAAAAQAAAwAAAAABAAABAAAAAAEAAAMAAAAAAQAAAQAAAAAcc3RzYwAAAAAAAAABAAAAAQAA\n",
              "ATcAAAABAAAE8HN0c3oAAAAAAAAAAAAAATcAABC0AAADLwAAAVIAAALZAAABfgAAASIAAAE2AAAA\n",
              "5QAAAIwAAAGtAAAAjAAAANwAAAD9AAAAvQAAAFIAAAC+AAAAnQAAAGkAAACzAAAAsQAAAFgAAACg\n",
              "AAAAqAAAAFcAAADvAAAATgAAAJgAAADAAAAAUQAAAPEAAABlAAAAVAAAAKwAAABYAAAAvAAAAF8A\n",
              "AABLAAAASQAAAH8AAABKAAAAUgAAAE8AAACUAAAAYQAAAF8AAABOAAAAVAAAAHoAAABaAAAAWAAA\n",
              "AIIAAABdAAAAewAAAF0AAACNAAAAqAAAAFMAAABtAAAAWgAAAEUAAABFAAAAjAAAAD4AAABsAAAA\n",
              "SQAAAKAAAABRAAAARgAAANEAAABbAAAAUwAAAGkAAABGAAAAmgAAAFoAAABSAAAAVAAAAGcAAABE\n",
              "AAAAfgAAAFYAAABFAAAAtAAAAH8AAABVAAAAUQAAAFIAAABEAAAAnAAAAFUAAABWAAAA9wAAAI4A\n",
              "AABDAAAAVQAAAOYAAAB7AAAAVQAAAGcAAACoAAAAYAAAAEcAAAB0AAAAeQAAAG0AAABVAAAAcAAA\n",
              "AFoAAACHAAAASgAAAGEAAABNAAAARAAAAIcAAAA1AAAAfAAAAEQAAAB1AAAATwAAAEAAAADBAAAA\n",
              "VgAAAE4AAABCAAAAjQAAAFAAAABBAAAAmAAAAFAAAACPAAAAWAAAAIkAAABUAAAARQAAAIEAAAA8\n",
              "AAAARwAAAEYAAAC1AAAAZAAAAEYAAABOAAAASwAAAEkAAABzAAAASAAAAEIAAABaAAAAegAAAH8A\n",
              "AABMAAAAowAAAFIAAACyAAAAUAAAAFsAAABIAAAAxQAAAFwAAABPAAAAXgAAAJMAAABSAAAAQQAA\n",
              "AD4AAACSAAAANAAAAH4AAABMAAAAhAAAAE4AAABDAAAAQQAAAIMAAABqAAAAVAAAAD4AAACSAAAA\n",
              "ZAAAAEcAAABJAAAAcwAAAE8AAACWAAAAWwAAAEgAAACyAAAAewAAAEYAAAGQAAAAZgAAAEEAAAB8\n",
              "AAAAZQAAAFAAAABtAAAAmwAAAEcAAABcAAAAbwAAAGYAAAB3AAAAlAAAAIMAAABHAAAAzwAAAG8A\n",
              "AABFAAAAVgAAAFsAAABLAAAAdAAAAGAAAACfAAAAWQAAAEsAAACrAAAAVQAAADUAAABzAAAAQgAA\n",
              "AKoAAABRAAAASAAAAPcAAACFAAAASAAAAFEAAABpAAAARwAAAEMAAACzAAAAWAAAAGAAAABDAAAA\n",
              "oQAAAEsAAAA+AAAAmQAAADsAAABKAAAAQgAAAJwAAABUAAAAQwAAAI4AAABGAAAAcwAAAGgAAABc\n",
              "AAAUVAAAAT4AAACEAAAAUQAAAEMAAAB7AAAAQgAAAHcAAABPAAAARgAAAEcAAAB+AAAAPgAAAEkA\n",
              "AABGAAAAaQAAAD4AAACUAAAAYAAAAEgAAAA3AAAAbAAAAEoAAACPAAAATQAAAEMAAABDAAAA5AAA\n",
              "AG0AAAA2AAAATwAAAJEAAABgAAAA/gAAAEkAAABgAAAAbAAAAK8AAABmAAAATwAAAEgAAADHAAAA\n",
              "ZgAAADYAAABFAAAA4AAAAFgAAABPAAAAQQAAAOIAAABkAAAATQAAAFoAAABgAAAAoQAAAF4AAABi\n",
              "AAAAhQAAAEcAAABfAAAAPAAAABRzdGNvAAAAAAAAAAEAAAAsAAAAYnVkdGEAAABabWV0YQAAAAAA\n",
              "AAAhaGRscgAAAAAAAAAAbWRpcmFwcGwAAAAAAAAAAAAAAAAtaWxzdAAAACWpdG9vAAAAHWRhdGEA\n",
              "AAABAAAAAExhdmY1Ny44My4xMDA=\n",
              "\">\n",
              "  Your browser does not support the video tag.\n",
              "</video>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualize learned weights"
      ],
      "metadata": {
        "id": "yFRZs1Tg9zoT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get all the model children as list\n",
        "model_children = list(csn_net.children())\n",
        "\n",
        "model_weights = []\n",
        "conv_layers = []\n",
        "\n",
        "# Append all the conv layers and their respective weights to the list\n",
        "for i in range(len(model_children)):\n",
        "    if type(model_children[i]) == nn.Conv2d:\n",
        "        model_weights.append(model_children[i].weight)\n",
        "        conv_layers.append(model_children[i])\n",
        "        \n",
        "print(f\"Total convolutional layers: {len(conv_layers)}\")\n",
        "print(f\"Weights shape for the 1st conv layer: {model_weights[0].shape}\")\n",
        "print(f\"Weights shape for the 2nd conv layer: {model_weights[1].shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zm6Ca6TR--6L",
        "outputId": "6fb94ee1-3e21-44b1-900a-af944d681620"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total convolutional layers: 2\n",
            "Weights shape for the 1st conv layer: torch.Size([12, 2, 5, 5])\n",
            "Weights shape for the 2nd conv layer: torch.Size([32, 12, 5, 5])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "first_conv_layer = model_weights[0]\n",
        "print(len(first_conv_layer)) # 12 filters\n",
        "print(first_conv_layer[0].shape) # 5x5 filter matrices for 2 polarities"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zUYJBHlqFAcZ",
        "outputId": "b864032f-5a1b-4d42-f574-5977b267e775"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12\n",
            "torch.Size([2, 5, 5])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(first_conv_layer[0][0].size())\n",
        "first_conv_layer[0][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WrEqMKxFFZdS",
        "outputId": "eb2b6c1c-d596-4057-c823-78244a2c1144"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([5, 5])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.0772, -0.0587,  0.0666,  0.0419, -0.1408],\n",
              "        [ 0.0432,  0.0499, -0.0827, -0.1347, -0.0007],\n",
              "        [-0.1702, -0.1093,  0.0930,  0.0708,  0.0315],\n",
              "        [ 0.0686,  0.0694,  0.1084,  0.1094, -0.0344],\n",
              "        [ 0.0062, -0.1568, -0.0386,  0.0390, -0.1422]],\n",
              "       grad_fn=<SelectBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize filters of the 1st conv layer\n",
        "plt.figure(figsize=(15, 5))\n",
        "for i, filter in enumerate(first_conv_layer): # iterate through 12 filters\n",
        "    plt.subplot(2, 6, i+1)\n",
        "    plt.imshow(filter[0, :].detach(), cmap='gray')\n",
        "    plt.axis('off')\n",
        "    plt.savefig('../filter.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "I0fcw1_9B2SN",
        "outputId": "ecbb8cc9-50e4-451f-c1fc-570720719968"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 12 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAAEaCAYAAAD0cA0RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANoElEQVR4nO3ZW6tV9d/G4bFkLbOdmmKFhlqWJmpJVrYzwzITK4ygRAtaZnqg2UbbSAdZBwaaoCdRnZhGpCRBIUKUZGlIGmkHlmiQQWm5wdwUQuj6v4Fc8HwfbkS4rsM54DMmzLGc8/bX0tHR0dEAAADwf9LlbL8BAACAc5ExBQAAUGBMAQAAFBhTAAAABcYUAABAgTEFAABQ0NrZxUceeSR680suuSTaf/7556P9AwcORPtTpkyJ9n///fdof82aNdH+Dz/8EO0fOnQo2l+3bl20/8QTT0T7ixYtirV79OgRazdN06xcuTLaHzhwYLS/Y8eOaD/97JzrLr300mh/0qRJ0f6uXbui/SNHjkT76fefNHXq1Gj/oYceivZ79+4d7e/ZsyfanzVrVrTfr1+/aP/hhx+O9s8777xov62tLdpPO9PvHidTAAAABcYUAABAgTEFAABQYEwBAAAUGFMAAAAFxhQAAECBMQUAAFBgTAEAABQYUwAAAAXGFAAAQIExBQAAUGBMAQAAFBhTAAAABcYUAABAgTEFAABQYEwBAAAUGFMAAAAFxhQAAECBMQUAAFBgTAEAABQYUwAAAAXGFAAAQIExBQAAUNDa2cW5c+dGb961a9do/+WXX472d+zYEe2/9tpr0X7asGHDov3NmzdH+926dYv2J0+eHO2vX78+2l+0aFGsffXVV8faTdM0hw8fjvanT58e7Y8cOTLaP3r0aLT/zDPPRPsnTpyI9u+6665ov729Pdr/7rvvov1x48ZF+0mvvvpqtH/HHXdE++l/O/fu3Rvt79u3L9pP6969e7Q/adKkaL+trS3aTz//v/76a7R/Jk6mAAAACowpAACAAmMKAACgwJgCAAAoMKYAAAAKjCkAAIACYwoAAKDAmAIAACgwpgAAAAqMKQAAgAJjCgAAoMCYAgAAKDCmAAAACowpAACAAmMKAACgwJgCAAAoMKYAAAAKjCkAAIACYwoAAKDAmAIAACgwpgAAAAqMKQAAgAJjCgAAoKC1s4tjxoyJ3nz06NHR/meffRbtHzp0KNr/+eefo/204cOHR/t79uyJ9tPPz3PPPRftP/roo9F+UteuXaP9FStWRPu9e/eO9pcsWRLtb9q0KdpPmzdvXrR/9OjRaP/YsWPRfo8ePaL9jRs3RvsjR46MtdevXx9rN03T3HTTTdH+oEGDov0BAwZE+0OHDo320z744INo/5Zbbon2p02bFu2PGzcu2r/wwguj/TNxMgUAAFBgTAEAABQYUwAAAAXGFAAAQIExBQAAUGBMAQAAFBhTAAAABcYUAABAgTEFAABQYEwBAAAUGFMAAAAFxhQAAECBMQUAAFBgTAEAABQYUwAAAAXGFAAAQIExBQAAUGBMAQAAFBhTAAAABcYUAABAgTEFAABQYEwBAAAUGFMAAAAFrZ1d3L17d/TmLS0t0X6PHj3O6f57770X7U+YMCHaX7JkSbS/f//+aP/pp5+O9g8ePBjt//TTT9H+ggULYu1jx47F2k3TNO+88060P2bMmGh/8eLF0f68efOi/bTDhw9H+/369Yv2H3jggWg//d07YMCAaP/ZZ5+NtW+//fZYu2ma5ptvvon2L7/88mi/f//+0f6cOXOi/RMnTkT7q1evjvbXrl0b7Y8cOTLab29vj/bTu+VMf79OpgAAAAqMKQAAgAJjCgAAoMCYAgAAKDCmAAAACowpAACAAmMKAACgwJgCAAAoMKYAAAAKjCkAAIACYwoAAKDAmAIAACgwpgAAAAqMKQAAgAJjCgAAoMCYAgAAKDCmAAAACowpAACAAmMKAACgwJgCAAAoMKYAAAAKjCkAAIACYwoAAKCgpaOjo+NMF1etWhW9+ezZs6P9OXPmRPsTJkyI9qdOnRrt79u3L9qfMmVKtP/iiy9G+1988UW0v3///mj/4osvjvZff/31WHvbtm2xdtPkP9vzzz8/2r/++uuj/U8++STaX7ZsWbTft2/faP/kyZPRfltbW7R/7bXXRvubN2+O9k+dOhVrt7S0xNpN0zRbtmyJ9tetWxftT58+Pdr/+OOPo/358+dH+0OHDo32d+3aFe2nv3tvvPHGaH/37t3R/uDBg//zdSdTAAAABcYUAABAgTEFAABQYEwBAAAUGFMAAAAFxhQAAECBMQUAAFBgTAEAABQYUwAAAAXGFAAAQIExBQAAUGBMAQAAFBhTAAAABcYUAABAgTEFAABQYEwBAAAUGFMAAAAFxhQAAECBMQUAAFBgTAEAABQYUwAAAAXGFAAAQIExBQAAUNDS0dHRcbbfBAAAwLnGyRQAAECBMQUAAFBgTAEAABQYUwAAAAXGFAAAQIExBQAAUGBMAQAAFBhTAAAABcYUAABAgTEFAABQYEwBAAAUGFMAAAAFxhQAAECBMQUAAFBgTAEAABQYUwAAAAXGFAAAQIExBQAAUGBMAQAAFBhTAAAABcYUAABAgTEFAABQYEwBAAAUGFMAAAAFxhQAAECBMQUAAFBgTAEAABS0dnbxhRdeiN78zTffjPbHjh0b7X/55ZfR/vbt26P9G264Idrv1atXtH/kyJFo/+abb472T58+He3PnDkz2n/qqadi7blz58baTdM0M2bMiPa7desW7Q8ePDjaP3jwYLTfp0+faP+yyy6L9ltbO/3q/H8bP358tJ/+7mpvb4/2Fy5cGGufOHEi1m6aprnoooui/TVr1kT7V111VbT/9ddfR/vz5s2L9ocMGRLtb9q0KdqfNWtWtJ9+Prt37x7tnzx58j9fdzIFAABQYEwBAAAUGFMAAAAFxhQAAECBMQUAAFBgTAEAABQYUwAAAAXGFAAAQIExBQAAUGBMAQAAFBhTAAAABcYUAABAgTEFAABQYEwBAAAUGFMAAAAFxhQAAECBMQUAAFBgTAEAABQYUwAAAAXGFAAAQIExBQAAUGBMAQAAFBhTAAAABa2dXTx+/Hj05vfee2+0P2jQoGj/lVdeifY3bNgQ7X/77bfR/oEDB6L9999/P9rv2bNntN/W1hbtf/XVV9F+0p133hntp/92H3/88Wh/5syZ0f6VV14Z7a9YsSLanz17drS/dOnSaH/GjBnR/mOPPRbtjx8/PtpfuHBhrL1169ZYu2maZtWqVdF++nfVPffcE+2vWbMm2k8bO3ZstD969Ohof8GCBdH+7t27o/1Ro0ZF+2fiZAoAAKDAmAIAACgwpgAAAAqMKQAAgAJjCgAAoMCYAgAAKDCmAAAACowpAACAAmMKAACgwJgCAAAoMKYAAAAKjCkAAIACYwoAAKDAmAIAACgwpgAAAAqMKQAAgAJjCgAAoMCYAgAAKDCmAAAACowpAACAAmMKAACgwJgCAAAoMKYAAAAKWju7eM0110RvPnz48Gi/T58+0f6DDz4Y7Z86dSraT5syZUq0v3bt2mj/yJEj0f6WLVvO6X7SddddF+336tUr2n/77bej/b59+0b7H330UbS/YsWKaP+PP/6I9pctWxbtP/nkk9F+ly7Z/0ft6OiI9pPuvvvuaH/IkCHR/s6dO6P948ePR/vt7e3R/v79+6P9P//8M9ofOHBgtD9z5sxov3///tH+/fffH+2fiZMpAACAAmMKAACgwJgCAAAoMKYAAAAKjCkAAIACYwoAAKDAmAIAACgwpgAAAAqMKQAAgAJjCgAAoMCYAgAAKDCmAAAACowpAACAAmMKAACgwJgCAAAoMKYAAAAKjCkAAIACYwoAAKDAmAIAACgwpgAAAAqMKQAAgAJjCgAAoMCYAgAAKGjt7OLOnTujN//nn3+i/SVLlkT7GzZsiPa3b98e7acdO3Ys2t+zZ0+0f99990X78+fPj/aXL18e7ScNGTIk2p84cWK0/9JLL0X7u3btiva3bdsW7aelP98PP/ww2h80aFC0v2rVqmj/r7/+ivZ79uwZa/fp0yfWbpqmGTFiRLSffv+zZs2K9jdu3Bjtp916663R/vDhw6P9lStXRvvff/99tJ9+/t96663/fN3JFAAAQIExBQAAUGBMAQAAFBhTAAAABcYUAABAgTEFAABQYEwBAAAUGFMAAAAFxhQAAECBMQUAAFBgTAEAABQYUwAAAAXGFAAAQIExBQAAUGBMAQAAFBhTAAAABcYUAABAgTEFAABQYEwBAAAUGFMAAAAFxhQAAECBMQUAAFBgTAEAABS0dnbx5MmT0ZuvXr062l++fHm0P2zYsGi/S5dze+uOGjUq2r/tttui/dOnT0f7n3/+ebT/448/RvvJz/fTTz+NtZumaSZPnhzt//LLL9H+0qVLo/2///472k9bvHhxtD9ixIhof+LEied0f8CAAdH+2rVrY+3ffvst1m6apnn33Xej/b1790b7W7dujfb//fffaD/tiiuuiPbfeOONaP+CCy6I9qdNmxbtL1iwINo/k3P71zoAAMBZYkwBAAAUGFMAAAAFxhQAAECBMQUAAFBgTAEAABQYUwAAAAXGFAAAQIExBQAAUGBMAQAAFBhTAAAABcYUAABAgTEFAABQYEwBAAAUGFMAAAAFxhQAAECBMQUAAFBgTAEAABQYUwAAAAXGFAAAQIExBQAAUGBMAQAAFBhTAAAABS0dHR0dZ/tNAAAAnGucTAEAABQYUwAAAAXGFAAAQIExBQAAUGBMAQAAFBhTAAAABf8D8RHq2rZzegoAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "second_conv_layer = model_weights[1]\n",
        "print(len(second_conv_layer)) # 32 filters\n",
        "print(second_conv_layer[0].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kVXduradrKdU",
        "outputId": "2fad0d23-858d-494e-f566-885b8c859b5c"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32\n",
            "torch.Size([12, 5, 5])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize filters of the 2nd conv layer\n",
        "plt.figure(figsize=(25, 25))\n",
        "for i, filter in enumerate(second_conv_layer): # iterate through 12 filters\n",
        "    plt.subplot(10, 11, i+1)\n",
        "    plt.imshow(filter[1, :].detach(), cmap='gray')\n",
        "    plt.axis('off')\n",
        "    plt.savefig('../filter.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "EbNHghUXrF1x",
        "outputId": "25713460-fd91-4097-9ea4-1c3cc1875398"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1800 with 32 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ressources\n",
        "1. https://snntorch.readthedocs.io/en/latest/tutorials/index.html\n",
        "2. https://tonic.readthedocs.io/en/latest/\n",
        "3. https://github.com/Shikhargupta/Spiking-Neural-Network "
      ],
      "metadata": {
        "id": "WGCe8ycdn8wV"
      }
    }
  ]
}