{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict Bike Sharing Demand with AutoGluon Template"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project: Predict Bike Sharing Demand with AutoGluon\n",
    "This notebook is a template with each step that you need to complete for the project.\n",
    "\n",
    "Please fill in your code where there are explicit `?` markers in the notebook. You are welcome to add more cells and code as you see fit.\n",
    "\n",
    "Once you have completed all the code implementations, please export your notebook as a HTML file so the reviews can view your code. Make sure you have all outputs correctly outputted.\n",
    "\n",
    "`File-> Export Notebook As... -> Export Notebook as HTML`\n",
    "\n",
    "There is a writeup to complete as well after all code implememtation is done. Please answer all questions and attach the necessary tables and charts. You can complete the writeup in either markdown or PDF.\n",
    "\n",
    "Completing the code template and writeup template will cover all of the rubric points for this project.\n",
    "\n",
    "The rubric contains \"Stand Out Suggestions\" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the \"stand out suggestions\", you can include the code in this notebook and also discuss the results in the writeup file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Create an account with Kaggle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Kaggle Account and download API key\n",
    "Below is example of steps to get the API username and key. Each student will have their own username and key."
   ]
  },
  {
   "attachments": {
    "kaggle1.png": {
     "image/png": "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"
    },
    "kaggle2.png": {
     "image/png": "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"
    },
    "kaggle3.png": {
     "image/png": "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"
    },
    "kaggle4.png": {
     "image/png": "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"
    },
    "kaggle5.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Open account settings.\n",
    "![kaggle1.png](attachment:kaggle1.png)\n",
    "![kaggle2.png](attachment:kaggle2.png)\n",
    "2. Scroll down to API and click Create New API Token.\n",
    "![kaggle3.png](attachment:kaggle3.png)\n",
    "![kaggle4.png](attachment:kaggle4.png)\n",
    "3. Open up `kaggle.json` and use the username and key.\n",
    "![kaggle5.png](attachment:kaggle5.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Download the Kaggle dataset using the kaggle python library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Open up Sagemaker Studio and use starter template"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Notebook should be using a `ml.t3.medium` instance (2 vCPU + 4 GiB)\n",
    "2. Notebook should be using kernal: `Python 3 (MXNet 1.8 Python 3.7 CPU Optimized)`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:09:37.549394Z",
     "iopub.status.busy": "2025-12-07T15:09:37.549141Z",
     "iopub.status.idle": "2025-12-07T15:10:04.195288Z",
     "shell.execute_reply": "2025-12-07T15:10:04.194372Z",
     "shell.execute_reply.started": "2025-12-07T15:09:37.549371Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pip in /opt/conda/lib/python3.12/site-packages (25.2)\n",
      "Collecting pip\n",
      "  Using cached pip-25.3-py3-none-any.whl.metadata (4.7 kB)\n",
      "Using cached pip-25.3-py3-none-any.whl (1.8 MB)\n",
      "Installing collected packages: pip\n",
      "  Attempting uninstall: pip\n",
      "    Found existing installation: pip 25.2\n",
      "    Uninstalling pip-25.2:\n",
      "      Successfully uninstalled pip-25.2\n",
      "Successfully installed pip-25.3\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.12/site-packages (80.9.0)\n",
      "Requirement already satisfied: wheel in /opt/conda/lib/python3.12/site-packages (0.45.1)\n",
      "Collecting mxnet<2.0.0\n",
      "  Using cached mxnet-1.9.1-py3-none-manylinux2014_x86_64.whl.metadata (3.4 kB)\n",
      "Collecting bokeh==2.0.1\n",
      "  Using cached bokeh-2.0.1.tar.gz (8.6 MB)\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25lerror\n",
      "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
      "  \n",
      "  \u001b[31m×\u001b[0m \u001b[32mGetting requirements to build wheel\u001b[0m did not run successfully.\n",
      "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
      "  \u001b[31m╰─>\u001b[0m \u001b[31m[34 lines of output]\u001b[0m\n",
      "  \u001b[31m   \u001b[0m /tmp/pip-install-2rka8jb9/bokeh_0e63f3deca7c44409ff3eb4459b49b3c/versioneer.py:416: SyntaxWarning: invalid escape sequence '\\s'\n",
      "  \u001b[31m   \u001b[0m   LONG_VERSION_PY['git'] = '''\n",
      "  \u001b[31m   \u001b[0m Traceback (most recent call last):\n",
      "  \u001b[31m   \u001b[0m   File \"/opt/conda/lib/python3.12/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py\", line 389, in <module>\n",
      "  \u001b[31m   \u001b[0m     main()\n",
      "  \u001b[31m   \u001b[0m   File \"/opt/conda/lib/python3.12/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py\", line 373, in main\n",
      "  \u001b[31m   \u001b[0m     json_out[\"return_val\"] = hook(**hook_input[\"kwargs\"])\n",
      "  \u001b[31m   \u001b[0m                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  \u001b[31m   \u001b[0m   File \"/opt/conda/lib/python3.12/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py\", line 143, in get_requires_for_build_wheel\n",
      "  \u001b[31m   \u001b[0m     return hook(config_settings)\n",
      "  \u001b[31m   \u001b[0m            ^^^^^^^^^^^^^^^^^^^^^\n",
      "  \u001b[31m   \u001b[0m   File \"/tmp/pip-build-env-5etrvsyy/overlay/lib/python3.12/site-packages/setuptools/build_meta.py\", line 331, in get_requires_for_build_wheel\n",
      "  \u001b[31m   \u001b[0m     return self._get_build_requires(config_settings, requirements=[])\n",
      "  \u001b[31m   \u001b[0m            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  \u001b[31m   \u001b[0m   File \"/tmp/pip-build-env-5etrvsyy/overlay/lib/python3.12/site-packages/setuptools/build_meta.py\", line 301, in _get_build_requires\n",
      "  \u001b[31m   \u001b[0m     self.run_setup()\n",
      "  \u001b[31m   \u001b[0m   File \"/tmp/pip-build-env-5etrvsyy/overlay/lib/python3.12/site-packages/setuptools/build_meta.py\", line 512, in run_setup\n",
      "  \u001b[31m   \u001b[0m     super().run_setup(setup_script=setup_script)\n",
      "  \u001b[31m   \u001b[0m   File \"/tmp/pip-build-env-5etrvsyy/overlay/lib/python3.12/site-packages/setuptools/build_meta.py\", line 317, in run_setup\n",
      "  \u001b[31m   \u001b[0m     exec(code, locals())\n",
      "  \u001b[31m   \u001b[0m   File \"<string>\", line 118, in <module>\n",
      "  \u001b[31m   \u001b[0m   File \"/tmp/pip-install-2rka8jb9/bokeh_0e63f3deca7c44409ff3eb4459b49b3c/_setup_support.py\", line 243, in get_version\n",
      "  \u001b[31m   \u001b[0m     return versioneer.get_version()\n",
      "  \u001b[31m   \u001b[0m            ^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  \u001b[31m   \u001b[0m   File \"/tmp/pip-install-2rka8jb9/bokeh_0e63f3deca7c44409ff3eb4459b49b3c/versioneer.py\", line 1484, in get_version\n",
      "  \u001b[31m   \u001b[0m     return get_versions()[\"version\"]\n",
      "  \u001b[31m   \u001b[0m            ^^^^^^^^^^^^^^\n",
      "  \u001b[31m   \u001b[0m   File \"/tmp/pip-install-2rka8jb9/bokeh_0e63f3deca7c44409ff3eb4459b49b3c/versioneer.py\", line 1416, in get_versions\n",
      "  \u001b[31m   \u001b[0m     cfg = get_config_from_root(root)\n",
      "  \u001b[31m   \u001b[0m           ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  \u001b[31m   \u001b[0m   File \"/tmp/pip-install-2rka8jb9/bokeh_0e63f3deca7c44409ff3eb4459b49b3c/versioneer.py\", line 340, in get_config_from_root\n",
      "  \u001b[31m   \u001b[0m     parser = configparser.SafeConfigParser()\n",
      "  \u001b[31m   \u001b[0m              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  \u001b[31m   \u001b[0m AttributeError: module 'configparser' has no attribute 'SafeConfigParser'. Did you mean: 'RawConfigParser'?\n",
      "  \u001b[31m   \u001b[0m \u001b[31m[end of output]\u001b[0m\n",
      "  \n",
      "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
      "\u001b[31mERROR: Failed to build 'bokeh' when getting requirements to build wheel\u001b[0m\u001b[31m\n",
      "\u001b[0m\u001b[?25hRequirement already satisfied: autogluon in /opt/conda/lib/python3.12/site-packages (1.4.0)\n",
      "Requirement already satisfied: autogluon.core==1.4.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.core[all]==1.4.0->autogluon) (1.4.0)\n",
      "Requirement already satisfied: autogluon.features==1.4.0 in /opt/conda/lib/python3.12/site-packages (from autogluon) (1.4.0)\n",
      "Requirement already satisfied: autogluon.tabular==1.4.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.tabular[all]==1.4.0->autogluon) (1.4.0)\n",
      "Requirement already satisfied: autogluon.multimodal==1.4.0 in /opt/conda/lib/python3.12/site-packages (from autogluon) (1.4.0)\n",
      "Requirement already satisfied: autogluon.timeseries==1.4.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.timeseries[all]==1.4.0->autogluon) (1.4.0)\n",
      "Requirement already satisfied: numpy<2.4.0,>=1.25.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (1.26.4)\n",
      "Requirement already satisfied: scipy<1.17,>=1.5.4 in /opt/conda/lib/python3.12/site-packages (from autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (1.16.1)\n",
      "Requirement already satisfied: scikit-learn<1.8.0,>=1.4.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (1.7.1)\n",
      "Requirement already satisfied: networkx<4,>=3.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (3.5)\n",
      "Requirement already satisfied: pandas<2.4.0,>=2.0.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (2.3.1)\n",
      "Requirement already satisfied: tqdm<5,>=4.38 in /opt/conda/lib/python3.12/site-packages (from autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (4.67.1)\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.12/site-packages (from autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (2.32.4)\n",
      "Requirement already satisfied: matplotlib<3.11,>=3.7.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (3.10.5)\n",
      "Requirement already satisfied: boto3<2,>=1.10 in /opt/conda/lib/python3.12/site-packages (from autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (1.37.1)\n",
      "Requirement already satisfied: autogluon.common==1.4.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (1.4.0)\n",
      "Requirement already satisfied: pyarrow<21.0.0,>=7.0.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.common==1.4.0->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (19.0.1)\n",
      "Requirement already satisfied: psutil<7.1.0,>=5.7.3 in /opt/conda/lib/python3.12/site-packages (from autogluon.common==1.4.0->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (5.9.8)\n",
      "Requirement already satisfied: joblib<1.7,>=1.2 in /opt/conda/lib/python3.12/site-packages (from autogluon.common==1.4.0->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (1.5.1)\n",
      "Requirement already satisfied: ray<2.45,>=2.10.0 in /opt/conda/lib/python3.12/site-packages (from ray[default]<2.45,>=2.10.0; extra == \"all\"->autogluon.core[all]==1.4.0->autogluon) (2.44.1)\n",
      "Requirement already satisfied: hyperopt<0.2.8,>=0.2.7 in /opt/conda/lib/python3.12/site-packages (from autogluon.core[all]==1.4.0->autogluon) (0.2.7)\n",
      "Requirement already satisfied: Pillow<12,>=10.0.1 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (11.3.0)\n",
      "Requirement already satisfied: torch<2.8,>=2.2 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (2.6.0)\n",
      "Requirement already satisfied: lightning<2.8,>=2.2 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (2.5.3)\n",
      "Collecting transformers<4.50,>=4.38.0 (from transformers[sentencepiece]<4.50,>=4.38.0->autogluon.multimodal==1.4.0->autogluon)\n",
      "  Downloading transformers-4.49.0-py3-none-any.whl.metadata (44 kB)\n",
      "Requirement already satisfied: accelerate<2.0,>=0.34.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (1.10.0)\n",
      "Requirement already satisfied: fsspec<=2025.3 in /opt/conda/lib/python3.12/site-packages (from fsspec[http]<=2025.3->autogluon.multimodal==1.4.0->autogluon) (2024.12.0)\n",
      "Requirement already satisfied: jsonschema<4.24,>=4.18 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (4.23.0)\n",
      "Requirement already satisfied: seqeval<1.3.0,>=1.2.2 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (1.2.2)\n",
      "Requirement already satisfied: evaluate<0.5.0,>=0.4.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (0.4.1)\n",
      "Requirement already satisfied: timm<1.0.7,>=0.9.5 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (1.0.3)\n",
      "Requirement already satisfied: torchvision<0.23.0,>=0.16.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (0.21.0)\n",
      "Requirement already satisfied: scikit-image<0.26.0,>=0.19.1 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (0.25.2)\n",
      "Requirement already satisfied: text-unidecode<1.4,>=1.3 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (1.3)\n",
      "Requirement already satisfied: torchmetrics<1.8,>=1.2.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (1.7.4)\n",
      "Requirement already satisfied: omegaconf<2.4.0,>=2.1.1 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (2.3.0)\n",
      "Requirement already satisfied: pytorch-metric-learning<2.9,>=1.3.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (2.8.1)\n",
      "Requirement already satisfied: nlpaug<1.2.0,>=1.1.10 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (1.1.11)\n",
      "Requirement already satisfied: nltk<3.10,>=3.4.5 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (3.9.1)\n",
      "Requirement already satisfied: openmim<0.4.0,>=0.3.7 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (0.3.7)\n",
      "Requirement already satisfied: defusedxml<0.7.2,>=0.7.1 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (0.7.1)\n",
      "Requirement already satisfied: jinja2<3.2,>=3.0.3 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (3.1.6)\n",
      "Requirement already satisfied: tensorboard<3,>=2.9 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (2.18.0)\n",
      "Requirement already satisfied: pytesseract<0.4,>=0.3.9 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (0.3.13)\n",
      "Collecting nvidia-ml-py3<8.0,>=7.352.0 (from autogluon.multimodal==1.4.0->autogluon)\n",
      "  Downloading nvidia-ml-py3-7.352.0.tar.gz (19 kB)\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: pdf2image<1.19,>=1.17.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.multimodal==1.4.0->autogluon) (1.17.0)\n",
      "Requirement already satisfied: xgboost<3.1,>=2.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.tabular[all]==1.4.0->autogluon) (2.1.4)\n",
      "Requirement already satisfied: catboost<1.3,>=1.2 in /opt/conda/lib/python3.12/site-packages (from autogluon.tabular[all]==1.4.0->autogluon) (1.2.7)\n",
      "Collecting loguru (from autogluon.tabular[all]==1.4.0->autogluon)\n",
      "  Downloading loguru-0.7.3-py3-none-any.whl.metadata (22 kB)\n",
      "Requirement already satisfied: huggingface_hub[torch] in /opt/conda/lib/python3.12/site-packages (from autogluon.tabular[all]==1.4.0->autogluon) (0.34.4)\n",
      "Requirement already satisfied: spacy<3.9 in /opt/conda/lib/python3.12/site-packages (from autogluon.tabular[all]==1.4.0->autogluon) (3.8.7)\n",
      "Requirement already satisfied: fastai<2.9,>=2.3.1 in /opt/conda/lib/python3.12/site-packages (from autogluon.tabular[all]==1.4.0->autogluon) (2.8.3)\n",
      "Requirement already satisfied: lightgbm<4.7,>=4.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.tabular[all]==1.4.0->autogluon) (4.6.0)\n",
      "Collecting einx (from autogluon.tabular[all]==1.4.0->autogluon)\n",
      "  Downloading einx-0.3.0-py3-none-any.whl.metadata (6.9 kB)\n",
      "Requirement already satisfied: pytorch_lightning in /opt/conda/lib/python3.12/site-packages (from autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (2.5.3)\n",
      "Requirement already satisfied: gluonts<0.17,>=0.15.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (0.16.2)\n",
      "Requirement already satisfied: statsforecast<2.0.2,>=1.7.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (2.0.1)\n",
      "Requirement already satisfied: mlforecast<0.15.0,>=0.14.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (0.14.0)\n",
      "Requirement already satisfied: utilsforecast<0.2.12,>=0.2.3 in /opt/conda/lib/python3.12/site-packages (from autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (0.2.11)\n",
      "Requirement already satisfied: coreforecast<0.0.17,>=0.0.12 in /opt/conda/lib/python3.12/site-packages (from autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (0.0.16)\n",
      "Requirement already satisfied: fugue>=0.9.0 in /opt/conda/lib/python3.12/site-packages (from autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (0.9.1)\n",
      "Requirement already satisfied: orjson~=3.9 in /opt/conda/lib/python3.12/site-packages (from autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (3.11.2)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.12/site-packages (from accelerate<2.0,>=0.34.0->autogluon.multimodal==1.4.0->autogluon) (24.2)\n",
      "Requirement already satisfied: pyyaml in /opt/conda/lib/python3.12/site-packages (from accelerate<2.0,>=0.34.0->autogluon.multimodal==1.4.0->autogluon) (6.0.2)\n",
      "Requirement already satisfied: safetensors>=0.4.3 in /opt/conda/lib/python3.12/site-packages (from accelerate<2.0,>=0.34.0->autogluon.multimodal==1.4.0->autogluon) (0.6.2)\n",
      "Requirement already satisfied: botocore<1.38.0,>=1.37.1 in /opt/conda/lib/python3.12/site-packages (from boto3<2,>=1.10->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (1.37.1)\n",
      "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/conda/lib/python3.12/site-packages (from boto3<2,>=1.10->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (1.0.1)\n",
      "Requirement already satisfied: s3transfer<0.12.0,>=0.11.0 in /opt/conda/lib/python3.12/site-packages (from boto3<2,>=1.10->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (0.11.3)\n",
      "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /opt/conda/lib/python3.12/site-packages (from botocore<1.38.0,>=1.37.1->boto3<2,>=1.10->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (2.9.0.post0)\n",
      "Requirement already satisfied: urllib3!=2.2.0,<3,>=1.25.4 in /opt/conda/lib/python3.12/site-packages (from botocore<1.38.0,>=1.37.1->boto3<2,>=1.10->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (1.26.19)\n",
      "Requirement already satisfied: graphviz in /opt/conda/lib/python3.12/site-packages (from catboost<1.3,>=1.2->autogluon.tabular[all]==1.4.0->autogluon) (0.21)\n",
      "Requirement already satisfied: plotly in /opt/conda/lib/python3.12/site-packages (from catboost<1.3,>=1.2->autogluon.tabular[all]==1.4.0->autogluon) (6.0.1)\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.12/site-packages (from catboost<1.3,>=1.2->autogluon.tabular[all]==1.4.0->autogluon) (1.17.0)\n",
      "Requirement already satisfied: datasets>=2.0.0 in /opt/conda/lib/python3.12/site-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==1.4.0->autogluon) (2.2.1)\n",
      "Requirement already satisfied: dill in /opt/conda/lib/python3.12/site-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==1.4.0->autogluon) (0.4.0)\n",
      "Requirement already satisfied: xxhash in /opt/conda/lib/python3.12/site-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==1.4.0->autogluon) (3.5.0)\n",
      "Requirement already satisfied: multiprocess in /opt/conda/lib/python3.12/site-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==1.4.0->autogluon) (0.70.18)\n",
      "Requirement already satisfied: responses<0.19 in /opt/conda/lib/python3.12/site-packages (from evaluate<0.5.0,>=0.4.0->autogluon.multimodal==1.4.0->autogluon) (0.18.0)\n",
      "Requirement already satisfied: pip in /opt/conda/lib/python3.12/site-packages (from fastai<2.9,>=2.3.1->autogluon.tabular[all]==1.4.0->autogluon) (25.3)\n",
      "Requirement already satisfied: fastdownload<2,>=0.0.5 in /opt/conda/lib/python3.12/site-packages (from fastai<2.9,>=2.3.1->autogluon.tabular[all]==1.4.0->autogluon) (0.0.7)\n",
      "Requirement already satisfied: fastcore<1.9,>=1.8.0 in /opt/conda/lib/python3.12/site-packages (from fastai<2.9,>=2.3.1->autogluon.tabular[all]==1.4.0->autogluon) (1.8.7)\n",
      "Requirement already satisfied: fasttransform>=0.0.2 in /opt/conda/lib/python3.12/site-packages (from fastai<2.9,>=2.3.1->autogluon.tabular[all]==1.4.0->autogluon) (0.0.2)\n",
      "Requirement already satisfied: fastprogress>=0.2.4 in /opt/conda/lib/python3.12/site-packages (from fastai<2.9,>=2.3.1->autogluon.tabular[all]==1.4.0->autogluon) (1.0.3)\n",
      "Requirement already satisfied: plum-dispatch in /opt/conda/lib/python3.12/site-packages (from fastai<2.9,>=2.3.1->autogluon.tabular[all]==1.4.0->autogluon) (2.5.7)\n",
      "Requirement already satisfied: cloudpickle in /opt/conda/lib/python3.12/site-packages (from fastai<2.9,>=2.3.1->autogluon.tabular[all]==1.4.0->autogluon) (3.1.1)\n",
      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /opt/conda/lib/python3.12/site-packages (from fsspec[http]<=2025.3->autogluon.multimodal==1.4.0->autogluon) (3.12.15)\n",
      "Requirement already satisfied: pydantic<3,>=1.7 in /opt/conda/lib/python3.12/site-packages (from gluonts<0.17,>=0.15.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (2.11.7)\n",
      "Requirement already satisfied: toolz~=0.10 in /opt/conda/lib/python3.12/site-packages (from gluonts<0.17,>=0.15.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (0.12.1)\n",
      "Requirement already satisfied: typing-extensions~=4.0 in /opt/conda/lib/python3.12/site-packages (from gluonts<0.17,>=0.15.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (4.14.1)\n",
      "Requirement already satisfied: future in /opt/conda/lib/python3.12/site-packages (from hyperopt<0.2.8,>=0.2.7->autogluon.core[all]==1.4.0->autogluon) (1.0.0)\n",
      "Requirement already satisfied: py4j in /opt/conda/lib/python3.12/site-packages (from hyperopt<0.2.8,>=0.2.7->autogluon.core[all]==1.4.0->autogluon) (0.10.9.9)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.12/site-packages (from jinja2<3.2,>=3.0.3->autogluon.multimodal==1.4.0->autogluon) (3.0.2)\n",
      "Requirement already satisfied: attrs>=22.2.0 in /opt/conda/lib/python3.12/site-packages (from jsonschema<4.24,>=4.18->autogluon.multimodal==1.4.0->autogluon) (23.2.0)\n",
      "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /opt/conda/lib/python3.12/site-packages (from jsonschema<4.24,>=4.18->autogluon.multimodal==1.4.0->autogluon) (2025.4.1)\n",
      "Requirement already satisfied: referencing>=0.28.4 in /opt/conda/lib/python3.12/site-packages (from jsonschema<4.24,>=4.18->autogluon.multimodal==1.4.0->autogluon) (0.36.2)\n",
      "Requirement already satisfied: rpds-py>=0.7.1 in /opt/conda/lib/python3.12/site-packages (from jsonschema<4.24,>=4.18->autogluon.multimodal==1.4.0->autogluon) (0.27.0)\n",
      "Requirement already satisfied: lightning-utilities<2.0,>=0.10.0 in /opt/conda/lib/python3.12/site-packages (from lightning<2.8,>=2.2->autogluon.multimodal==1.4.0->autogluon) (0.15.2)\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.12/site-packages (from lightning-utilities<2.0,>=0.10.0->lightning<2.8,>=2.2->autogluon.multimodal==1.4.0->autogluon) (80.9.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.12/site-packages (from matplotlib<3.11,>=3.7.0->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (1.3.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.12/site-packages (from matplotlib<3.11,>=3.7.0->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.12/site-packages (from matplotlib<3.11,>=3.7.0->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (4.59.1)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.12/site-packages (from matplotlib<3.11,>=3.7.0->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (1.4.9)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.12/site-packages (from matplotlib<3.11,>=3.7.0->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (3.2.3)\n",
      "Requirement already satisfied: numba in /opt/conda/lib/python3.12/site-packages (from mlforecast<0.15.0,>=0.14.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (0.61.2)\n",
      "Requirement already satisfied: optuna in /opt/conda/lib/python3.12/site-packages (from mlforecast<0.15.0,>=0.14.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (4.4.0)\n",
      "Requirement already satisfied: window-ops in /opt/conda/lib/python3.12/site-packages (from mlforecast<0.15.0,>=0.14.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (0.0.15)\n",
      "Requirement already satisfied: gdown>=4.0.0 in /opt/conda/lib/python3.12/site-packages (from nlpaug<1.2.0,>=1.1.10->autogluon.multimodal==1.4.0->autogluon) (5.2.0)\n",
      "Requirement already satisfied: click in /opt/conda/lib/python3.12/site-packages (from nltk<3.10,>=3.4.5->autogluon.multimodal==1.4.0->autogluon) (8.2.1)\n",
      "Requirement already satisfied: regex>=2021.8.3 in /opt/conda/lib/python3.12/site-packages (from nltk<3.10,>=3.4.5->autogluon.multimodal==1.4.0->autogluon) (2025.7.34)\n",
      "Requirement already satisfied: antlr4-python3-runtime==4.9.* in /opt/conda/lib/python3.12/site-packages (from omegaconf<2.4.0,>=2.1.1->autogluon.multimodal==1.4.0->autogluon) (4.9.3)\n",
      "Requirement already satisfied: colorama in /opt/conda/lib/python3.12/site-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.4.0->autogluon) (0.4.6)\n",
      "Requirement already satisfied: model-index in /opt/conda/lib/python3.12/site-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.4.0->autogluon) (0.1.11)\n",
      "Requirement already satisfied: rich in /opt/conda/lib/python3.12/site-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.4.0->autogluon) (13.9.4)\n",
      "Requirement already satisfied: tabulate in /opt/conda/lib/python3.12/site-packages (from openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.4.0->autogluon) (0.9.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.12/site-packages (from pandas<2.4.0,>=2.0.0->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (2024.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.12/site-packages (from pandas<2.4.0,>=2.0.0->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (2025.2)\n",
      "Requirement already satisfied: annotated-types>=0.6.0 in /opt/conda/lib/python3.12/site-packages (from pydantic<3,>=1.7->gluonts<0.17,>=0.15.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (0.7.0)\n",
      "Requirement already satisfied: pydantic-core==2.33.2 in /opt/conda/lib/python3.12/site-packages (from pydantic<3,>=1.7->gluonts<0.17,>=0.15.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (2.33.2)\n",
      "Requirement already satisfied: typing-inspection>=0.4.0 in /opt/conda/lib/python3.12/site-packages (from pydantic<3,>=1.7->gluonts<0.17,>=0.15.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (0.4.1)\n",
      "Requirement already satisfied: charset_normalizer<4,>=2 in /opt/conda/lib/python3.12/site-packages (from requests->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (3.4.3)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.12/site-packages (from requests->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (3.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.12/site-packages (from requests->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (2025.8.3)\n",
      "Requirement already satisfied: imageio!=2.35.0,>=2.33 in /opt/conda/lib/python3.12/site-packages (from scikit-image<0.26.0,>=0.19.1->autogluon.multimodal==1.4.0->autogluon) (2.37.0)\n",
      "Requirement already satisfied: tifffile>=2022.8.12 in /opt/conda/lib/python3.12/site-packages (from scikit-image<0.26.0,>=0.19.1->autogluon.multimodal==1.4.0->autogluon) (2025.6.11)\n",
      "Requirement already satisfied: lazy-loader>=0.4 in /opt/conda/lib/python3.12/site-packages (from scikit-image<0.26.0,>=0.19.1->autogluon.multimodal==1.4.0->autogluon) (0.4)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in /opt/conda/lib/python3.12/site-packages (from scikit-learn<1.8.0,>=1.4.0->autogluon.core==1.4.0->autogluon.core[all]==1.4.0->autogluon) (3.6.0)\n",
      "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /opt/conda/lib/python3.12/site-packages (from spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (3.0.12)\n",
      "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /opt/conda/lib/python3.12/site-packages (from spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (1.0.5)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /opt/conda/lib/python3.12/site-packages (from spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (1.0.10)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /opt/conda/lib/python3.12/site-packages (from spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (2.0.11)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /opt/conda/lib/python3.12/site-packages (from spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (3.0.9)\n",
      "Requirement already satisfied: thinc<8.4.0,>=8.3.4 in /opt/conda/lib/python3.12/site-packages (from spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (8.3.6)\n",
      "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /opt/conda/lib/python3.12/site-packages (from spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (1.1.3)\n",
      "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /opt/conda/lib/python3.12/site-packages (from spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (2.5.1)\n",
      "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /opt/conda/lib/python3.12/site-packages (from spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (2.0.10)\n",
      "Requirement already satisfied: weasel<0.5.0,>=0.1.0 in /opt/conda/lib/python3.12/site-packages (from spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (0.4.1)\n",
      "Requirement already satisfied: typer<1.0.0,>=0.3.0 in /opt/conda/lib/python3.12/site-packages (from spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (0.16.0)\n",
      "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /opt/conda/lib/python3.12/site-packages (from spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (3.4.1)\n",
      "Requirement already satisfied: language-data>=1.2 in /opt/conda/lib/python3.12/site-packages (from langcodes<4.0.0,>=3.2.0->spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (1.3.0)\n",
      "Requirement already satisfied: ujson>=1.35 in /opt/conda/lib/python3.12/site-packages (from srsly<3.0.0,>=2.4.3->spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (5.10.0)\n",
      "Requirement already satisfied: statsmodels>=0.13.2 in /opt/conda/lib/python3.12/site-packages (from statsforecast<2.0.2,>=1.7.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (0.14.5)\n",
      "Requirement already satisfied: absl-py>=0.4 in /opt/conda/lib/python3.12/site-packages (from tensorboard<3,>=2.9->autogluon.multimodal==1.4.0->autogluon) (2.3.1)\n",
      "Requirement already satisfied: grpcio>=1.48.2 in /opt/conda/lib/python3.12/site-packages (from tensorboard<3,>=2.9->autogluon.multimodal==1.4.0->autogluon) (1.67.1)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /opt/conda/lib/python3.12/site-packages (from tensorboard<3,>=2.9->autogluon.multimodal==1.4.0->autogluon) (3.8.2)\n",
      "Requirement already satisfied: protobuf in /opt/conda/lib/python3.12/site-packages (from tensorboard<3,>=2.9->autogluon.multimodal==1.4.0->autogluon) (5.28.3)\n",
      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /opt/conda/lib/python3.12/site-packages (from tensorboard<3,>=2.9->autogluon.multimodal==1.4.0->autogluon) (0.7.0)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in /opt/conda/lib/python3.12/site-packages (from tensorboard<3,>=2.9->autogluon.multimodal==1.4.0->autogluon) (3.1.3)\n",
      "Requirement already satisfied: blis<1.4.0,>=1.3.0 in /opt/conda/lib/python3.12/site-packages (from thinc<8.4.0,>=8.3.4->spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (1.3.0)\n",
      "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /opt/conda/lib/python3.12/site-packages (from thinc<8.4.0,>=8.3.4->spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (0.1.5)\n",
      "INFO: pip is looking at multiple versions of thinc to determine which version is compatible with other requirements. This could take a while.\n",
      "Collecting thinc<8.4.0,>=8.3.4 (from spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon)\n",
      "  Downloading thinc-8.3.10-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (15 kB)\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.12/site-packages (from torch<2.8,>=2.2->autogluon.multimodal==1.4.0->autogluon) (3.19.1)\n",
      "Requirement already satisfied: sympy!=1.13.2,>=1.13.1 in /opt/conda/lib/python3.12/site-packages (from torch<2.8,>=2.2->autogluon.multimodal==1.4.0->autogluon) (1.14.0)\n",
      "Requirement already satisfied: tokenizers<0.22,>=0.21 in /opt/conda/lib/python3.12/site-packages (from transformers<4.50,>=4.38.0->transformers[sentencepiece]<4.50,>=4.38.0->autogluon.multimodal==1.4.0->autogluon) (0.21.4)\n",
      "Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /opt/conda/lib/python3.12/site-packages (from huggingface_hub[torch]->autogluon.tabular[all]==1.4.0->autogluon) (1.1.7)\n",
      "Requirement already satisfied: sentencepiece!=0.1.92,>=0.1.91 in /opt/conda/lib/python3.12/site-packages (from transformers[sentencepiece]<4.50,>=4.38.0->autogluon.multimodal==1.4.0->autogluon) (0.2.0)\n",
      "Requirement already satisfied: shellingham>=1.3.0 in /opt/conda/lib/python3.12/site-packages (from typer<1.0.0,>=0.3.0->spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (1.5.4)\n",
      "Requirement already satisfied: cloudpathlib<1.0.0,>=0.7.0 in /opt/conda/lib/python3.12/site-packages (from weasel<0.5.0,>=0.1.0->spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (0.21.1)\n",
      "Requirement already satisfied: smart-open<8.0.0,>=5.2.1 in /opt/conda/lib/python3.12/site-packages (from weasel<0.5.0,>=0.1.0->spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (7.3.0.post1)\n",
      "Requirement already satisfied: wrapt in /opt/conda/lib/python3.12/site-packages (from smart-open<8.0.0,>=5.2.1->weasel<0.5.0,>=0.1.0->spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (1.17.3)\n",
      "Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /opt/conda/lib/python3.12/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3->autogluon.multimodal==1.4.0->autogluon) (2.6.1)\n",
      "Requirement already satisfied: aiosignal>=1.4.0 in /opt/conda/lib/python3.12/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3->autogluon.multimodal==1.4.0->autogluon) (1.4.0)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.12/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3->autogluon.multimodal==1.4.0->autogluon) (1.7.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.12/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3->autogluon.multimodal==1.4.0->autogluon) (6.6.3)\n",
      "Requirement already satisfied: propcache>=0.2.0 in /opt/conda/lib/python3.12/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3->autogluon.multimodal==1.4.0->autogluon) (0.3.1)\n",
      "Requirement already satisfied: yarl<2.0,>=1.17.0 in /opt/conda/lib/python3.12/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3->autogluon.multimodal==1.4.0->autogluon) (1.20.1)\n",
      "Requirement already satisfied: triad>=0.9.7 in /opt/conda/lib/python3.12/site-packages (from fugue>=0.9.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (0.9.8)\n",
      "Requirement already satisfied: adagio>=0.2.4 in /opt/conda/lib/python3.12/site-packages (from fugue>=0.9.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (0.2.6)\n",
      "Requirement already satisfied: beautifulsoup4 in /opt/conda/lib/python3.12/site-packages (from gdown>=4.0.0->nlpaug<1.2.0,>=1.1.10->autogluon.multimodal==1.4.0->autogluon) (4.13.4)\n",
      "Requirement already satisfied: marisa-trie>=1.1.0 in /opt/conda/lib/python3.12/site-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<3.9->autogluon.tabular[all]==1.4.0->autogluon) (1.2.1)\n",
      "Requirement already satisfied: llvmlite<0.45,>=0.44.0dev0 in /opt/conda/lib/python3.12/site-packages (from numba->mlforecast<0.15.0,>=0.14.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (0.44.0)\n",
      "Requirement already satisfied: markdown-it-py>=2.2.0 in /opt/conda/lib/python3.12/site-packages (from rich->openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.4.0->autogluon) (4.0.0)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /opt/conda/lib/python3.12/site-packages (from rich->openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.4.0->autogluon) (2.19.2)\n",
      "Requirement already satisfied: mdurl~=0.1 in /opt/conda/lib/python3.12/site-packages (from markdown-it-py>=2.2.0->rich->openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.4.0->autogluon) (0.1.2)\n",
      "Requirement already satisfied: patsy>=0.5.6 in /opt/conda/lib/python3.12/site-packages (from statsmodels>=0.13.2->statsforecast<2.0.2,>=1.7.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (1.0.1)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/lib/python3.12/site-packages (from sympy!=1.13.2,>=1.13.1->torch<2.8,>=2.2->autogluon.multimodal==1.4.0->autogluon) (1.3.0)\n",
      "Requirement already satisfied: fs in /opt/conda/lib/python3.12/site-packages (from triad>=0.9.7->fugue>=0.9.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (2.4.16)\n",
      "Requirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.12/site-packages (from beautifulsoup4->gdown>=4.0.0->nlpaug<1.2.0,>=1.1.10->autogluon.multimodal==1.4.0->autogluon) (2.7)\n",
      "Requirement already satisfied: frozendict in /opt/conda/lib/python3.12/site-packages (from einx->autogluon.tabular[all]==1.4.0->autogluon) (2.4.6)\n",
      "Requirement already satisfied: appdirs~=1.4.3 in /opt/conda/lib/python3.12/site-packages (from fs->triad>=0.9.7->fugue>=0.9.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (1.4.4)\n",
      "Requirement already satisfied: ordered-set in /opt/conda/lib/python3.12/site-packages (from model-index->openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.4.0->autogluon) (4.1.0)\n",
      "Requirement already satisfied: alembic>=1.5.0 in /opt/conda/lib/python3.12/site-packages (from optuna->mlforecast<0.15.0,>=0.14.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (1.16.4)\n",
      "Requirement already satisfied: colorlog in /opt/conda/lib/python3.12/site-packages (from optuna->mlforecast<0.15.0,>=0.14.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (6.9.0)\n",
      "Requirement already satisfied: sqlalchemy>=1.4.2 in /opt/conda/lib/python3.12/site-packages (from optuna->mlforecast<0.15.0,>=0.14.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (2.0.43)\n",
      "Requirement already satisfied: Mako in /opt/conda/lib/python3.12/site-packages (from alembic>=1.5.0->optuna->mlforecast<0.15.0,>=0.14.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (1.3.10)\n",
      "Requirement already satisfied: greenlet>=1 in /opt/conda/lib/python3.12/site-packages (from sqlalchemy>=1.4.2->optuna->mlforecast<0.15.0,>=0.14.0->autogluon.timeseries==1.4.0->autogluon.timeseries[all]==1.4.0->autogluon) (3.2.4)\n",
      "Requirement already satisfied: narwhals>=1.15.1 in /opt/conda/lib/python3.12/site-packages (from plotly->catboost<1.3,>=1.2->autogluon.tabular[all]==1.4.0->autogluon) (2.1.2)\n",
      "Requirement already satisfied: beartype>=0.16.2 in /opt/conda/lib/python3.12/site-packages (from plum-dispatch->fastai<2.9,>=2.3.1->autogluon.tabular[all]==1.4.0->autogluon) (0.21.0)\n",
      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /opt/conda/lib/python3.12/site-packages (from requests[socks]->gdown>=4.0.0->nlpaug<1.2.0,>=1.1.10->autogluon.multimodal==1.4.0->autogluon) (1.7.1)\n",
      "Downloading thinc-8.3.10-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (3.9 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.9/3.9 MB\u001b[0m \u001b[31m141.7 MB/s\u001b[0m  \u001b[33m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading transformers-4.49.0-py3-none-any.whl (10.0 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.0/10.0 MB\u001b[0m \u001b[31m235.0 MB/s\u001b[0m  \u001b[33m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading einx-0.3.0-py3-none-any.whl (102 kB)\n",
      "Downloading loguru-0.7.3-py3-none-any.whl (61 kB)\n",
      "Building wheels for collected packages: nvidia-ml-py3\n",
      "  Building wheel for nvidia-ml-py3 (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for nvidia-ml-py3: filename=nvidia_ml_py3-7.352.0-py3-none-any.whl size=19208 sha256=7dbd49d917b3f8384ca7ae3ad0f111cd2b31dd59e30194a964a53ae255e13daa\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-7ivkz6wz/wheels/6e/65/79/33dee66cba26e8204801916dfee7481bccfd22905ebb841fe5\n",
      "Successfully built nvidia-ml-py3\n",
      "Installing collected packages: nvidia-ml-py3, loguru, einx, transformers, thinc\n",
      "\u001b[2K  Attempting uninstall: transformers0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/5\u001b[0m [einx]\n",
      "\u001b[2K    Found existing installation: transformers 4.55.2━━━━━━━━━━\u001b[0m \u001b[32m2/5\u001b[0m [einx]\n",
      "\u001b[2K    Uninstalling transformers-4.55.2:[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3/5\u001b[0m [transformers]\n",
      "\u001b[2K      Successfully uninstalled transformers-4.55.2m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3/5\u001b[0m [transformers]\n",
      "\u001b[2K  Attempting uninstall: thinc━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3/5\u001b[0m [transformers]\n",
      "\u001b[2K    Found existing installation: thinc 8.3.690m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3/5\u001b[0m [transformers]\n",
      "\u001b[2K    Uninstalling thinc-8.3.6:[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3/5\u001b[0m [transformers]\n",
      "\u001b[2K      Successfully uninstalled thinc-8.3.6\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3/5\u001b[0m [transformers]\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5/5\u001b[0m [thinc]32m4/5\u001b[0m [thinc]rs]\n",
      "\u001b[1A\u001b[2KSuccessfully installed einx-0.3.0 loguru-0.7.3 nvidia-ml-py3-7.352.0 thinc-8.3.10 transformers-4.49.0\n"
     ]
    }
   ],
   "source": [
    "!pip install -U pip\n",
    "!pip install -U setuptools wheel\n",
    "!pip install -U \"mxnet<2.0.0\" bokeh==2.0.1\n",
    "!pip install autogluon --no-cache-dir\n",
    "# Without --no-cache-dir, smaller aws instances may have trouble installing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup Kaggle API Key"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download and explore dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:19:56.737734Z",
     "iopub.status.busy": "2025-12-07T15:19:56.737465Z",
     "iopub.status.idle": "2025-12-07T15:19:59.071412Z",
     "shell.execute_reply": "2025-12-07T15:19:59.070542Z",
     "shell.execute_reply.started": "2025-12-07T15:19:56.737711Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading bike-sharing-demand.zip to /home/sagemaker-user/udacity-AWS-ml-engineer-nanodegree/project\n",
      "  0%|                                                | 0.00/189k [00:00<?, ?B/s]\n",
      "100%|████████████████████████████████████████| 189k/189k [00:00<00:00, 19.9MB/s]\n",
      "Archive:  bike-sharing-demand.zip\n",
      "  inflating: sampleSubmission.csv    \n",
      "  inflating: test.csv                \n",
      "  inflating: train.csv               \n",
      "bike-sharing-demand.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
      "Archive:  bike-sharing-demand.zip\n",
      "  inflating: sampleSubmission.csv    \n",
      "  inflating: test.csv                \n",
      "  inflating: train.csv               \n"
     ]
    }
   ],
   "source": [
    "# Create a kaggle directory\n",
    "!mkdir -p ~/.kaggle # Using -p ensures no error if the directory already exists\n",
    "\n",
    "# Copy 'kaggle.json' (Kaggle API Token) to the folder we have created\n",
    "!cp kaggle.json ~/.kaggle/\n",
    "\n",
    "# Authorize permissions for the json to act\n",
    "!chmod 600 ~/.kaggle/kaggle.json\n",
    "\n",
    "# Download the Bike Sharing Demand dataset\n",
    "!kaggle competitions download -c bike-sharing-demand\n",
    "\n",
    "# Unzip Dataset\n",
    "!unzip -o bike-sharing-demand.zip\n",
    "\n",
    "# Download the dataset, it will be in a .zip file so you'll need to unzip it as well.\n",
    "!kaggle competitions download -c bike-sharing-demand\n",
    "# If you already downloaded it you can use the -o command to overwrite the file\n",
    "!unzip -o bike-sharing-demand.zip"
   ]
  },
  {
   "attachments": {
    "kaggle6.png": {
     "image/png": "iVBORw0KGgoAAAANSUhEUgAAB7oAAAT4CAYAAACG6s0GAAAK12lDQ1BJQ0MgUHJvZmlsZQAASImVlwdUU1kTgO976SEhQCAUKaF3pBNASugBFKSDqIQkkFBCTAgKdmRxBVcFERFUFnBVRMFVKbIWxIIVBUXsG2RRUdfFgg2VfcBP2N3//P9//jnnnvu9eXNn5t5z5515AJC92SJRBqwEQKYwWxwR6EOPi0+g44YABtCAIqADdTZHImKGh4cCRKbnv8v72wCamG9aT/j69/f/VVS4PAkHACgR4WSuhJOJcAcyfuOIxNkAoPYgesOl2aIJvoiwqhhJEOEHE5w6xSMTnDzJaPSkTVSEL8IaAOBJbLY4FQCSEaKn53BSET8kP4RthVyBEGHkGXhy+GwuwscQtsrMzJpgGcJmiL0IADIeYUbyX3ym/s1/stw/m50q56l9TQreTyARZbBz/8+j+d+SmSGdjmGCDBJfHBQxMSPndyc9K0TOwuR5YdMs4E7aTzJfGhQ9zRyJb8I0c9l+IfK1GfNCpzlFEMCS+8lmRU0zT+IfOc3irAh5rBSxL3Oa2eKZuNL0aLmez2PJ/efxo2KnOUcQM2+aJemRITM2vnK9WBohz58nDPSZiRsg33um5C/7FbDka7P5UUHyvbNn8ucJmTM+JXHy3Lg8P/8Zm2i5vSjbRx5LlBEut+dlBMr1kpxI+dps5HLOrA2Xn2EaOzh8mkEQCEdqLwHYA5ds3rKJOwp8s0S5YkEqP5vORKqMR2cJOTZWdHtbezsAJmp26hq8jZisRYh2akaXhdQQ4z1SJyUzuuQyAFoLAdC4N6Mz2g0ApQCAlk6OVJwzpZsoJ+RbQAQUoAo0gS4wBGbAGsnLGbgDb+APgkEYiALxYBHgAD7IBGKwFKwAa0EhKAZbwDZQCapBHdgPDoEjoBWcAGfABXAF3AB94D6QgSHwAoyA92AMgiAcRIaokCakBxlDlpA9xIA8IX8oFIqA4qEkKBUSQlJoBbQOKoZKoUqoBqqHfoaOQ2egS1APdBcagIahN9BnGAWTYFVYBzaBZ8MMmAmHwFHwQjgVXgLnwQXwJrgCroUPwi3wGfgK3AfL4BfwKAqgFFA0lD7KGsVA+aLCUAmoFJQYtQpVhCpH1aIaUe2oLtRNlAz1EvUJjUVT0XS0NdodHYSORnPQS9Cr0BvRlej96Bb0OfRN9AB6BP0NQ8ZoYywxbhgWJg6TilmKKcSUY/ZimjHnMX2YIcx7LBZLw5piXbBB2HhsGnY5diN2F7YJ24HtwQ5iR3E4nCbOEueBC8Oxcdm4QtwO3EHcaVwvbgj3Ea+A18Pb4wPwCXghPh9fjj+AP4XvxT/FjxGUCMYEN0IYgUvIJWwm7CG0E64ThghjRGWiKdGDGEVMI64lVhAbieeJD4hvFRQUDBRcFeYrCBTWKFQoHFa4qDCg8ImkQrIg+ZISSVLSJtI+UgfpLuktmUw2IXuTE8jZ5E3kevJZ8iPyR0Wqoo0iS5GruFqxSrFFsVfxFYVAMaYwKYsoeZRyylHKdcpLJYKSiZKvEltplVKV0nGlfqVRZaqynXKYcqbyRuUDypeUn6ngVExU/FW4KgUqdSpnVQapKKoh1ZfKoa6j7qGepw6pYlVNVVmqaarFqodUu1VH1FTUHNVi1JapVamdVJPRUDQTGouWQdtMO0K7TfusrqPOVOepb1BvVO9V/6AxS8Nbg6dRpNGk0afxWZOu6a+Zrlmi2ar5UAutZaE1X2up1m6t81ovZ6nOcp/FmVU068ise9qwtoV2hPZy7Trtq9qjOro6gToinR06Z3Ve6tJ0vXXTdMt0T+kO61H1PPUEemV6p/We09XoTHoGvYJ+jj6ir60fpC/Vr9Hv1h8zMDWINsg3aDJ4aEg0ZBimGJYZdhqOGOkZzTVaYdRgdM+YYMww5htvN+4y/mBiahJrst6k1eSZqYYpyzTPtMH0gRnZzMtsiVmt2S1zrDnDPN18l/kNC9jCyYJvUWVx3RK2dLYUWO6y7LHCWLlaCa1qrfqtSdZM6xzrBusBG5pNqE2+TavNq9lGsxNml8zumv3N1sk2w3aP7X07Fbtgu3y7drs39hb2HPsq+1sOZIcAh9UObQ6vHS0deY67He84UZ3mOq136nT66uziLHZudB52MXJJctnp0s9QZYQzNjIuumJcfVxXu55w/eTm7JbtdsTtD3dr93T3A+7P5pjO4c3ZM2fQw8CD7VHjIfOkeyZ5/ugp89L3YnvVej32NvTmeu/1fso0Z6YxDzJf+dj6iH2afT74uvmu9O3wQ/kF+hX5dfur+Ef7V/o/CjAISA1oCBgJdApcHtgRhAkKCSoJ6mfpsDisetZIsEvwyuBzIaSQyJDKkMehFqHi0Pa58NzguVvnPphnPE84rzUMhLHCtoY9DDcNXxL+y3zs/PD5VfOfRNhFrIjoiqRGLo48EPk+yidqc9T9aLNoaXRnDCUmMaY+5kOsX2xprCxudtzKuCvxWvGC+LYEXEJMwt6E0QX+C7YtGEp0SixMvL3QdOGyhZcWaS3KWHRyMWUxe/HRJExSbNKBpC/sMHYtezSZlbwzeYTjy9nOecH15pZxh3kevFLe0xSPlNKUZ6keqVtTh/le/HL+S4GvoFLwOi0orTrtQ3pY+r708YzYjKZMfGZS5nGhijBdeC5LN2tZVo/IUlQoki1xW7JtyYg4RLxXAkkWStqyVZHm6KrUTPqddCDHM6cq5+PSmKVHlykvEy67mmuRuyH3aV5A3k/L0cs5yztX6K9Yu2JgJXNlzSpoVfKqztWGqwtWD60JXLN/LXFt+tpr+bb5pfnv1sWuay/QKVhTMPhd4HcNhYqF4sL+9e7rq79Hfy/4vnuDw4YdG74VcYsuF9sWlxd/2cjZePkHux8qfhjflLKpe7Pz5t1bsFuEW26XeJXsL1UuzSsd3Dp3a0sZvayo7N22xdsulTuWV28nbpdul1WEVrTtMNqxZceXSn5lX5VPVdNO7Z0bdn7Yxd3Vu9t7d2O1TnVx9ecfBT/eqQmsaak1qS2vw9bl1D3ZE7On6yfGT/V7tfYW7/26T7hPtj9i/7l6l/r6A9oHNjfADdKG4YOJB28c8jvU1mjdWNNEayo+DA5LDz//Oenn20dCjnQeZRxtPGZ8bGcztbmoBWrJbRlp5bfK2uLbeo4HH+9sd29v/sXml30n9E9UnVQ7ufkU8VTBqfHTeadHO0QdL8+knhnsXNx5/2zc2Vvn5p/rPh9y/uKFgAtnu5hdpy96XDxxye3S8cuMy61XnK+0XHW62nzN6Vpzt3N3y3WX6203XG+098zpOdXr1Xvmpt/NC7dYt670zevruR19+05/Yr/sDvfOs7sZd1/fy7k3dn/NA8yDoodKD8sfaT+q/dX81yaZs+zkgN/A1ceRj+8PcgZf/Cb57ctQwRPyk/Knek/rn9k/OzEcMHzj+YLnQy9EL8ZeFv6u/PvOV2avjv3h/cfVkbiRodfi1+NvNr7VfLvvneO7ztHw0UfvM9+PfSj6qPlx/yfGp67PsZ+fji39gvtS8dX8a/u3kG8PxjPHx0VsMXuyFUAhA05JAeDNPqQnjgeAegMA4oKpnnpSoKn/gEkC/4mn+u5JcQagrh+AqOUAhF4DYEcl0sYi/inIv0A4BdG7A9jBQT7+JZIUB/spXyQvpDV5OD7+1gwAXAkAX0vGx8fqxse/1iHJ3gegI3eql58QA0UAnJwm6E5PCfinTPX5f9njP2cwkYEj+Of8JxlMFsbB3Ic3AAAAimVYSWZNTQAqAAAACAAEARoABQAAAAEAAAA+ARsABQAAAAEAAABGASgAAwAAAAEAAgAAh2kABAAAAAEAAABOAAAAAAAAAJAAAAABAAAAkAAAAAEAA5KGAAcAAAASAAAAeKACAAQAAAABAAAHuqADAAQAAAABAAAE+AAAAABBU0NJSQAAAFNjcmVlbnNob3Sp7+u0AAAACXBIWXMAABYlAAAWJQFJUiTwAAAB2GlUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iWE1QIENvcmUgNS40LjAiPgogICA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPgogICAgICA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgogICAgICAgICAgICB4bWxuczpleGlmPSJodHRwOi8vbnMuYWRvYmUuY29tL2V4aWYvMS4wLyI+CiAgICAgICAgIDxleGlmOlBpeGVsWERpbWVuc2lvbj4xOTc4PC9leGlmOlBpeGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6VXNlckNvbW1lbnQ+U2NyZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgICAgPGV4aWY6UGl4ZWxZRGltZW5zaW9uPjEyNzI8L2V4aWY6UGl4ZWxZRGltZW5zaW9uPgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4KLg8pQwAAABxpRE9UAAAAAgAAAAAAAAJ8AAAAKAAAAnwAAAJ8AAGUSMAV2gYAAEAASURBVHgB7J0HuCRF1b97yUlyXIJLzjksOSlBJSgiWUQRRP0bMOccPpXvM6LkJIgiYkZyBpEsGVbCknNOAgv/fVvPPNV1u2fmhtmd3fue59md6eqq6uq3qmvu0786p8a8PtkKTQISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJDCNEBij0D2N9JTNlIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCCBkoBCtwNBAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgASmKQIK3dNUd9lYCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhJQ6HYMSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpDANEVAoXua6i4bKwEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACCt2OAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISmKYIKHRPU91lYyUgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQKHbMSABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAtMUAYXuaaq7bKwEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCSh0OwYkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSGCaIqDQPU11l42VgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAGFbseABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlMUwQUuqep7rKxEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCSg0O0YkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCCBaYqAQvc01V02VgISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEFLodAxKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkME0RUOieprrLxkpAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgEK3Y0ACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABKYpAgrd01R32dh+J3DfffcVJ598cjFhwoRi1113Lbbeeutipplm6vdm2z4JSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJTFMEFLp73F2vvvpq8Z3vfKerq8wxxxzFYostVowdO7ZYdNFFi6WXXrqYbbbZuiprpu4I3HnnncWJJ57YXeYOuQ444ICyvyLbgw8+WKy44opxWH5+7GMfK775zW9W0np98Oc//7m49tprW5eZZZZZik9/+tPFjDPO2Eob7pcf/ehHxTPPPNOqZp111il22GGH1rFfJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJNBLAgrdvaQ7ue6XXnqpWHjhhYd0lbnmmqvYb7/9ir322qtYbbXVhlSHhaoELrzwwmLHHXesJg7x6JJLLinWWGONVulvf/vbxfe+973WcXx54IEHCvpyStnBBx9cHH300ZXLTZw4sZhvvvkqacM5WHnllYv777+/VQVC9wUXXNA69osEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEeklAobuXdCfXPRyhO23aFltsURx55JGlp3eaPjW+P/nkk8UxxxxTufQGG2xQbLbZZpW0fjzopdD9rW99q/j+978/4LbxIl9wwQUHpPcqQaG7V2StVwISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIoF8IKHT3uCcQuhdZZJERucpCCy1Uht3ecMMNR6S+oVaCcLv22mtXin/mM58pvvjFL1bS+vHgoosuGjGP7osvvrji0X3vvfcO8Lzfd999i5/+9KdTFAVCd74Q4e677x5xj2481cPw6D7//PPj0E8JSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJ9JSAQndP8f7Ho7tO6N5mm20qV37xxReL6667rnjuuecq6XUHp5xySrHddtvVnZoiadOb0L3ccssVa6655qDZfeUrXynGjRtXKXf99dcXv/zlL4tbb7212GOPPYp3vOMdBXuvT0lT6J6StL2WBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpDA1CCg0N1j6qlH95gxY8qrIaoSQrvOnnjiiWLChAkFgunXv/71WuGb/Z6vueaaIe/9XXfdwaQhdOPBmxoe3V/4whfSpL78jkf3TjvtVGnbxz/+8eJrX/taJW1aPvjEJz4xwKP7rrvuGlGP7lVWWaXIPbrPO++8aRmbbZeABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpiGCCh097izBit0p8156KGHio997GPFmWeemSaX37fffvvi5JNPLkI8H5ChhwkK3T2EOwJVK3SPAESrkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAIS6GsCCt097h6E7kUXXbRylXYe3ZWMkw9ef/314ic/+Umtx/Hvfve7Yuutt86L1B7Tjquvvrq4//77S0/c559/vtw7fLHFFivwzl166aVry0XiI488Utx2223lIZ68Bx10UJwqP3fbbbdin332qaRtuummHYX4xx57rPRep87wEIbX2LFjS6/x+eefv1LncA/w6N55550r1eDR/dWvfrWSNpSDf//738UVV1xRKbrGGmsU88wzTyWt6eCVV14prr322uK+++4r7rnnnuKFF14ox87iiy9ejB8/vph33nmbilbSEbqPPfbYShqLE+abb75KWt0B44R7mDhxYtmOGWaYoQzrvv766xcLLLBAq8iqq67a6i8S8fA/99xzW+fbfSFiAff56KOPFkQwmHvuuYsll1yyWGKJJco9zqd0qPd2bfWcBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpBAfxJQ6O5xvwxX6KZ5kyZNKrbYYovipptuqrR2r732Kg499NBKWn5w8803l2Gsf/Ob39SGQY/8W221VfG+972veMtb3lLMOOOMkdz6/OMf/1jst99+reNuviBkzjTTTLVZzznnnOL4448v/vKXv9Sej0TukXatu+66kTSsz14K3Qj1CMCpdSOiP/XUU8UJJ5xQ/OxnPyvF37R8+p39vqkP8bydDUXofvbZZ4sjjjii+NGPftQ4TnbYYYdyvCFMD0XoJrT5Jz/5yeLuu+9ubD5h+Wn/e9/73q6F/cbKPCEBCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJDDdElDo7nHX4uVb59F9wQUXDOrK7OmN0Jkb4upss82WJ5fHePUiLA7GPvCBDxTf/e53BxT505/+NGihGy/wXOhG+P/85z9fitwDLtIm4bjjjhuwt3ab7I2nELrf/va3V84THn4kPLrpi9VWW61S94c//OHim9/8ZiUtPbj99tuLXXbZpeIdnZ6v+/6tb32r9KjH27rO6PPco/uOO+5o9Oh+8MEHCzzy84UUdXUvu+yyxe9///uC0PnhgU++tddeu9Gj+9VXXy0OOeSQ4vvf/35dlbVpePSzGCJ/dmozmygBCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJDDqCCh097jLR0roppkIorlAftJJJ5Ve2PltICr+z//8T57c1fEXvvCF4lOf+lQl70gI3a+99lp5D4jNQzEEVjzbh2P9JHRfddVVJY/nnntu0Lf02c9+tuBfnQ1G6GYxwmabbdbWkzy/xkYbbVT861//qpRpEroRuXfddddiKH2OqH722Wfr2Z13gMcSkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAKFQnePBwFCN/tgp8Ye3eeff36a1NX3ww8/vECETu0rX/lKgUdyarfeemuxySabpEmt73vuuWex1lprFXPOOWe5D/QxxxxTsE92bmeeeWax3nrrtZIffvjhMmw1YjWhp//2t7+1zvEFoXPDDTespH3961+vhEE/8cQTB7SVAoSrJlR17BOOl/Nhhx1WqSvycW+zzz77gHPdJlx88cW1Ht1wHK7h4bz66qtXqsGj+xvf+EYljQPEbQTj1Cs6MiE8038zzzxzccsttxRnnHFGbTjxU089tSDkfG4sUsg9uhGm6/boxoOfeuqMMPbjxo0r7rrrruKSSy6pbUOUo//xwM6NfeQPPPDAPLkMv86e7uw//swzzxSXXnppwfjI7Utf+lJx8MEH58keS0ACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJjHICCt09HgAI3YRhTo09locidCN47r333mlVZQjrb3/725U0QnMj6KZGG/76178WSy21VJpcvPDCC6XInIuU73//+4vvfe97lbxxgPCZiuCkI64SkrzJEDMRgXPvZUJgszc0wntq9913X/GmN71pgAiPgLvTTjulWQf1vV+E7s997nPFkUceOaDtp59+ejF+/PhK+uOPP16KvfRfaojQeIWPGTMmTS77ohuhm3D4RAnIDaGdvd8RocNef/314n//939rw9qTp0no3n333QcI4Nz7pz/96ai69ckzgfd3agsuuGBxww03FLPMMkua7HcJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIFRTkChu8cDYCSFbvZQzkN377zzzsXRRx9duQs8iC+77LJSBI0TiJqrrrpqHFY+EZXx8k4N4ZKw0XWG0L3++utXTiF0I2A22UMPPVTu04zQzH7RWISmnnvuuWuLEar8gAMOqJzDu/eLX/xiJW0wB1y/bq9zBNVubYEFFig9kPP8eGeziCG1D33oQwM8up988sli+eWXT7OV3xGyc5E7MiE0Ixqfd955kdRYhr5gT/PUJkyYMMCjG29uPK5Tw7v+iiuuKBZeeOE0ufX9+OOPr933vWm8wCP1Wqd+xk8uzscF8OAOb37qJPoBe5wPx4s/6vZTAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhg+iGg0N3jvkToTj1juRziXy5YdtMMvKKXWWaZSlaE0dzTNzK8+OKLxfXXX196wyIatrNckET4JUx4nQ1F6E7reeKJJ4prr722FHtzD/M03z333FOss846aVLp0f7jH/+4kjaYgyahezB1INYSvj23boXuk08+ufjIRz5SKc495d76lQyTDwg/noeH32+//coFBGneboTuunFJHbRtm222SaurfCd0PSHXY7FCnGwSuldaaaWKVz7sCE3f5KFNu1gUscQSS1TC3sd1/JSABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpAABBS6ezwO6gTFoQrdL7/8cq1ofu655w7rLhAW872lqfDRRx+trRehe4MNNqic++QnP9nWo7uSucsD9gHfd999K7l32GGHAftPVzJ0OEDorgvX3aFY5XR4JVcSJx8gdOOBnBoe3exVntpee+01wFuevbi78Srfdttty0UCUR9jKe9/woLnHt2Iy+ke3Xj8Ew0gNUT0P//5z2lS7XfE8I9+9KOVcwjdZ511ViWNg3e9613FBRdcUEl/5zvfWXznO98p5p9//kq6BxKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhLoloBCd7ekhpgPoRvv1NTqxMn0fNN3vIgJGZ6Gfcb79qSTTmoqMiD9kUceKQhVfv/995f/rr766lIozffOpiB56wyhOw+xjdD92c9+ti57x7RJkya12kO77r333uKSSy4ZsM84FSF0H3PMMR3rbMqA0I3QOhxD6L7zzjsHVIHQnYeAR+j+2te+VslLH06cOLGVRn0nnnhi67jdFzin3tSI4zfffHOlCEI3IcZTu+222ypC92mnnVbu757mYV92BOhORh/lEQI4PvPMMwcUZV/vpr3eEdoZv3jtE8Y+HdcDKjJBAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAgkBhe4ERi++jqTQXSfS4vF8yCGHNDYdUfrUU08t911G1K4TtJsK90roZr/pq666qkBsve666wra1a31Quimzk5hw9P2EXZ7s802S5PK790I3dz7IossMqDscBK47kwzzdSqohuh+/DDDy++/OUvt8rw5Qc/+EHxnve8p5JWd/Dqq68WY8eOrZxqErqff/75YpNNNqns010p+N8DxP7NN9+8XMjw5je/uZh33nnrspkmAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggZKAQnePBwJC95JLLlm5Ch7d55xzTiWtm4Nf/epXxcEHH1zJiqjJnsy5sZ8z5whRPVR7+OGHa4sinud7RX/iE5/oyqP77LPPLr761a9WvJJrL9KQiCh99NFHN5ztnIyneO7RzX7ZX/rSlzoX7pADwTn3dP7gBz9Y8eh+6qmnihVXXLFDTYM7jUf3Agss0Cr0mc98ptajOxWP8dzO9zr/3e9+V2y66aatetp9WXXVVSt7b+PJXufRTR033HBDsf/++1e82NvVzTkE989//vMVL/ROZTwvAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQweggodPe4rxG6l1pqqcpVELoRfAdrX/ziF4ujjjqqUuyHP/xhwZ7PqbEfM/tQP/bYY2ly43dESjyrc2Pv7jpD6N5oo40qpxC6EVjbGfs/H3DAAe2yVM5ttdVWxfnnn19JQ+jOGVQydDhA6N51110ruf7f//t/IyJ0P/jgg7VCN8J+2NNPPz3iQveNN95Y2d+bEPJ56PJbb7214iWN9zZhxVMjfDre1N3YcsstV4kOwBg644wzGovi2Y2wzmKNbsclYdlPP/30Ac9P40U8IQEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkMGoIKHT3uKtHSuiuE1Fp+jXXXFMJI01YaUTHJjFx4403LsVM9kReeumly38zzzxzsfvuuxcXXnhhhcZICt233HJLgXDdZDvuuGPpTfzGN76x3K859jVfbLHFKkWmdaGb0OX5PRG2mwULQ7Vtt922mHXWWVvFuxG6jz322NJjulVo8hcEeTzQO9mLL75Yjps0XyehO/K+9tprZaj6iy66qAxfny9kiHzxucIKK5SLHWacccZI8lMCEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEAChUJ3jwcBQjfibWqrr776oD26P/nJT5besGk948ePL/7whz+kScVNN91U65X7f//3f8V2221XzD///JX8cUA47zzMOeJ6neHRjWCeGiHV23l0I6x+4QtfSIuUXAihTV2zzTZb5RwHiPZ52Pe3ve1tw/bofte73lW5Fh7deMsP1+C1zjrrVKo56KCDSgE5TSTs+8SJE1tJjI/LL7+8dTzcLwjdJ5xwQqUaFhqkocvxrj/wwAMreVjs8KMf/aiSVndAWPx8j3KE7r/97W912dumTZo0qbjtttuK3/zmN8URRxxRm/ekk04qtt5669pzJkpAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCYxOAgrdPe53hO5x48ZVroLQfdZZZ1XS2h2wB/Ob3vSmAVm+//3vF+9+97sr6Qicn/vc5yppCIh4QjfZK6+8MkCMJy97TtfZ3XffXSt0syd4kyH4/ulPf6qcxht90UUXraSlB9dee22BsJ0ax0ceeWSaNKjvhC7fbbfdKmU+/OEPj5jQve6661bq5r6/8pWvVNL22Wef4rzzzqukEVp87rnnrqQN9YD+z4VuxlAqdNexXWaZZUqv/k7e0yyaOOSQQyrNQ+gmzPhwjLDu22+/fWURAPVxPx/96EeHU7VlJSABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEpjMCCt097tDhCt1//etfC7yln3vuuUpL2b/44osvLuaZZ55Kep3IyR7OTZ7cFL7qqquKnXbaqVIPB4MRuvEO/trXvjagjkhgT+/Ui5mQ1BdccEGcrv382c9+VuDxndr0IHTjvUyfpvahD32o4z7heLizuGHOOecsVl555QKGeLznwnTdGMiFbjypqSMfV4jyiPNNRtjyNddcc0C5OqH72WefLe68886ChRF33HFH+fnNb35zwJhNr3XMMccM4LD//vsXlNMkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkEAQUuoNEjz4RutkLOzU8us8888w0acD3J598shT8fv/731fOjRkzpjz+9a9/PSB8NCcQhg899NBKGa612mqrVdLiADESr/Arr7wyklqf999/f+t7+uWee+4pEK5T23zzzYuTTz45Tap8x1P3hhtuaKWxLzVh1meaaaZWWvrl9ttvLwgxnu81jtDdFOI6Ld/0/dJLL6316M7DqjeVb5dO6PL11luvkuUDH/jAAI/up556qlh11VUr+ThgUQOCcZN961vfKn7xi19UTtMPp556aiUNofuXv/xlJQ3WqUc3J7nn448/vpKPg7PPPrtYZZVVBqQjtH/pS18aUDcZaTftT40w6D/4wQ/SpDKMex4yPc3w85//vPj2t7+dJhVf/vKX24rvlcweSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJjAoCCt097maEbkJCp8aezN/73vfSpOKZZ54pvV4ReAljzZ7KTXbAAQc0ek+feOKJA0KXI6wjjOfe348//nix7777Fv/85z9rL3XffffVpr/00kvFcsstN+AcAiue4YQjx1M49SL/4Ac/WLAvdGpc+xvf+MYAsfv6668vxejc25iyb33rW4ctdLMXdWp4U4+U0L3++uunVRcI3Qi1ueH9ftRRR+XJZQh1+jddAEBI79NOO622np/85CfFLrvsUqnn85///AAxGq/+XOjGy3vbbbetlOVgqaWWKgXqTTbZpHXu0UcfLdvWFJ4cL+9c6MaLe4sttmjVEV8IPf+Wt7wlDsvP119/vSxf503OHt119VQq8EACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSGBUEVDo7nF31wndw7kk3teIw7PMMkttNQ8//HCR7xMdGRGWF1pooVJUv/zyyyse1pEn/WwSusmzzTbbtBXjEXxTb/QzzjijeP/7359WX35fbLHFSqF2ttlmK0Ol43GNx3iTTS9CNyHA2Xe97l7xdl9nnXWKxRdfvECgTj3hUy4I0hdddFFFFOd8t0I3eQkJfvjhh/N1gFE/+8tPmDChwFu9ndUJ3eR/xzveURstgMUXm266abn4Aq997oNFHrkRXp2IBDPMMEN+ymMJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIFRTEChu8edj9C97LLLjshVCAW96667dqzrkEMOKX784x93zNcpw7333tuY5S9/+UuBl3Y7w6M3BHnCXuNJfcUVV7QrUjmH4Jt7dSN0NwmzlcINBwjpe+yxR+UsHt2Iw8M1xOANNtigUg1huus8usmEiM3ChTw8e6WChgP2aCdU/EorrTQgB97peehyxPLco5uC8N1tt90axfQBlU9O2HHHHQvC2l9zzTWt0wjdjIncGAOM2aHcI/1/yimnFIjimgQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgARSAgrdKY0efEforgvz3e2lEPv23HPPYu+99x6UYM7ey00Ca35t9n4mfPlvf/vbyqk6b+M0A+G0P/7xj6dJle8In2ussUYrDRaf/exnyzDcrcSGL9w3Yi1e4IRYD0PoPuyww+Jw0J+XXXbZAKEbwX6khO7x48dX2oTQzb7WTfbQQw8VhCpvCh9fVw5Pa0LU421dZwjdnE+NcPB1Qjd5ELthcOGFF6ZFar9vtdVWxU9/+tMy5H0udOeh6aMCPMIR09N+jHNNn9wj+5ErcjcRMl0CEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJjG4CCt097n88mZdeeunyKmPGjGl7NcRdxMuxY8eW/zbaaKMyvHV4RbctXHPy/PPPL/daxnO4zgiP/elPf7pgL+avfvWrxbHHHlvJ1knoJjNiJ/uN//3vf6+U5QBBGmE6NfZiJp19l5vqZ5/vT33qUyWLLbfcsrjzzjtbVQxX6EZQxiM5tZESup966qmKsM81Ognd5HnllVeKs88+u2BxQh1H8mB4TbOH9XbbbTcgXPl/cvzn/+9+97ulSJymtRO6ycc4Zf/tQw89tDYk/QILLFCK2x/5yEfKa7/nPe8pGF9htK1J6CYPodr/8Ic/lN74aX9G+fhcbbXVSg/wffbZpxUNIM75KQEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIEgoNAdJKbjz5deeqm47bbbSsEYr2r26V5yySWLFVZYYcTuetKkScXEiROLRx55pNx3GbF+nnnmaVv/008/Xdx8880Fe4EjgC+66KLF8ssvX7Bv92i1Rx99tNyrHE9v+mrWWWdtLXxAbO610Q+ErKdPnnnmmVJsXnXVVYtFFllkRC792muvFdwjYd65xyeffLKYe+65i/nnn79cEMIY0CQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCTQiYBCdydCnpeABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggb4ioNDdV91hYyQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIoBMBhe5OhDwvAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAJ9RUChu6+6w8ZIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkEAnAgrdnQh5XgISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAE+oqAQndfdYeNkYAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCCBTgQUujsR8rwEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCfQVAYXuvuoOGyMBCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAp0IKHR3IuR5CUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhLoKwIK3X3VHTZGAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQ6EVDo7kTI8xKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQk0FcEFLr7qjtsjAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJdCKg0N2JkOclIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSKCvCCh091V32BgJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEuhEQKG7EyHPS0ACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpBAXxFQ6O6r7rAxEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCTQiYBCdydCnpeABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggb4ioNDdV91hYyQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIoBMBhe5OhDwvAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAJ9RUChu6+6w8ZIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkEAnAgrdnQh5XgISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAE+oqAQndfdYeNkYAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCCBTgQUujsR8rwEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCfQVAYXuvuoOGyMBCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAp0IKHR3IuR5CUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhLoKwIK3X3VHTZGAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQ6EVDo7kTI8xKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQk0FcEFLr7qjtsjAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJdCKg0N2JkOclIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSKCvCCh091V32BgJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEuhEQKG7EyHPS0ACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpBAXxFQ6O6r7rAxEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCTQiYBCdydCnpeABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggb4ioNDdV91hYyQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIoBMBhe5OhDwvAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAJ9RUChu6+6w8ZIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkEAnAgrdnQh5XgISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAE+oqAQndfdYeNkYAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCCBTgQUujsR8rwEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCfQVAYXuvuoOGyMBCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAp0IKHR3IuR5CUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhLoKwIK3X3VHTZGAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQ6EVDo7kTI8xKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQk0FcEFLr7qjtsjAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJdCKg0N2JkOclIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSKCvCCh091V32BgJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEuhEQKG7EyHPS0ACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpBAXxFQ6O6r7rAxEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCTQiYBCdydCnpeABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggb4ioNDdV90xehrz6quvFnfccUd5w8suu2wx00wzjZ6b904lIIEhE5g0aVLxwAMPFAsvvHAx66yzDrkeC0pAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJDBtE1Do7pP+u+yyy4rHHnusbM0aa6xRjBs3rk9a1ptm/PGPfyxuv/32svKVVlqp2HHHHXtzoemo1rvvvru44YYbyjvaeuutiznnnHM6uruBt/KPf/yjeOSRR4q55pqr2GqrrQZmmMop//73v4uzzjqrbAWLNVZZZZWp3KLp//KvvfZacfTRRxdPPfVUebMf+tCHpvvnYPrvVe9QAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJDA0AgrdQ+M24qUQb5544omy3m222aZYa621RvQazz33XPH000+Xdc4xxxzFfPPNN6L1p5VxHy+99FKZNO+88xZcLzXEqh/+8IdpUnHwwQcXM8wwQyXNgyoBhN9LLrmkTNx///0L2E7PdsoppxT33ntvOX4++MEP9t2tvvDCC8UvfvGLsl3rr79+sfnmm49oG3lmn3nmmbJOvJdHQ9SDxx9/vGABwZgxY4rFFltsAE/OH3fcca30t73tbQULZUaLdeIzWjh4nxKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCCh098k4OOaYYypC95prrjmiLTv77LNb3sB4nm6//fYjWn9a2RFHHFEg0mHbbbddseqqq6any+/nnntu8c9//rP8jgf7m9/85gF5TKgSQOi+9NJLy8T3ve99073Q/dvf/rYUumefffaiX4Xuww47rOwPhO7NNtus2mHDPLr66quLCy+8sKzlPe95T7HAAgsMs8b+L/773/++uOuuu4qZZ565+MhHPjKgwa+//nrxu9/9rrjnnnuKeeaZp9h7772L2WabbUC+6TWhE5/p9b69LwlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpBAHQGF7joqUyENoTvC8b7pTW8qeil0IzwjQPfKjjrqqOLZZ58tq+c6TSGdCUuN4a2qdSZwxRVXtITu9773vdO90H3qqae2hO6DDjqoM6ApnOPFF18sQuheb731eiJ0X3TRReVd7bvvvqNC6P7DH/5QCt2zzDJL8eEPf7ixR59//vnS0x/P79Fk3fIZTUy8VwlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEhi9BBS6+6Tvjz322IrQjZfzSNo555xT3HjjjWWVCM/bbrvtSFZfqQuhOzy6uU6T0F0p5EFHAgjd7OWO7bffftO90I3nboQuP/DAAzvymdIZELoPP/zw8rII3ZtuuumINuGaa64pQuh+97vfPSqE7j/+8Y8toZv9t7UqAflUeXgkAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACo5uAQvcU6v9XX321uP3220vhjn138VhcfPHFixVXXLF4wxveUO47Gx7dW2+9ddEkdD/88MPFv/71r1IUZ49gwvayV/Pyyy9fLLroogPuhnDX7Hl76623FuTHuB754/vaa69dfk//Yx/tu+++uwwRjHf2jDPOWCy00ELF2LFjSw9sQgunxjW4Fnbttde2TrHPbrRrySWXLJZeeuny3B133FHcf//95XfOr7DCCq0y6blx48YVlOO+ET3vu+++8n7f+MY3FksssUTJkYIvv/xy8cADD5R1PvbYY6W35yKLLFKGTaft7Yz7gw97i1PPnHPOWd4nbSc8cr/YlVdeWRG62fv8wQcfLO8b73j2cF5wwQXLPYvnnnvujs2mzIQJE4onn3yy3FOdcRHlYVBneNISUhujz6Jv87yEn6a/MATg2H+d0NOMEzgz/hkP9CmhqPlkXL3zne8sy5122mllHYQuD6Gbcpdffnl5nnHf9JyQIW0r/TiUKAks2Lj55psLxhTf55prrmKppZYqGb/yyisFYfoxhO5NNtmk/J7/RznGF8/3008/Xd4j/bPccsuVDHKv5BtuuKHsk4ceeqjsX+rjeeXa2LLLLluWKw+S/2gP1+FZiTmG/uT5gXW/GnPN3//+92LSpEnleIxFMum8tO6665bPJfdwyy23FI8++mj57G+44Yat28rH1jLLLFPAkHHFP8YAfQePWWedtSzHvMW8wT/y0i+MaaJexJhtXSD7QjtpC/tm8z2eH/qHsdnOuOeJEyeW90F/8fvAtZlz+E1I56zB8kmvyz3xLDLuaCO/F4wJnt35558/zdr6HnyZA+DO/MCzzDMKE+ZeOHK/GO2LeYg5nd82rrH66qsXPLu5kZd5B9t4443L/dg5Zj6iXylD21gg1c08ltfvsQQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCYweAgrdU6CveXmPaIcHaG4Ie+94xzsK9tBGUMDqhG5EitNPP70UZHJhLOpE+N1pp51KIS3SjjvuuFLkiOP8E2Flt912qyQjEOJNG8J75eTkA9q8zTbblEJdnKN9hF9vZ+PHjy/4h3G/CCrYaqutVt5zeTD5v/TcRhttVCBghbgZefhkz+Ldd9+9QOA75ZRTau8TweTtb397SyRMy/Mdr9lLLrkkT24dI1oRSr4f9gFG6EYQxPbaa6+Cfc4RyeqM/ll55ZXrTpU8YYwo2mRN5REECSmONeXh3HnnndeKIEAI6hDu6Muf/vSnZClFNMTN6667rjzmP8ZW7MfNM4NAifB1wAEHlHkQ1Y4++ujWs/SBD3ygJVq2Kvnvl+uvv7644IILyiPGEftoD8ZYsHHxxRfXFmHRB/f/q1/9qjyPIJgL3YiX7Kkee9HXVcTzx/PPIoUw9iZHDGyyLbfccoDAj6DJ/s08C3WGUL755pu3xOK6PINJQ/g/44wzyiJsTwCPoRqcfv7zn7ctznhHPMXqxgXp+dhCzI4ICJwPY05gzoMVYxkRODfy7LLLLuWCmfwcx+nYqjvPPMeYSPs18jFX/u1vf2vsYxY0cL8x5wyWD9dhoQNjn8VKTbbOOuvURiEIvvQpY4bfgjpjf/T55puv/F268847B2ThWd55553LRUPpyXQeY+955iLmlTpjIQPPbdNvXl0Z0yQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSGD0EFDo7nFfI14ff/zx5VWaXtbjAYeFSLXVVluV3nBl4uT/8Do84YQTWuJepNd94m234447trwRuXadkBNl8W5EaAvDYxbxByGrqb2R9y1veUtL7Ea8Ifx6O0NsxPMVI5R6KnRzz2HpOQQnPK2bDM9ERB2EviZD5Nt+++0HnEboRWTpZAg+u+66a61o1ansSJ6/6qqrWkI3AhNji7GD5zqeloij9F8Y4a7rPEsR9lOve7z08d7EMzVljUiF52ZqCFIhfL35zW9uFNMRum+66aayKCGoU6H7Zz/7WZmOgB2LP7gP+pr7CI9uhNsQut///ve3moHYDwuM0Ph4wNYZgnGMC0K9hwdqXd48jQUQiNSpwQm+PBtY9AHfETXxTk0NkTwV8TnH8wkL7iv6iqgFPLNhiLP0bfwjPY1esNZaa1U8tBn/J598cqu+yM8iglQwZxwj8HbyVI52tPvEw5f9ojGeDQT7oRqLFxB+sZQLHtkY8xDzQ3gHN40LhO66scV9IxpHhAHqpB+I5MB4p16eIcZ29Al5WCjCGM8N8fjPf/5zK5mxy/1HfXGCftpss83isPxkjmdBUHod5mDGJt7QMVenc9Zg+dDvjH0WWIXRRu75pZdeqnAg2kLqOU/+4Mt3ytFWxHcWGqSMSOO+8cYOBoxFxm0YXvSI9qngn85jcI/FOjz/RKlgDKTGbwa/HZoEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIICeg0J0TGcFjhBdEwdRbjVDNCHMLL7xw6TGN6JiKi1w+F7ovmOyZRzjjMDygEUJCJMC7kHDfYXvssUfLwxIhEaGE60S4WEImhwCDQIHnHUa+v/zlL2WI2qgLIQQhBiEU0ZC2IGqHve997yvFCe41QqP/+te/bgmYlKetGIJSiGyI2eFRTJjgXOiOc3EdvFi5X0QQPGTTNpAH7248arkGQhRMQkTlPJ6DqdBJPSHUcZ57JAw2wg1iEPuZpyIlQubUFlsQiFLPdkIDI65G/9FuvLwJVYyxCCDfix0use8zIjgCaxqeHXaMgTDGUnjSkoZwmgrdK620UmStfJ5//vktoRsP7VToPvTQQ1t56S/ERES43Ogf+ok8+++/f+s0z0t4UiMS77DDDq1z8QXBjcUhGGG708Uckafpk0gGJ554Yus0Xq08sxHumrr/9Kc/VSIe4B2bCt2EpU7F0Le+9a3looEQ/HhWEBRDFHzve987wNua8RfRBhALEQJzYxEMomZEX+C5JuRzjAmiM7CYI8RDnpFc2Mzr7OYY0Zj9ojEWJgxH6E6vx9hjDNJ+vPXrrGlcMAflY4u2xWIPGDF203mBZz5C6zP/wYm+DWOBRXhWk4Z4/Jvf/CZOlx7LjK+Y15gj8XSP+YlFAGl4f+bpEPW5NnNK9BXXZ15kiwsMT/mYO+OC3fBhIUhsL0Dobxb5IPbHwiVChBMBA+P8vvvuG9WXn8E3Et/2tre1tpxgvJ155pmV3wgWgHANRGqMBTMwiLHN88lzGpbPYwjmLKph4QiGsM72HCyWCSNyB/egSUACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgARSAgrdKY0R/o4ogugbhpjKC/0QHEhHOMCDGvEu0rfYYouKRzeCF/uj8g9hMfcyRLhJw4YT+jwPW43wyF7DGHUQjju32267rRRaIp0w6LQ5Na6FCBgeiYhE+d7HeJGH0MN16sRQBNkQsxG6EbLD0nOk5e3A8zEVqRHr8V5OBSm8BCPENnUQvjz2KUYQQwSNNuK1jHd6iJDkxxBkYt9xroGoH4Ltf3JM2f8Rr0LoRlzinmLMREvSsYDAR3jhMMbaUUcdFYdl2PdUxI4TjLfwZoZN6m2M0E1oY6ypbznHgojw6D7ooINa3GCfhqlGCGQBQ50hpIbQDfvUEOrCY5VziOGppffA89Lk9Z2Wie+IyxFunEUlPI+5pZw5h9CdLoRAYEas45likQQh8HND0P3rX/9aJiMmpmIgiek97LnnnrVCdzpGEbHxIs4NL+Jf/vKXLYGXhQchzOZ5uz1G6A5BGDE5FXO7raMuHzzgwvMW4erzfE3jIh9biKP5+E7nOJ4PuOYsrrjiioLw2hge8KnAyuKEWLjE4gmew9zw+A8xPH9+0rmtbtzyjF544YUlTxZFpYtzuE43fBCaEctZbMG8li5kibYSNSCiOiDmxyIOzgdfvrPIgwU1qeF1ni4EYe7N99JOxweLLxD1w9J5jDSE9vw+SWfuZXxjTb9Z5Un/k4AEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggVFLQKG7h12PWBJCKZep89okHU/rs846i6+lIawhsOWGxx/CZi5ukg9BPTzDESbCYzvqQHhMhW7E8NwQURBHMITy1Ms6zYsAh1CCIXCxl21qqYjMdeqEbrz1EJ0wPFBTMTE9h1D1rne9K62+3IsX4Q4xEaP+vK2IXocddlirHO0IsROPxvBKJsM+++xTK7SwN+5xxx3XCimP4EsI6W6MsuHh3pQfT85coG3KSzoCESIchqd2nXjKObw+I0wzInOMF4TXCNXOvrcRRp4yqcEuwteTTtjvaCdCdywySJmm5fnOeIvQ9AceeGBF6I5+qevbtB6EVBY1cG3akBoe97F/Nosk8oUdCOEIfhje4Ain3Rj3zkINhGz6h+vmCyCiHjxneRYwvKTZT3gwlo7DDTbYoBTE0/KI7fGcIdrWeXQTshxPZRgRtSD6Oq2H70SECO9wxN3wcs7zdXvMAoTwWEfwHSmh+/TTTy/nINin4erTdjWNi/SZJwJF7qlMHYjQeMBjzLH5PEk6XtcxHyMUxwIE5puIEoCndb7giLJheDQTWSEfu0RTiAUgzHnMfYOxbvh0Ux+RHWIxCwtmUo/84Es9TXPjL37xi/IysVggv2bKCpE73b8+ncfq5u6oi987BHXqYjywMCBflBB5/ZSABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIHRSUChu4f9TohXxEWMUOW5YBuXRhQ9/PDDWyIVXnR1QneaH686PJLZFxYPQ64TIledeJAKjwi+dUI33r6xTzhe1rFHblw3PhE7w9MuF3LIgwgd3tJNYige5uHRnQvd6bm6e+EaqRhTJxKS56STTir37+Y7Qjh1YQj+eE2GpR7LkRaftKXTvUTe9JN7o2w7w5M5XyTQLj/7RsfCCTxNCddeZ6mYhliISIThDR5enEQWqPNGjfqa8nYrdMM3FlY0Cd2EVq+LLBBtiD6uG2MI0cdNXoSA4amP138YIZMjkkLT+Im8+Sfh32OveRY1tBsbPHcRvrsboRtvXZ5XxhNtZIFLiPF4fTOOU+skdBNV4eijjy6L0NZUTEzr4TsLBkLoJox6Xaj4vEwcI2rnizZYVBNjiYgOuec0YahTT+ioq9NnCLksTEjD1aflmsZFKnQ3CdHp2GiaN4icEZ72hA+PeTBNp7/YAqLJeFZjqwgE2vCYTkV0ytIPzLU8i/GcNtVJejd86sojFsfYQ+xnfuIZwggtnkbuCL7t+oBxx/jLn724djo2WXhF5I+wdB5LFxLE+fSTBQexLUeT6J7m97sEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQmMLgIK3T3sb8LcIgxi7TykOY9oF8IDXoa50I1QQThavKBj71PKpZYK3WkocPIgPIaHLUJ37gE9adKk4sgjj0yrawnnlcSaA0IMp5524YVHVtoRntRpUUTg2IsWNoj7Ye3ORR68SSOEMN7JhI7ODW9X9lPG8J4MoRuPwhDq8zLtjhHGut3fGCEpFdPr6kUIHKzQHSGV8crNwwXHNfDUxOMZQ2QLAQ1P+RDf8HZFQG4y8sUeuXiuhvc4exCHuNvUt9SJ2B7jDbE9Qr4jRh5xxBHlZRG683GYtif6mJD0eCvnxkISwlxjiGB48WJp/yLiRcj68mSH//COjrDTsYdyUxHGFmMMI2T4+PHjK1m5VwRmWCKSIqI3GeMqF7rxusVrHGNhQ+xhHHWw2CUE/Ujr5pMQ62ko6U5lEFcjQkCnvHEecXibbbaJw64/2b8aVuHBW1ewaVx0M7aYO2N/aiIaIFjnxr1yzxiREwghjjH3smBosMYzHqI/bSSqAt7eubEYCm48a+xbXWfd8KEci6d4Nhh7/AbFAqa6Ogmbn0aqaOKblmWrDOpEoK9bDILQHQtG+C1LF2EgdMc8Rtj7fJFEep30WSZyAIw0CUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkEAQUOgOEj34JPxz7COM1+PGG2/ceJUIQUwGRF+8/MLw+iRcdJNYEUIm4gaGoJuGAictFR7rBEa8TcNDlvxYCOf/OWr+HzE1De+MJzXCPIYYyvVyQzBKhe40hHC7c1EP4blD6EawahK68WLEYBpCN97KiIiDNa7RFO47rwsRFM/JdoaX52BEWDxoQyDaY4892grdER6ZcPkxPhCGIzQ94nO6OCFvJ2IfohqWskPoxuMTa+pbzjHewmMfz9xU6I4FFYwL6miy6GOE7row1IiFEYqdZwtBDSER8Zd+pxz7B3c7jmkHz1qEtW8aV9FevJxjr2Ke71ToJuwy4zgiOkSZ+GRPYhYqIIRjCN0s2EiNMRp7shMNIhe607am5Tp9R9ytE3ibyhGGG/F5MIZg2y60d1NdcS3GLGO3zprGBX3faWwhdEfo8qb+Tcc+Yn0I3SweiVDyde1qSmOxRRo9gXYimlNfbDeRl8WTvM7rvhs+jEsYsWijzhCW+S1hoQSGh38qdDfxTetCxKYOQp43Cd3xe8Jzmf72pfPYrrvkZ2wVAABAAElEQVTuWhuSP66VMuc6aYj1yOOnBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJjF4CCt097HuEwhAWl1pqqVJQqLscwkd4uXKeMK8hdHOOUOCpN2h4/uElOM888xRzzDFHKaqFcIwHdS50s59xeNjWCYypSEQbEMJS8YO0OkPAzD3yfvWrX7WEbtpRJ3Tj7RztxaM7DW3b7ly0gdDCqdBd52kdgidlEGvDs5yQ0LHXNWIoXpvdGKzJP7UMgSg80dmzucmjGzEuhG72mA6hm9DVMQYQTtvt08yeziGyIroxfjGEbjw+saa+5RwCNB6lGAshUqGbEPkY4yIfp+WJ//4XfRyCdXqO70Qh4NlAcONZYK9hFhcQSQFjYcJgBF3KsECD8Ysh1rYLrc4iltivHKE79chO+4q6Ijw1+2zjeQ4PBEkWhWCM33wRRdoHCIK50J22FZZ1z0BZefYfCywilHZ2qvaQRTD8S41xEBELYJTPASx8YV4arLEYIzy6Gbt11jQumMM6jS3E3xC6GRt1C2QQuhGUMcT6ELoJoR1RDvBQ7mZ+pA44pAuBSAtjQQx7tcOTRRHpYqbtt9++ElKcMoPhQ36efcYmvxk877SFhR933nlnce6555KlIHx4ei9NfMvM//0PETuEboT83PDoZq97jLGfC90xjyGyt1vsk85ZeH/X7VOfX9tjCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISGD0EFLp72NeIy7FPMWIdXql13qV49eH9HYboy77VGMJMhNrlGPEjREeOwxDV2UsXQ9BF2E2NtoSHbZPAmArUCBMhtqf1dPMd7/Tw6KYdTUJ3hNHG0zoXupvOxfURYyIsPIJVnchHCOrw6MZjPIRuBKt0/2w8juv6Ja7VL5/XXXddS+gmlHU7oTvGHSG/Q+hOyyPgjRs3rvHW8MiOhQgIyCFkIsqFRzfCbl0IbARHBNxYnIFnbip0x77S7KPcSeimj3l2CE1eZ4j6ca8wwVOWhQwY4nA7Mb+uPsTzCLnMwgYWBDQZfOCEwSEVutPxScjluv3U8cgOUZ7Q53VCd+zJjsiXC91pWxEqmRumlOGJHh7/eNqy3/xIGEIuQjNjti5cPdcItvm4YNx1GlvMpzHXNs0bXJ92YIj4IXQzFrk2hvc9AvJIGsIxYb1Z4IAxb+bzeCc+RPVAhMaIGsCzW7eogYUwERYfoTsVm5v4lpX+9z9E7BC6CX2eG0L3CSecUCbzO0K4/LB0Htpwww0HbNMR+fhkUc3DDz9cJhGdoe5e0vx+l4AEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggdFFQKG7h/2N19w555zTukIqYLcSJ39JvdZIx1swhG68YtM6CDmdWypucA5BNw0FThrXCKEbT9Wtt96a5IrhwRt7OyOiIq6FQJlmpE14s+Jdx79cgMOTOoRuBHM8tnNDIEzF7HQP13bnoh720E2FboTC3FggkArdIbizF3p47VIGMQnRNTeEHPaIZb9c7hGv4VlmmSXPNsWOEYhoD4YA2yR0I2CF+MviihC68XaO/bURJvGmrAtfDrNYXIGYuNdee7UWAuCFHPtSs+Cibh9mFm6EgEtb8cyNcYQYyf6+GMxzIa888d//oo9pw957752ean1HeA8Pc4RLBDwEdoT5nXfeuZVvMF/w5o2Q4ojHqQgY9XAfeHNH6GmE7jT0OCIf4weBG7GxznjWQshm/NL+1NLziMl1+xMjfMYCF4R9xPk6gxPPJKGf4Tlcg094PePRO1JC91lnndXaD5wFEnXjs2lcdDO2ELojND3e3HULZOCZCt2xIISFBSzgoF8RkZvmR9gSVp+5Ai6pNzce3Ai3ePWn4cyjP5jLwxOaa7B4I7VOfNJnLx+TTfXkY7yJb1o+xjfjiXkkN4Ruoi1gCN0I2mHpPAYH7rGun9Nnu91zFPX6KQEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAqOPgEJ3D/s8Dd/KZfAaDlEoPIhTz7poCqJviMMIIyE6cj71ruUYYYQQtCF2kYagmwvdhFLmH4bQtcsuuwwQvPAwDSGUfMsuu2wZcjaEUtIQuENY5Jh9ZPPwzuzxikiBLbnkkuX5XMjAwzyEboT5VOhud66sdPJ/eJOG0I1gVSd0E6I4hG4WGYTQTR2EIEawx7g/7iEVnhDN8PqOPOSD/dQMnYuncgjdiJrthO4IUY4XZPRfiLPsU4whrOZeqewtjdAV/QfXNLwzdYTHM3XgGZ5GGGDfXwTQWOhAHjxzU6E7yiN05+OU/GHRxyG2R3r6SXvw0I1+jnMssIg92SOt20/GJWMQa3pWeG5DpCbf6quvXhG6EUoRg2HPfurRB+TFeNbgjGiK0Q+50E0o6/AYxyM25oSywH//Y3xGOG2EcBYe5F6vzCE813GtkRjHbBsQQjcevSMldKfPflM7m8ZFOjabxhZC92mnnVbSa5o3mEsRlDEWBIXQzTGLgWIRCQIuixvyuS3tE/qdRRrkYZEIi4Aw0lmski86QKiOcPjMv3nEg058EONDKGfurVuIko5v2sLWBOlijia+5A2LLQMWXXTRWqGbsRZCN4u2UqE7nceoj8UG+YIDysMhnuvx48cPOcJItNlPCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISmP4IKHT3uE8RRQivnBpebHio8RIf4SW33AuaENypcIi4jMcqaYhpIQZEPQi6aShw0tkDFmEtNURbQh6nIZMJ6x177yLGI8QgmPCJJ2IIoFHPTjvtNCAsM0IGIXhTwyMaYSlEUQQbhDwMoZt7Dmt3LvIgsqVCdy7Yki8VQOGRem2zCIE6EBzDELrpFwQpuEbobc7T7lzQj3JT6jPlijdpk9CNGBdCNyG/U5H1ueeeKxcqxL0hBiOokYdzhE2OUMH0O0JfLuSl9XPvlMf7lPLsSY9IRX0hrOJVngrdx/03tPJyyy3XVuiOPmbs7bnnno2Yr7/++tYCgMiEF3ou+Ma5Tp+I/SzWiLHB80pb8V5FSGQPaUKkp4bQnT5HCOGxDzxjirGHGMxe1zxDcA4+1MP4TRcUkMbznS5yYdEB3sE8/2nfs/gBBhisqIdnm7bSlwibMUdwDyMR4hyhO7yeEbrrvM3LBg3yP6JgxPzDmApxn3EaY6hpXCB0dxpbzLcRbQBxtW6BDEI3e8xjjH94h8EUEZz5FGMepu/pD/oL3hF6nPP0RTo3EZ2D0OgYZWNc8OwwnxEOP8YF803Ml2WByf91wye9BiI9dTAeGAP0W8wNUee2225bEbqb+EZ+Pk888cSynQjdhD7PjXsgD4bQjVAdls5jkcYcQoh4nlnayG9DjFnGAeH/o/+jjJ8SkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAYXuHo8BRLNLL7205b1cdzlENISOEE9yb1Q8BNM9pfM6ELcQTcKrG/EkF7oRaPDUI3x1agjQeYhnxPkQ6dK8+Xe8cREocsODFK/vEGzifHhAckwodUR1DGE+3cO13bmywOT/ENmCF4JV3V7RhCiGKwbTVOgmjRDmiP+Rh7Q6Q6is85Sty9vLNMTM8MrHIx8BqM7wNI4w9Qi+qdBNfrxGU6/8ujoQxhCw0rDLkQ+xFm/LEMsjPT4RJ4k0EB77iO0hUiFGxt69jJ18nEYdfEYfM75333339FTlO/0X4ag5gbiXe8JWCnRxwBjm+ix6aDLGbOxzvNpqq1U8smGDWBqhzevqQByOhSOM39yrlTJ4a4cwGnUgqPMshcGUOSaep0jPPwlrjqg5xxxz5KcGfYznfoj99HfTWBxsxYwt9oDPuSOmM8dhTeOim7FFu8NjGpE7FaGjrSxyie0ittxyy4rQTR4WyTBvUFc7YxwyvmPskxfxlrJNz07UB1P6OV9k0g0f5ngY8Qw2WTr2mNvSaBZNfNO6COFO/QjdeITnxtwf20NwL+n+9ek8xpiPOS2vg2OefcZsvj1GXV7TJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIYPQRUOieAn2OAIMoxAv+1DObSyO28A8ROkQvBDS8nFPjHAIwAlyEPec8giRiCnvChich3o+pB13UgzBx5ZVXlnlTEToVIiMvnoMI3uHVGul8IuAgtIXwlJ6L74iPeLKH1zXpeJMiWmAIc+HRnQsh7c6VhSf/h1dl1N0kdBOuOTzm64Ru6qI/4BaiXdTPJyIL3poI8XWCb5p3SnxPBaLhCN20FTGMvXIR9XJj/CAAcv9NhlDHWGKcpEY5RFvEdrxTsXR8pWIkCzzSkPVpPXyPPu4kdJM3fX7whCVSwXANsRXv07iPqI/2EIoZT9kQ7euEap43vN/rBGjGLPdPeH2srjzpLJShn1i4EM/sMsssM8ATnnx4kfPM5iIqCx3wquXf1NxjnvvpxhBzmTvwnA9jTuQ5xJrGRTdjC6E5Qpd3I3Q3jSXGBs9j3byB6M/CBxbWpHN13AtzI3NOPq44j6DLnIN3c5N14kM57pPIGGw1kRpjF5a0K0Le50J3E9+0HqKMMM66EbrzRSD5PMZvGr9t+bjl+WA+mWuuudJL+10CEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQk0CKg0N1C0fsviFEIq/zDqxJBpE4IadcSvAkRb6mLOtIQxu3KpecQhBBb+KSOdiIuXs+IOniEE1aW/LmHcFp3/p12Irpwjdlnn32Ah2Kef2odI0pyn4hI9An3SXsH2z9Tq/1DvS7iEuORsUAfISq1Gw/5dWJM43k6NfsXD3WeC9qO93fuCZu3ezDHPHM8L/HMMTYGY5TD+5fxRRh0ntnBti+eWa5LHU3loz94bhm7CJvt8g/mPqZ0Xu6FuYP5pl+fReYNnp+YN2DdbVujLOOCuRWP+9T7uxPvbvhwjSeffLL0vub3hvZN7TkNkZ/FGxj7sNMuxjd9zT/GLGnTwqKMTn3keQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEugtAYXu3vK1dglIoMcE8Ow/99xzy6sQHWDdddft8RWtXgISGCqBG2+8sSV0s20GorYmAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEhgKAYXuoVCzjAQk0BcE8FxG5I79knfYYYfSM7YvGmcjJCCBAQQQutkSANtpp50UugcQMkECEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAS6JaDQ3S0p80lAAn1DgP2HCXMce7DTMPY1Zv9hTQIS6F8C7CUfQveOO+6o0N2/XWXLJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQn0PQGF7r7vIhsoAQmkBNh3+Le//W2aVCywwALF1ltvPaj9xSsVeCABCUwRAgjd119/fXkthO655pprilzXi0hAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkMD0R0Che/rrU+9IAtM1gZdeeqkg/PHLL79czDLLLMXCCy9cLLnkksWYMWOm6/v25iQwPRC45557iokTJ5a3st566xWzzz779HBb3oMEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlMBQIK3VMBupeUgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIGhE1DoHjo7S0pAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQwFQgoNA9FaB7SQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISGDoBhe6hs7OkBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlMBQIK3VMBupeUgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIGhE1DoHjo7S0pAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQwFQgoNA9FaB7SQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISGDoBhe6hs7OkBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlMBQIK3VMBupeUgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIGhE1DoHjo7S0pAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQwFQgoNA9FaB7SQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISGDoBhe6hs7OkBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlMBQIK3VMBupeUgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIGhE1DoHjo7S0pAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQwFQgoNA9FaB7SQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISGDoBhe6hs7OkBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlMBQIK3VMBupeUgAR6S+CFF14oJk2aVLzhDW/o7YWsXQISGLUEnn766YJ/zz//fDHjjDMW8803X7HQQguNWh6j6cafeeaZYpZZZilmm2220XTb3qsEJCABCUhAAhKQgAQkIAEJSEACEpCABPqOgEJ333WJDRpNBJ566qnivvvuK15//fXypfmKK644mm6/J/f64IMPFjfddFNZ97hx44rllluuJ9fph0oR8x977LFSbHviiSeKl19+uRTb5p133mL++ecv5pxzzn5opm2QQEmABSiPP/54gUjI3Me8hzgc43X22WefJkjR7gkTJhT33HNPpb0LLrhgsdZaa1XShnLAM808hq200krFzDPP3Krm7rvvLp599tli1llnLVZYYYVWeq+/cE2uHbbssssWc8wxRxyOqs+bb765eOCBB8p7pr/pd00CEpCABCQgAQlIQAISkIAEJCABCUhAAhKYOgQUuqcOd686ygkgUN5xxx3Fvffe2yKBR+CWW27ZOvbL0AjccMMNxSOPPFIWRjjbeOONh1ZRn5d65ZVXiuuuu64UDZuauuaaayrCNMExfYoS4Jnk2Wwy5r+11167mGeeeZqy9E068/btt98+oD2rrrpqseiiiw5IH2wCgjK/DxjzV7oA4JprrimefPLJUvzefPPNB1v1kPIj7F955ZWlwB4VrL/++sXcc88dh6Pq84ILLigjhnDTiy++eLkYYSQB/Pvf/y5eeumlssq55pqrjBYwkvVblwQkIAEJSEACEpCABCQgAQlIQAISkIAEpicCCt3TU296L9MEATwZb7nlluLFF1+stBehZ4sttqikeTB4Ang2IwBjeDwuscQSg6+kz0sghFx77bUFHrIY3p0LLLBAKYjgLUs45TDEt0UWWSQO/ZTAFCeAcHvnnXe2rkukAURS5jzmw+eee651bt111+17sZv5hXkGW3nllYuFF164GDNmTOtf62aG+CXltdFGG1WEbp57hG7CZm+66aZDvMLgihF1JBf2EbpH69YQ0T+MXzy6R3pxBgspiBiAjR8/3sgcgxuu5paABCQgAQlIQAISkIAEJCABCUhAAhIYZQQUukdZh3u7U5fAv/71r4oXNyF7CQmLhzcvzaeUh97UpdD7q8MTL8SZZpqp9xebCldgocRDDz1UXnns2LHF8ssvX8wwwwytlhAe+vrrry+PCXs8pQSxVgP8IoH/EmD/6iuuuKLFA2E493omBHh4MBNyn0gE/Wznn39+2TwWmPQiYsTEiRNbCwM23HDDAUI3iwOm1HONZ/Hf//73Ad2x3nrrjVqhGxivvvpqOeem8+4ASENMQOjmbwVsgw02UOgeIkeLSUACEpCABCQgAQlIQAISkIAEJCABCYwOAgrdo6Ofvcs+IXDxxRe3Qp7ibYxIiQiEZy5C92abbdYnLbUZ/UrgtddeKy666KKyeYQ0xrOyTmxBPAwvWsSS0bqfbr/242hpF6JdiNjLLLNMsdRSS9Xe+j//+c/SU5mTLPipG9O1BadCIqGrMTy5V1lllfL7SP6H0H3XXXeVVeLRm4YuD04I3ZtssslIXra2LsLNs3AGYy9q9g/H8LwfrR7dJYAe/pc+M8zvREDQJCABCUhAAhKQgAQkIAEJSEACEpCABCQggXoCCt31XEY8Fa82Qgrjvct3RE1eXuO9RsjhTka5Rx99tBRE8VTlxSfhMgn/ygvvTkZ5XlATLvvll18uy8w222zFQgst1HGfTYQ1QqXSbkLMsjcwnmy0Ac+8TtfHu5b9Wbl/Qi5j3Dvt58V5k6DBy17aiiEI017ug7DMeLTBkDYgNqRCQFmgT/+79NJLS3EAkZv7wdj7lH7hfqaEcDESaOhL/jEe6NNO4+Hhhx8u8xJud8kllyz7FfGE/uTeKY9oQojtJi9srsczgC299NLlOCR8MPXQBgQ0xjNjhrGD5eM7r4NnifHEP9pC+9gTlXYMZlzj9Uj/Maa5Jn2b33PZoBH4D16MGYz9YZdddtnaWrkfwhxjyy23XPkM1Wbsk8TBjqm0L+n7pv4i7DLjgz6BV1g6TpjHmEOYVyKUNuNxvvnmK/uUsNQY9XCe8UKfs3iAPE1zOCGOmf9irqPNcQ2uR1miOsSYx0s08tB/tJk5nvZFG6L9+SdtY8yxaIbv8UwyxzbNjzkDrsczxT+ebX6feNaGY7QpnlvGIdeoM8Rd/mGEg+63/Z/hwb1g999/f/nJmOP3J+yNb3xjqy8jjb7g94/nNuZ5GNAv9H2dsUiFsYMhdKb9R6QGxhDXJqx5L41+I3oEtthii5W/t+FpzH7q/Sp0p32V/wYErzQP95H2Y+Thbx36AovfBb63m3vyZ57nkfmCv6GYC/ibheeef7k9+OCD5fPLs881MMYJz3J8Zy7JLf7Golz8FtFexlddfspPiWc/b6fHEpCABCQgAQlIQAISkIAEJCABCUhAAhLoBQGF7l5QTepESEvDkCanWsIFL/TXWGONWqGG8jfddFNLKKB8LnggRCD05Onk5SX7zTff3HppSlpuvAxdbbXVSqEuP8dLWsrz2WSItnko2sjLC/kIoRxp6Scv6wlTW+dtSrhUXjRj5HnggQcqHNJ6pgUhj/YiHPDiPbWRELp5iR4vxtO6231HlBmssegB70xeyDdZnUgV4gwv3xkveAlG3+b1MBYR2HJLxR9EFurkBX9YjAFEBbwesRVXXLGyP3VaBwLSjTfeWIpPUUd8Mi4JsdwkRPE8ICKn14+yCN7cA8894597XmeddeL0sD/hRr0YdaciWFo5YwLOWLtnNC2Tfu/3MZX3ZROHf/zjH+U8iOCThsROxwn7mDO/cM+5MbfBL82f50GMyj17mbuJ4ICxTzxjKrx00/LMfYxnxhJ7PyNU5UYefiNYiFFntD0EyLrzPOssMMkXFaX3xHhngQgCYFjdfcW5kf6k/dwHRljsut+EdtdEREZQ7tb4vUS8pV+6sU6MqYMw49FHzJVEVIh7qrtG/Pbn/dJubMdcSrt7KXSz6ILfJuYbrsV8Cd8YZ8MRuns9tzCGr7nmmhJ5PL85f/qGRTAYcwf3lxsLG2677bYymQVFsVCmqX/yZ54xnO9tHtdAWGdeSfue57/d73j8xkUdfJKfeb7ut4jzPMOUi3FJGtZPz/5/WuT/EpCABCQgAQlIQAISkIAEJCABCUhAAhIYGgGF7qFx67oUL0nD+6tOiI6KeOGNGJbnoXwuKuZ5qIMXsLzMTI0X1YTFDkGxrlzkx7sIcS7Nk3qORr6mzzpxEg9wXtw2vYCNuniJzr2H11Kkp0I3Hld4K7Wz4bx4b1dvr89dddVV5UKC4Xh040XGy/fBGGHS0/7upiyLLiKMLfkRWumbiFhAGv2IUMX9hIU4Qxov9mNMMu4RhFJxjTJ14ykVFxgzUQdiAnWy2IOX+rz4Z9xh7YRuyiFY0yZEUNoRAjJlSSdscHjbkobhCYfIzSKSMLz0yId4gFEWYYFnaKSF7rhmp09E1fBsH8qz0e9jKh0PuddryqZJ6E7HCWM2+pO5kLkznW8QRGMeZtyQPxfF87GWil7peGU8cJyWZ2EHAjfjkXM8F4zFdO4kygDXyA3vaxZshMV4jvoivc77P2WQtpH74x/PxXA9uuP67T5hzzzI/dJ+BNxUAGxXNs7lHCK93Wfdopym/NQfYyDmwOgrytBe+ifazYKg+O2POuln5ty0b4kGwEKL1NqN7ZhLuXYvhe4JEya07pdFECzQSsV+frMZy0Oxu6fA71X8/dDEKX53o/1s75BHG7j11ltbiyfSOaapf9JnPp1T4MQxzze/CWGE8mcRTBhzduThE2OxFc8ERt7UQ5tn/Oqrr67ME+Tnt4xnO4zr8xuQ/t73y7MfbfRTAhKQgAQkIAEJSEACEpCABCQgAQlIQAJDJaDQPVRyXZTjZeOFF17YyokHD151CIO87HzooYcq4uTqq69eCYGbhnKlEsJ387IZUY3yvBRNhblcHEw95CiPWEP5EI15aR/7bXKeF6Hpi2tEMl5Ih/ESP166IujhERUvbeNFauTlBSxiY4iRvGym/bzo52UwQkF4U1EGjyq8LVNREYEqypOHl72ILohACFG8dE/bT9tgMK0ZL6rhGALPUNoPyxA1uy0/WJEEgZeFExhjCPEj9RJjPMVevHjRpQsv8DgLEZjyiImMp+hv6uYewvuR8ZKL5dxfhDamDq4xbty4Vh2kYbzARwzCll9++YpHd14HY5I6Qpxi3KYRDMITtqxs8n8809xLiKCIjwjsiBgY5/HgS8clzyvP1pQ0xhPjCqOPYBn32G07+n1MpX3J/eUiVdwnY5bxxbyBV3RYOk5IQ0DCKztEJYRNxkJqeGBGiGNEWebw8NJGAE8995nn2KogjDHN9cPznD5inKZzHOOROQ5BivLM7ywuCcNjOJ4Z0lhgEuHpOWb+4z5C0GKcEnqa+8eYY3l2w3IGnOMeo42Rr1efcY/pbwn3H56zg7ku95Ky6qYsAjPjYrB2ySWXlEVYWLPSSisNKM4ihrQtzJXMefEM0h/MI/H7iciazqXtxnbMpYwnFuL0wtJxwW92RCtI53gWCaR/LwymHVNibkkjjzD/Mg+HsbAitn+ItPy3grF5+eWXlyIyzwN7koc19U/+zNOnjI90jPEbx3gP23jjjVvjItJYIBHzCnMKc0tu/A1E5JIYQ4jm/B7F/MUYY6Fk/O7mz1Xax9Q9pZ/9/H48loAEJCABCUhAAhKQgAQkIAEJSEACEpDAUAkodA+VXBfleNkdIZTJvvnmmw94oYnHEGIFL4x5SYlYiyG4hajIMYJbHmqal6p48oXYzQvp1DOMl6W0gX/UjYCRGgILYnJY/qKXtoegh0CfXx8vXEQUXsLS/jR8Ot5gsZ8p9SPip55IpBEGNQ3rmYuKudDNi+ZcgEFM4P6wwQrF3D8v3DsZQmgIR53yDuX8SAjdQ7nuYMuk/YVwU7cvMS/n6QfGYiqCpOOU8ZJ7l9EWxjMiDi/gsVRU5DgVF1LxhXOppS/w29XRtDAiFQ9zIQsBm2cW4/4QLkO8StsQYlTkQxSaUpaL8U19NaXa0+46wxlT6XhoJ3QjaCFsMf8wD4Wl46RuYQX50nksX7wR9aTz5KabbhrJ5XhOhe5cbCNjev/MbYha+VyD9yj/sFxcTMcZYzEV1MoCk/9LxzNia/obkTNgjk2F9KhjJD/pC+YDnnd+51LjNwCv1ZxBmqcfvncSuhEZmSvoX3476+bKdCEFQjJzWli7sR1zaa+EbuYPFk+EgIoncyzkGSmhO+6zl5/8XQQrLBd50+cOjvwtkM/1/O0Tf7/lf/809U8udNf93UN7+LspogLUCdndCN1pG/L74xoYi3HwXI/FNJtssknr2Zoaz/5/WuX/EpCABCQgAQlIQAISkIAEJCABCUhAAhIYWQIK3SPLs1JbHkoVwSnfn7lSIDlIX3TiFYTHV52lL2M5j5duePREfl6+YnXiAV7XCCEYHt94BYXxkjdCSiPq8dK2TtSL/PHJi3LChobl9UY6n7zwDaEaoSf1fI39QcmHUM8emblRljrCeCnPi+tujBf5qTdkUxm85nKmTXmHkh6iAtfolYfeUNqVl0G4iYUJiAL5wok8f3rMgoTwLGNBRdNzkPYJohzPTBiLEkLwI53zdcYL/BA48CoPD1zypnXgUV4nQJEvxh6ifCpSp97aeMemXoKUC0vHJXnSvaEjT68+0zbmDHt1zaHWO5wxlfYlYlGTRzdCD8IjInAa8SEdJ03zC8JeeFYy3hn3ueGdiWc3ls4/zLsxD4ZXfV6WuTfENIT0dP6NvCkjvENDEOWeuDes0/PI4gx+j5gbaWNYygAv6nwxU+QbyU/E7dg/Oa2XZxFBsc57Nc3XD98vu+yyshm0mXlkKMZva0SeYJFYGsK63diOuTTvy6G0oa5Mem0EVH6/wxjn4Y3Mwop0MVPk6ZdP/g7BIxvLn308nRGamZtZ8MTfW/z+8ndW/J2EkMw/jL990kgIKaN07kmf+fzvmbKi//6H0B57nafPdOTB6zui6fD7U/dMxN8NncZBOofx91UsFpwaz37cn58SkIAEJCABCUhAAhKQgAQkIAEJSEACEhhJAgrdI0kzqwshIl60ximEJ0QVXprGC8c4l37yIvbRRx8tk8jbJEDwMjfC6/KCFo+8JsGHvIgMeNTxD+ExvIq4EIJgGm4agYeXpGG8CEaM4R54Qdwk/rK3ZOyRTFm81cJTPeqKT7y+I9w1aamoHGIj6fkLd9KwVBjlOH3pzHE7o39CZGqXjzqb7rVduW7PxQtrrtHPQjdjJsJhc2+MAUQQ+jYNu1t33yHOcA4xoZ3XKJ78eKLhRZiGi20SF/LrpS/w2wnd7YRqhHLqyUUExnXsncqikhBF8jakgkd4fud5enFMaHdEGwxxBGG3HetetGEwdQ5nTHU7HhizXCcXu9JxwiIa5uXc0vkJlnUe0ynzJqGbhR0s8Mgtnb9ysTPy4pkac3y6OCNNRyStE+GjDsZE/J6kz1/KIH9WouxIf+JdCjOeEfoFsZ/nPaxpro/z/fAZQnenBQZpWwk1zf3yj35nAUMsMqP/6P+wdmM75tJ8boqyw/lMx2MsbkvnuGlJ6IZDuugnxj3jjmg5jDmY8/cVTLFU0GYRAosR+F1OBXDyNfVPOu+3WwTB315xzbqFX52EbsZSRPzh95eoM03GtWKxTrpAbGo8+01tNF0CEpCABCQgAQlIQAISkIAEJCABCUhAAsMhoNA9HHpdlMUjKDxz0hfGFOVFNS/KEVhyzyheskbY8PwyeT3p+fRFLemIuQgceBDxErud5V6NCBIIwRH2Mi+L6MPLXATy1NObF6shzFAGsbJJCOVla7zwJS8eRyHUpyE3eRlcJ+QgGqTegWl56psWDPGUvokX6kNpM96aEcK+2/J13qOdyqZ7Eqd5WbTBAgj6CAE8N8YD4wLrtDc444FxkedFrAuP7nb9zHOTenSn3uPd1hFt4BklLHZYCCTcIx6N7QyBleevm7zt6un2HItSYq6h3cwFEXK42zrSfP0+prrty+gH5qs0bHe7cRIcELrDg7VpP2fGJG3BGCuwxxC9YqETYzBdRFRmmPwfz30sCsKbOfXqjTw81xG1AqE7PLqZ09NFQpG/0yfjIn5vumHQqb7hnocT9xKcqY/FUflWF52uwwKU8KzvlDfO48U+lGckPPX5/WuKbMF9MY/x+0sfNv2O0hbakQrd7cZ2zKX53BT3NJzPmPeog/ktn8vT+b/ufLfXnlJzC+xTz2l+o9Ixz7PAgqDYwiWeQfoqoiXw900eTaapf7p55mGU/t3DvJD+RnE+nctZkJV7dLNgsZtoNNSVGoJ4eOinHOrakJbzuwQkIAEJSEACEpCABCQgAQlIQAISkIAE+pmAQvcU6B1etiKG5EJzKljjxTZ27NhWa1LP0VZihy/UlwoEvPhH8AtvufR6VBVeynGel635C11e+OJ9h/dZk/ESFgEmBAPCNuORHoaXY1wr0uKTNrLPbFi8eOYYgQrPJYwXsYgKuSF0h1DEOV4Kh1Ce5+3XYxYT8OKaxQJpWOHBtJcFFXiBDcZyL7Vuy/KSnmuFcJ2XQ6xDuEkNoS7E605e63jhRTj7tI2IC3jSYe36mRf4sdACMT8VEbqtg/LUgzd06lUegghegDxr7Yxxyfjk+WBc99J4PlPRE+/jXKQa7PX7fUx125cIQiw4yPus3TgJVgiwqTdknUd3yonoD6nQHV6XLADJ51auwW9ChK+ue27Ig0ga8ykLfkLoRnRkbh6s4dUZ99ENg8HWP9T8RBcJUZL2pdsWdFNnyqmb/OThGWZcDNZiHqAv6jz1ETx5HtOIKek1+K3kX8yJ/PYjsoa1G9sxl+ZzU5Qd6id/p8RiA8TQVHiPOln4EYtphjPHpM9M1N3pM/0t6JQ3zvP3SyyEQ7Dm76xgy+8tC1P4uyh+c1gAwoKWdCyykAGBPLWog7T0t4h+7/TMU4Z+j8UrzAv5Ij6E7ljUxYKCPPoPkQBiMRf1dWv8Lsdimn569rttv/kkIAEJSEACEpCABCQgAQlIQAISkIAEJFBHQKG7jkqP0ng5yctF/tV5eKV7wPISlDwYL1lz4bCpibyoDUE7FYrJzzlezPPJS3a8rHkZHyI2gmCTly9iOC9nCeUZ95C2AXGZF74YeVIv7VS8TsvwHUFzwoQJrWQ8dUMo4gV1CN28DG4SutPw47wUntaEboQuhG4WA6SiagtKF18Q5PjXrTFGUu/Wbsul+RARYkzjoYeoG4ZwEy/USWOP4BB1EBfSCABRJj7pT+rKeSCuh9Ddrp8ZnyEiMJ5TEaHbOqK9iEmIl2HRVzw76d7dcT4+ETwIvY8hOA+XddRb98mCgxBCOc+ik8F6w9bV2+9jqtu+DOEJQTMVT9uNk+ABgxD2KFsniiLaxRYP6fyVjgHGYN3citAdC32Y4+vm+bR/WfATQjdiXCxuwFMzxOtoe9MnYzeev24YNNXTi/SY8/Nnv5trMRfFooRu8pMH4XMoC0JiTNEX9Elu6djkHNFS6B8Ey+DP/Bleufl8mZbP57qmuSlvw2COGauwj0VvzB91i9NYmMZvFcazwG81+eA4GJuScwuLlvibJOZs/jZhrPD3RCw+QeSPscN8j8gcfxfV/V419U83zzycOj13LGIJoZu/n3Khm4U7scCPuSVdJNmuH/g94183bWhXj+ckIAEJSEACEpCABCQgAQlIQAISkIAEJNBPBBS6p1Jv8EIUcRBPojBeWIZ3GJ5VIVzyMr0pPGqUzT9TT0HOIX7V7ZONQBaeuQjd3b6w5mU3Ak943nKNeCGbvoQlvUmk5hwvjPGOCgsPK44RAVKhOwSeyMsngmh4RHKMKBCe5RxPC4bQFR7dQxW6p/Z9Mp4ZCyG8ISrEwgfahjjDy30ML8omcYl95FmggfFyH6/BsHSstOtnrsP1MMZzLnTHeGtXR7QXUQDxMoznNZ4XxnvTogqElRDb8ejuldCdXoc2Irrl3ofR9mnts9OYQgxiDsIQoVnAkxvPVQjJnM+F7qZxEvUggIXQvdJKK9UK3Sy+CKGbxQ+xUIf2R/hjxmDd3Mo8HZ6ZiNx1ghXjLX4n0v5NxzmLSiIkcbS9m8+0jvxZ6aZ8pzzxO8cnz1Ldb1BaByIkYiqW/hakefrheyxi4TcpxNK0XTF/kMazn4eeJp37jAVh9Hu6yKHdXBd153MTdQ7V0rE6lDqGGolkKNcabJl0nuAZjmeehSexeC79O4L+RPTmtwjBv+5vr6b+STk2PfO0P33u0nbEvaVt5jcwF7rT6zS1Mepq+kzb0Itnv+m6pktAAhKQgAQkIAEJSEACEpCABCQgAQlIYKQJKHSPNNGkPl4k4jmE2IKwl4ZQjmwIg4gpGC8zQxxENAxxg3OIcvnLTtIRShDEOcfLdK6Dt27uKU3Yz9xSUZFz6YtZBGY8cGk7L4EJY5p7eKUvhynPC+IQoxFvaBuG8MzL2vAiLBMn/4dHGyJUeJHlogEeS+TB0rrLhP/+h6iee3RPq0I3fFPv4fQ+++E7YwJhlc9UPE7bFiIMaYxlBG+MBRXh0c1zgAdqnaUv+BHuUq9wxMTUo7upn1PxN3+B320d0V5Ey9RzGy+/8PxDJKnz0v3/7N0JvDRZXR/8mn3fmGH2YWbYHFQwcUOJIq4oMMaYqNFokGh4XfK6yyp5CVEEUWKSN74J8Y3RuKKikcUFQYJGNnEBBVkUmH0YZmUWZk//evw/c5661X37Vve9t+99vufzeaa7q2s59T2nqu/Ur0539qsNxHNMbvY150MWm03L8ZUwvY6f/r5utvw6vL9Mn8qNQnVjRc5PGTXbLzk31td7ZwRqgq4q8/pJzbPOQXfO3zlHpv1nnWNrP2KVYDRhf3seXsSg1jHmsQ3ksnxu5Gm3364zfaFGOPdvlGnnW4fnmwXdNTp63rHfnuv6Qfe889Ssc9MyLmmnujFn3nryeVufyWmjuqljO85v8+qxlffam13yN1LdSNHelJL11Q1nuXkpy6Tkc2ro77ZZ7dP29/bvqenKmv+0IfNmQfesG8Paz5h5N13lb9D8rZZvFMg5oMp2H/u1HY8ECBAgQIAAAQIECBAgQIAAAQIEtltA0L2Nwvm90fr6y2wmI0PbcC4BRUZ0VSB89tlndxdeeOG0RhVi5OJ/Si6YJ+xtR5DmvXwtZy5ipiQozcXbBAn9ELp/sTTrT/1qdGqWbwPIXOitkYZ5L79v2f/NzuxbjXbMPAk268J3AqIKmPJe1p3fIK2QI/uewLDdfkazt1+/mwC79j8hXoXoWV+VXHivEZuZlkC9Na751vkxzmmv2LSjh9etzlXP1Cuj3Ppf41z9OUFILqjXTRuZPxflc3G/Sr+tMz0BRBu2JJRMv6+ScCEj6VLmtXMu4CcMSklQUaP28nrRdVR9+/uRfUwd65irfl0/F5DjKl852/42b8KVdiRx6pFSgVHavX8TyQNzzP5v+n3qUcdHbggYCnpnr2E93lmmTyXAqdGZMcw5rj32c26p33zO3ibkzTdbVJnXT2qenMfqK4SH+nzmyzcEJLRMyQ1JdQ5M6JXAMyV9MH2xX3KebUf1Do3KbvcjoVg7Yj8j2uubP3KOTj+ovljbyk1P9dvLccpnRM2ziEHWk36W/clybVhW25j32H7O5Bweh9p+LZfjJjct1M0w6cvtTS4137o81rdOpC2GbnbJz3FkX+KdPtE/vnOuyzkq+52Sz/52RPe889Ssc1PWkzaqc0K2me2vsrTHQ85pQyPVV7m9Va2rPc9knUPn5PY4ru22x3NNy+Os9lnkmM/y+SxMO6bkeGg/ozItnx/1t1X+7hoK29vjOp+T+Uzt97N8TuVvxOpnOUfWDZOLHvupj0KAAAECBAgQIECAAAECBAgQIEBgnQUE3dvYOgkoKojJZnIRMl8zmYusCWnqN7irCgn22qC3gr+6SJn5EhQkzEk43l++P6oxX+mdQKxKLspn2wnYchG+wrp6PyOQKmjPtNQ99aySC6SpXy7mZt/a5XOhtR0tmWUSEOXCeFuy/1m+Ao16byioS/0rDNzPQXeCrgRe6R/t6OGyWZfHBGr1VdEJ8xKs1ddF58J9Ro3WSLh+UFXhTLsvGcWYsDz9O/0pF/cTJKcMBYMJE+trx8cG3Yuuo+qb/UzY0Zb0+4RU1TfzXo6rhHd1vFTgkP0ZClXaGzRyTAz9zm+7zfZ5gqxsv6yrLdp5+s8zTz9M6c+zG6+X6VM5jyTsrxuFUv/0u4R7Q+fH9LX2a4gXCXpyM0/dsJOQvPp7a9UGZG0wtkjotUjQnXNlgtOU3OzUfv13jp0EyXUTSc7DMchnRPpn9rFC+CyfMDWhapVFDDJvGxTmWyf6QXWtb+ixPsfqvdQtwV0+T2KUtko/qOMpI4XzWZI+u65ls6C77dc5/vPZmrbJ+SDm11577YFzXfYxNzi0X1s/7zw179yUc3B940S/rVdhuVeD7vYYjUOs+zeVtOFz5hk6b2d6yqz2WeSYz/LtcTf0t037+ZD5U9+c13LstzfztPuV4yXz1XGVbeT8VX+njTn/ZdsKAQIECBAgQIAAAQIECBAgQIAAgXUXEHRvcwvlQmT+DZU2LEjQ27/wmmVysbINy4fWk2kJDhJ0t+tsR/wMLZcLo7mYm5AxpR9052Jrtl0BxNA6Mi0XVjOqrR1tnum56JuL7qlHv7T1rJGI/XkSdNfotFwMbkcy1rypY0KYKvk91PZCcE1f58eMuKqgux0FvW51TqiWkaH9mxT69cwF9bRXO/IzQV2ChEzLzRoJgmaVhIkJ9CosrvkSLtSI7nntnGOmRsvlxo024F10HW19+0F36pNwLtuoYL7qmMfUO8F1AtK0a46x/k0gCcRr1Pms/t+us30e/3akcvverOc5NjOab93KMn0q+9K29dC+5byUr7vPeSL9qh90z+onta4ERe2I7qGgO30y/SolXyFcAW3Of/VV3OmD7U1Etf70jxz/KTn/D30GtO2d/WmD7iyXPtje+JBpQyXnz4webc+9rV//WKl1tPsxth/leMmI7bTDvBLf7GN77pg3/269VyP1Y5pzXb/kcyvHaEL+WSX7Gv+U3HzQD7pnnevmnZsSoNffG0N9ZVZdFp2eoLtudso5Lee2vVDafp765qaV3HjQlpyL8lMAVdIe7U0hNT2Psz5H2mNl1jGf5dv65Jgc+raaHC/1t1mWScnfifm8aEs+Z9pvEGnfq+c5bjPiu85Nmd7WYdaxX8t7JECAAAECBAgQIECAAAECBAgQILDOAoLuHWidjLLKhdEa7VmbTOCQUDAXVPM4q2S5fFVm/6Jn5q8RfEMhcN7PshW45XWVXKDOxc2E0BXS1Ncw1zx5TMidUHIomMzIu4QuCWdmfUVqLh5n2Vwgr9C61p/lc9F26Gs5M08C7FomF4P7AU/mSXBSQVFeJwBtL+Zm2rqX3EyQwCuGQ6HqOtW/2jNtmudtSXumHyYcaMO0zJPQJxfWE2BlNHYCmfTptmT/0xdqVG77Xp63y+Rrc2fd0JA+XyNgc/NHGyIsuo5+fft1yevsf46fhFkJGxNw57jKtx6kD9ZXnOdGkPYrs7NslquRwv065v15JTcM1G9Tz5uvfS9WQ1+f3s6zW8/H9qmqb0LUhG9tqBj/nFfTH/ONCTmP5VyZsKfKvH5S8+TcXUF3bl6YFXTX+TF9u0LahF75+YWU9MG0c79ktGX6SUqOm6FgLUF3ffV4wsv2Wz9qfdm/9O0E8/2S4zLHVOrQPy4XMci66+vVZ+1Hf5tDr3OM5JjPeSDnu7bENf/yeTDrs6Sdf7efVyCaz6R8Ng2V9Ov0y6EbvfKZGctyTfu0NznMO0/NOzdlexV65saWtP0qS/pXbhxJyTkt57a9UHIs5idO0ibpX7khpX8sZD/aG7nm7d+s9lnkmM92cgykHVNmnf+zrhwvMU+9U3I+698wk/lm/Y1Vn6k5rvo3ji1y7E836j8ECBAgQIAAAQIECBAgQIAAAQIE1lxA0L2DDZTQNsFsHhM8zQrqZlUpQUGWr1AtYU6FKrOWqelZJuFCLopmua1uO8sl8Mj2c4E4F7i3Ekhk+dr31Cl1yL+hi81VZ4/rK1DtmT6RNsyIsf6F9Lb2CWZrRHfCwJSsIyFl+mb6QkKZrfSpdv3r+DzBSvYt4Xc7kjh1bYOSjIyM36Fettqn+l4Jg9If0w8XPS/217HXX8egPiOyLzmmcmwtU9pA7IILLjjoGxLGrjf1rK9UTt/fz58D6df57E3fTHvks3e79rfOszkGEuYq+0Mgx3T6UfrPrL5T58+6OTDnwHnz7w8Ze0GAAAECBAgQIECAAAECBAgQIECg6wTdegEBAtsukACmRnRn1P1eLgnpMoIuX/U/K1BNiFc/OZBRn/1ReBnNfdNNN02X3+see7kt1X1zgYwQrlG8uSljqzdJbb4Fc6xKoEYtZ7R4bkpQCBAgQIAAAQIECBAgQIAAAQIECBAgsN8FBN37vYXtH4E1EMhXwlbQvY6/Fb0oUUbWZV/ymG81SICdUXNtySjuzJPR6in5Cu2E4m1JCJ515HdczzvvvPYtzwmslUB+8zlhd/p5/7fm16qih3hlMmK8vgY/X4c99FMfhziR3SdAgAABAgQIECBAgAABAgQIECBAYB8KCLr3YaPaJQLrJrBfgu6M5s5o7HwNe5X8bmp9lX9Gcud3nRN2p2TEd8LB9ivZ8179Nu/FF1/cnXTSSbUqjwTWTiC/JZzfP8/v/A79hvjaVfgQrVDOSR/60Ieme59viZj3UxKHKJHdJkCAAAECBAgQIECAAAECBAgQIEBgHwoIuvdho9olAusmkAAmv/Wb8OUxj3nMulVvS/XJb6FedtllB4XdQys48cQTu/PPP3/D15snDP/ABz4wDb9jMes3V4fWaRqBnRbIKOHcnPGIRzxiekPHTm/f9hYTyA02V111VXfyySd3GdGtECBAgAABAgQIECBAgAABAgQIECBA4FAQEHQfCq1sHwnsssC1117bJeBN0L0fvqo7YffNN988/ZevKL/nnnumwhnZfeyxx3bHH398l5HeCgECBAgQIECAAAECBAgQIECAAAECBAgQIECAwPYICLq3x9VaCRA4hAQSfBuZfQg1uF0lQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEdl1A0L3rTaACBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILAVAUH3VrTMS4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQK7LiDo3vUmUAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ2IqAoHsrWuYlQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgV0XEHTvehOoAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhsRUDQvRUt8xIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDArgsIune9CVSAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLYiIOjeipZ5CRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDXBQTdu94EKkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECWxEQdG9Fy7wECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsOsCgu5dbwIVIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGtCAi6t6JlXgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDYdQFB9643gQoQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwFYEBN1b0TIvAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECOy6gKB715tABQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgKwKC7q1o7dN5P/GJT3R33313d/zxx3dHHHHEPt1Lu0WAwKEicM8993T33Xdfd/TRRx8qu7yr+3n//fd3d95559T78MMP39W62Pj+F0hfy98qRx555P7fWXtIgAABAgQIECBAgAABAgQIECBAgMBcAUH3XJ7l3kzQct1113UJAVLOPPPMbjdCgFwUvuWWW6Z1yMXhhzzkIdPn+U+m33DDDdPXqdsFF1zQHXbYYQfeX+RJ9u/6668/sJ+nnXaaC9CLwG1xnnvvvbe78cYbp84nnXRSd+yxx25xDTs7e/pWBRJtn9vZWuzs1vZaG83Tyfkrx3VKboI54YQT5s2+Nu/diYYoGAAAQABJREFUdttt3cc+9rFpfU455ZTu1FNPXZu67ceK5Px/1VVXdbm5IOX8888fdcNUzm1Zx1FHHXVItNm67u+6nMNyDKdvHXfccd2JJ5544NDJOenWW2+dvs7fVHl/q6X9myjnCDfEbFXQ/AQIECBAgAABAgQIECBAgAABAgTWR0DQvY1tkVHSl1122YEtXHTRRaMCgAMrGPkkF4Ur+MkIqAQRVa699trujjvuqJfdOeec0x1zzDEHXi/yJBfGr7jiigOznnvuudOw4sAET1YikIvzaa+U008/fdeCxwSg6dsp6U+zvgXgox/9aJdvC8gNFG2fmy64B/+Tfc6+p8w6RtaljVbBm+P6yiuvnK7q5JNP3jPhY851t99++7Te6Z85HynbJ5Dj4uqrrz6wgTPOOGN6Y8SBCQs+yTqyroSOZ5999oJL7d3Z1nV/1+UcVn875QabfN5Vufzyyw/cVJcAfMxNVPmbJzchpjz0oQ8dFZZXfTwSIECAAAECBAgQIECAAAECBAgQILC7AoLubfTPRfu6WJvN7HbQnZHaCX7OO++8A3vdjn7MSLoxoVAbiGXFCcuzLmW1AgkgEh6n5OL+bo2wveuuuw4E7hm93462a/c4dU2dE3S3fa6dZy89TzCS4D7H0azgfl3aaBWuCfUr6M43COyVkdFpowqxUufUXdlegTrWc9NLQuox31yS4LdGdB8qQfc67u+6nMMSaKfkc64Ns/NNITfffPP0PJyQetZNR/N6fILuuvkvN2aMGRU+b/3eI0CAAAECBAgQIECAAAECBAgQIEBg5wQE3dtonaC7LtZmMxdeeOHM0a/bWI3p13zm6z4T0CWI6IeOudiesDoj6bb6teWpd5bNV9dWSUgh6C6N1T2uSwCxaNBdwXD63JgbKFYnt5o1JRhJQDIvuF+XNlrFHrdBd0Z05yt+90rJVx7n35jAda/s47rVM58Ds77dYZG6XnPNNQdGdJ911lmLLLKn51nX/V2Xc1j97dQPutPoOTflb5Uxf69keUF3FBQCBAgQIECAAAECBAgQIECAAAEC+0NA0L2N7bguQXdGbbdB96pDR0H3NnaiZtUJIGqkakZS7+aI7hpZPq8eqWvqnLBx1X2uYdmxpwm6a0R3/2aRqsS6tFHVZ5nHhEl1A0tGRe+loHuZ/bbs7ggk+K0RzodK0L2O+7su57D6OZTjjz/+oBHdq+idCbrzN1FKRnQfe+yxq1itdRAgQIAAAQIECBAgQIAAAQIECBAgsAsCgu5tRE/QXRdrs5mHPexh09AvYVn+ZWRsRsBl9HO+/nmz0XAZoVjL5gJ5XueryLN8Qs9ZoxcTdN9www3TPe2Prk0d835Kls/IzaGS0CvzZf5sO9vNV4bmInTea3+jNSHF0Iju1DcX0bMPCcezXO1/1tPf/2wv20rJ9uYFux//+McP/H7yZvMO7d8qpmV/Uo+U7H/2qV/aeeI962uV8/Ws8cqItWqT9Jc26M76M63+VX/I17BmdP5mJW2Q3zJOm+Z53PIvy+exX9q2S1CQku3UtvruCYazTPYzX2efbaSumZZ2rbZPuy4yMq/t+9l2jLPteV87W441X+1D1Sshx2YlX5Obkn3OPqS0X9feHru73UbTym3hP/0+kDap4zr9qY7r7OOsoDvzVT+q4zXrSXiUf/22bY/VWeusXaj2y+vN5q1lsk+33nrr9OWsYyHHYfpA2iv9P3VMnTP/mK9Crm3XY/XV6i/pf+mvWXffo5apx/JM3bJ81a08a77+Y1lVX89+1fGW/c32s47N9i/bj00tn9fVL+IzVP9q/7xX56t+/fr7lferr2X911577YGg+8wzz+wvfuB1v89udt7KgnGoc1bqF4+2jXIe7p9H+vXNvlUbbmaYbfaXz7Qx+5vlZpXaRtpqkWNv1nra6bHazs+ZfB6kzfL5Fc9ZpX42IfPlhqoqbVum3bK+oVLzxaX6f/zTzukL9TdRfv87x8VQyTrST7KO9LtsK3VOnfqfkWmDHAcp2c6sdeb9qtsi82YehQABAgQIECBAgAABAgQIECBAgACB2QKC7tk2S7+TC59t0H3BBRdML+Zner/kAmpCt1xAHSq5yFpBwND7mTZr+XlB97z3aju5eFsXhWtaPeaib34/M3WrMhR0JzjJCKpccK7SD0z6YVoCxgqsskxGBfeXyfRchG63n9/lnReKZ5ntKAkdEgzmMcHN0O/M5qJ5jSRLHTJP5m1L2jqjG1NysTwX4lNycbwNIPI67TdUsv/zflM5y910001Di06nJUDIv9Y7QVoF+UMLJkBof0u1DbrzW6oZBR6bfsn+Z7kEdEMlfSb7XUFOf570wezr0PIVllQw3fan7NtmI81T3xrV3N9uvU4oV4HNbrdR1WmRx1hUiN+fP/uTcKlG7vePzZo//SjrGGrXzJPzWtq+DYVuvPHGA4FQzlmzQsOcJ2v7mWeRmxKyzX4b9M+peb9/LspyVdpjrqYt+rhZX41DjufWo1136pbjZpZn2iUOQ+Fe29ez/lnHd47TtG17bFcdcu7JuT71GCo5VtPf+9tvj/Xc1NIvdf4f2q/UI/uUfpFjPPs4K+gec95KXXLeyvkrJf2xb9z/zNisHWKYGy/65+7pBib/WdX+1vqGHscce0Pr6U/Lvu/U58y8z6nqz/2gu23L/K3RP5bSx9KX6saG/v5lfTnG62+aoaA7x0GOn3xezyr9c2L7d0g+i9LPZpVsu+qX+YY+u2YtazoBAgQIECBAgAABAgQIECBAgAABAgcLCLoP9ljpqwQ1dbE2K05YkwvgVYaChnwlcv/CbcKTBLkVkA8tV+vMRdP+SKJcEM+F1SyXC/NtEJH3clE4pf9eprUjn/J6qGS5XBiu0gZ/mdZeOK95Zj1mtF0C1pT+ckMXpDNfP7DL/vWDmMy3E6UN8YYuwufiecyrJHDqB3F5v0Kq9v3WozXPRfLsb/pH2w7tsrW9PPbD9vSLrCPLtmFy/0J++kJuekh/TF1S0lerv6bftTcYVPiV+aq+eUyQ1a6j3o9Xv28ntMh6anuZN8tnfzOtDc6Gbhqo4y/zZ5sp2UbWkTIvjMj7WX8FIjl2a3vtMRbn6m+73Uap8yKlDYtq/vSB7F+dZ9Ku1R/6fSHLDJ0bso60cRyqL+Z1G47GMW2a0g+xphP/7j/tjRWz+nI7fz1v26C/XPte5k9fSFvmMftT7dsPPmvd8x6zbL+v1rGZ7bb9L/2u+mCtM9btzSB1rGS97edGPkdyLuwfK9XXs1zZZxt53T83tOfZ2n62Uzfq1LRsK8tn+7XO/g0tmbeO9RwH7edL3uubZ1rqlfrnvZTal9Qh7w0F3WPPW1l/29/bc0H6eLadz5zsV0q/HTItDqlb1TfTZtVzVfubbcwqY4+9Wetrp7f1b/tS9eV+X+ofY7WufnvFK97pR61j7Ie+BaD6c/8c0bbl0Gds9cWqRx7TfmnX6sPtua3/d0XaOUF/nQezfPpIzhM5httjMTc75NxYJcdvLTf0eZT5so4cZynxHbohbvqm/xAgQIAAAQIECBAgQIAAAQIECBAgsJCAoHshpnEz5YJnXaytNeRiby4M5zEXXTMaskYN5WJqLpq2o2Lromt7cbUuyueCfZZN0JuLuBUW5OJv1l9lXpg9771ckM3I4tShSoLMBAK5QJuL1Qlk2/czXz/o7o9Ez0XtXDSuACUXruvicD8oaZedNforF6XrwvlQCFN134nHNoAYCsviWRfbU5/+RfxMa0d7tRfL2wAi8yV4SF/KRfsqbTjYt8w8ca5RsnndH1GbbWT7Vceh0WZtPYb2MetN6QcO/XnTZzOyNo8pQ4FJe+NArBIsZL9S0u+yvzVKO30qoUVb2uMv/S11yHxjSuqa423ItdbX2mTabrVR1WfoMd79b0DIcVOuafu0XbVL1tEPozJP+lGFtzmmc3zWOtI2OTfUV/nmnFZBYtZXx0HmTx+vc1feq1Lz5PWsb3OoedvHtg36far9loj+e9mX9psDhkK0djv9521fTR/L+luPbLtucukf9/1t9wO0vJ/112dFf/nUpd/X+9+S0N5Ak/n7pu25K59DadNql83as471fnCX5dJPqi+lj6QvZb6UvJ/9yrar5Jjp34Cy7HmrDUezndQj+1ftU9seaofMWw7p96lvfR7322lV+1v1GXpc9tgbWmc7rT1+Mn3MOay/jv7nTPrDZuf+6s/9vt62Zf8Yzfkm7ZOSts0xmJC72i/tls+3tHOVftDdfobmszXHUfv3VLtveT91qNIeY/2+UfO0dUwfzPGgECBAgAABAgQIECBAgAABAgQIECAwXkDQPd5u0yX7QXculvbDhVwYT6CTeXMxNvO0I3xy4bRGlGaDuXiai6NtyUXjrKNKLuy2QUHWkYu/WX8/iGgvuvbfay/aZt1DF2X7AUTma4PuXFBOEJkLzLlA3A+XMn/Cm3Yfs3yFt1k2F55TUv9+KJaL/m1ol4vSY4PM6UaW/E/2t9oi7dAGr2mnCplzET7zZp/aEZDpDzXaK32hbcf4JVBKyXK5wN4PavJe5sm8KfFq52nfS/iQEKNf0qb11bX9fci87fsJjhNEDJUEGRUI5WL+0AX9vJ/5UhIopX9Xad9Lm6Zth0r6TgWA/X1qv3a8/97QuuZNq+3026xdZl3aqK1T/3kbuPbNa962H2daBZ/1fvppzHP+SD/tn5MyX7uO/vLtDQpDx2zrmP6VfrZoaZft988cm6lXzi9Do4bTt3POSX/Lv7T1IqXtqzmm0tf6JWZtkN4emwnv8i8lNwTkPNkvqXd7c0G/Py/S16sPZ93teTav2xsT2rrlvZRsP30n+xebCqvzXh3rOddk2SrtuT39JHUeMm3PS/3zXtbVvt/f79pWe17qn7faz5F555K2HdKn02/7Je2Yz5x4pOT8Xfu0qv3tb7N9veyx165r6Hl7/GS/lv2cyTk955l+abeT91rHvK7+3D/+27bs9+H2poocQ+3NNVlnSnus5nX//JN+lBsvMl/WUX+LZN4q7fmrPVbSJ+rzf6gfZ/k6VvI8tu1xlGkKAQIECBAgQIAAAQIECBAgQIAAAQJbExB0b81rS3Mn2KxRSVkwoeVQKJgLtzUKKfPlt7yr5AJ/hXi5cNqOHqp58ph1JISokouvdQF1Xpg97702XJh10Tbba4OzvM5+Zv5+yQX6CgTa93JxuA2rc3E5YURKjV6r+XNROiFGlYTxbRAen6Ft1Pz9x6x/s5LwZivrTJiUi+QpaYdaNtbVRgng6uvJE9yUVy7+V/CbYLgNWtr30o/aULjdh/SFCs0StFeY3VomAJgXHqY/pt9l3/t9LkFA+kZK6jDUp/Ne9iN1TkkgUf1xOqH5TwX7adc2zI5VzFLSp4YCh7w3rz617vjGeZlS7Zr2bMO8dp3r0kZtnfrPK6TLfsw7XnJc5fhKSVA1FGb3191/Xf7pg+1NH22bDfXFdtttH+6vf+h12wbp423YlSCsjvl5fWpovfOmtX11Xn2z7XwupMSkzg1Vr7zOsdLenNJuN+eV9MOUfpuUdY6T7NtQSYBX5532PJt5232Yd1wPrbeO9f75Ituq0drtea6/jna/+sfqKs5b7Tmx/xnS1qVuROjvRztPnrefO20/WsX+9rc19nX1h/6xt9n62uNn2c+ZzbbdHuf9/lj1758f2rZs7XNctTdotZ8l/X3O51fOQSnz+kN/uXrdtn//eK/Pzszb1i+v2791NrPJ/AoBAgQIECBAgAABAgQIECBAgAABApsLCLo3Nxo9Ry681qikrCThWC5u9ksCxVygrdAjv9Ndz7N8AuKUBJ+zwqZsqw2LEypUIFxhdtaZsLENLvNeBR/99zIyqbadwHVoRG7q1V4Yz+t5gUbez8XehBf1L/ufdVTph1MVouT9/oX3ee/V+uY9Vug3b56tXghvA+122boAnj6Q6TXyK21aI94SUOdCfkr/InnrPC+IasOsdvttmJT2bAPA/v6nDhVQpb+0wVsbUs6rRwXDWXc7ar2/rQqX+hf+2zAiFrNK+mgF7/1+OissmbWuedOr/eaFYOvSRrP2ow1a+jcW9Jdp96Xv2p83r9MOdUznMf2tbtLpt23mb9u3vSEk79VxmXNSgt+tlLbe/XNJG6xlnTmf5F/C4TrnbmVbNW/ty7y+UfP2H7fSJu28fdPq67lJKKHhUJlnk7ZKH6+S9cQm29nMpo71/v6XS9bZb+PaTh7Td+p82A+6V3HemhWOtnVobXNszPqszTKpU/pSSnuOXcX+Tle6xf9s9dibt/q2j8w7vy/yOdO/Wau/3bZt+/NWf+5/5rdt2d5ANW9d/e2262jbrz9fvU7fyN9YecznXz7j8zylv/y8erR/G/TPTbUtjwQIECBAgAABAgQIECBAgAABAgQIbE1A0L01ry3N3Q+6E2APjWrNhdGM6KswoYLuXLxug/JcUJ0VTvbnTdCRC8QpubiaAGMrQXcbPGQd8y7KZj9rJFXmHQq6c3E4F8bzry4QZ96h0t9We0G9DVKynjbc74+sGlp3f1oFav3p7ev+hez2vaHnCfnSnik16rL1rGC7wqE2cKzQdyjgawOIvlFbjzawauveOrbzb/a8355bDbrbNhvaVu1zP7hbpG366+uHfLPCkv5yi7zeatC9m200a3/aY7X65qx52348K+jO+tKv0ufyfFbpt23ma0dFtqM5237eD79mrb+d3i7fb4OcM3JzzFBd03fqX52L2/XOe159dWg/5y2X97bSJpm/Rn/3R24v0tfn2WTd1cfzvC3Zr7IZ+gyrc1n/WC+Xfnjdrruez5p3FeetNthsw9Hadh7bdminb/a8zueZb9Y+DK1jK/MOLZ/6jj32htZX0zbrIzXfIp8z7XFdy7WP7Tmmfz6a1Z9ntWUbIm+23bZPtZ+RVbd8Xmf/8i8e8/5m6S+fZXOMZpn+MdremNe/gay27ZEAAQIECBAgQIAAAQIECBAgQIAAga0JCLq35rWluXMhui7WZsH8PvdQSDAr6M6F0nb5BLkJG2aVzJuLrCkZiVWjhHMBOKO2K+huR0i2F3zbcLUNZrO+/sXcTKuSeuaifZX+SOR2GzVP+5iLwbGqMhROteuvQLu9sN3WvdazyGPsy2zW/Al5EuBspdTIvrrQ3YYHFRy39c9oxzj2A/J2m+06+kbtfLMCiDZYbOff7Hl513w7FXS3fb+2vdljP2isdfRHBW62nqH3KwTsh3ntvOvSRm2d2udt220WIrfH9VDQ3fbfdht5HqO0RfpiSr9dMq1df85rCadS2q/Q7p9LpjNs8p/N2iDHe46F/Esd+iXHbOqSx0VL9bP2ppVFl91Km2SddW7p98Oqw7y+vplN1p92zb/Ua6gMhYizgu6q01D799ddN7z0Q/FVnLdmhaNtHdp2aKdv9rw9jlaxv5ttL+8ve+zN28YifSTLL/I50//s6G+3PQf0v6K8LPv9eVZbtiabbXdW3VO/1GnWzTB5P/0z55D6m2Xob6P2myPq874N9fv7mvUqBAgQIECAAAECBAgQIECAAAECBAiMExB0j3NbaKlFg+5cWE7AWaMIE4jX8/Y3vhNuVnjdr0AuztZXv+a9XHytUDwXgLcadGcddaE5z9tRa3ndln5A0IZTMWhHe2e5GhmYICn/sq81AirvJ6Tvj1xP/Ss0y4Xv1CfBY4LqlARx+bcuJRfj8y8lHgn78zrhVN1oEJuEVikJj9KGCflS0n4Jh9qSfpJAKWXIqOaNU30dfdab8C2lnR6/ml7LzXpMnas/Zp60d4KAlKwn7TFUUtfUud3nofliEIsECAkoqqTfJBzITQzxWLS0N5PUMZH+FLNlSvW3WLRf/9+uc13aqK1T+zyedTzmOMw5ZVZp2znnnQR6Vdr9zLT0pawvbRj/6i+z2rbWU6Z5neOizgUJkvr9oZbZ7LGt27zjJOvJPuZflqnzS60/9UnfXaS0fTXH+1ZK2yaL9NPq0zl3JkSrUtPnrWMrNqlX5o9Pzl/tDUHteSXbn3Wsl8u8Y6Zf/367r+K8lbA8PwuRkvZpzxG1/X47LPp5kn2rfrKK/a36zHps2zDzjD32Fln/vOOnbZe2P7TT5y2f7c87x8zqz7Past3uvM+lbLddR1v3vFd9Oc/Ttjn35bM4fab6Tbut/vJZbmi/8ndY+3X3/c/3LKcQIECAAAECBAgQIECAAAECBAgQILB1AUH31s0WXmIrQXeFT1l5G3QnAM56UhIozgqmcvG7wsfM245oakdU50JtBa2Zb957ddE+880LT9oLuJk34UuNhsz6K7zNReO8VxeLM29KApQayZzXuTheIX1epyTQrvA2oUL2rzUbWu8DS+7Of3Ohu0LpXHSPUdoxjnldJfuQgDttm8dcQI9Twpg8tqW/zqxrqGQdZZ7+UoF2+kiCxZSEOLNumhhaZztt0XpkW9lm2mte+JcwNCFTAq420K7ls+1ZwXJbr6Hn9U0AffeheTeblv6Xfph2aY+hdrlFbba7jdo6tc/bYy3HYRuUtvPleY7dCmbSV9rgL0FR3ciRAHzWzQ45riu0btu2ttU65LhIX6njPK9n9fFafuhx0TboL5s+mP1Nn00ZOg/1l6nXbV9NX6/gs96f99i2Sc6b7c0e/eVSx7o5Jsd1+3mwSF9vzwFb2b/UMe2d81hK/3iq/e8f6zU9y8xzaferb9DWeex5q+2vsz4r2nZICJkAc6tlFfu72TbbfVnm2Ju1nUWPn/bYbT9n2uVzXmhvkOlvs11Hf19m9ed2/9u2bPvJZtvNcZ7zW0pb97YP5PyYc9bQsZzjoG6cSD8ZCq3rc62OiRolXq/7Fl4TIECAAAECBAgQIECAAAECBAgQIDBOQNA9zm2hpRJs1qikLHDOOedsCHkzPRdoE3hWsJn56nkuyObCbpWEBRUi17Q85iJq1pOSC6ltMFhhdtaZi7dt6NgG0f33ciG3go2sNwHM0Lbrgm7mSWnna9fRD2YemPuB/a9wK9MScPWD7lyAznbymJL3c5E8JXUaCtGmb+7if9KmqW/qVzcrJFyq4DlVi0/aoNo78+f9zNcvCRASpKQMGdX8CWPboLsuwreGdRG/tlvL1mNs048SPvfnaeuR4GlWyJm6Zt4s3/a52kY9pu8m6Oq3Y/pehanz9jc3CGSfs5/Zr7bUDRTpL1nHMiV9tI6xeUH3OrTRvP1s9yMhT/WP/jK5UaP6bdq4DbrTv2Kekrbt95FMb/tJv23zfkrbJ1OP9Lk6rhNi5fVWS7vdfr/J/qQNc4z1+0q2k/fqXJQwd15I19ar7au5KWDWTSQ5l1cfyjFe+7dom9T5IttO3dobARbp6+3+9W1yDMYudRrqE2mrurkodm0gP+tYz/7W59c8lzZE7/eVto+MPW+19Ui9h9o+pm075DMldRkqcYpX9dmap93O2P2tdc16XNWxN2v9846fdplFPmdyXoh39fN2+bRr+k2dY/res/pza9y2Zds/5203nxd1s0jq034mpy51g1r/nNfWve0ns86h7Tkhx2qO3ZR562234TkBAgQIECBAgAABAgQIECBAgAABAosJCLoXcxo1Vy6aLht056Jze1E2F97bC8e5uJsLv3URNRXNRdU2lBobdCcUqdAu6004kNFLFRJk2wniK5jKPCmpXwUE2XbmScnF7rbumZawINvIxecq/QCmprchT03LYz/wad/bzedD9e2Hd21YUHWdtf/LBhBZf1unWUFeW6ehkDptVt8eMCuUz7ZmhV95ry2zgu52O6lH+l71q1q+H5b0+8KssKSW38pj29f7oUytZ13aqOoz9Jhjss4XswKhNqTJOvrhTBs2pV1yQ0RbcjwnDKoQK+0262aUtk/WOub1q5pn1uOsNmj7deqbevdLa9PvS/1529dtX830IZO2XjmH5lxYpa3brDZpQ+qheRbp6+062vNMG/4NrTv1bEPA/o0js471rbpkO0N9pe0jY85bbX+Ne32GZXttadshfaS9GaHmyz4lDM25J6U9F6xqf2tbQ4/tvgz1s60ce0Prb/tp20f687ZW/bC3ba/cDJD3+6W9uWHoeJzVn9v977dle44e2m79zZK6V2mD7rxfN3MMLZ9l8vdO/U2T1/19z7SUfl94YOrB/aWmeSRAgAABAgQIECBAgAABAgQIECBAYLyAoHu83aZLriLozkYqqG43WCPJcsG2Lrjn/QQQ/QCnlk+AkQv87ejavFejf/vvZX0Jq9qLwpmWICLz5oJ4G1DnvZRcfK5AMhd726A+YXeNpoxPgpe2/ll+1sX19gJ85qvSD49r+m4/tqFS6jJ0Mb+9sF71nbU/7f7PMso65gUQ2V7aNOtKSTslxMxj2irTE3JWmTUqscLpzJc+l+XzL/2yyqzwq96vx1pXlu+Hoe2+ZP7UJ47py+k/CR1qXzItdnmsMissqfe38tgGHOn/NZo2j7XNdWqjefvWjtbOMZk2rLbLfuZfW/pBd7uf2fe8X8vnvZxX0p+qDLVtvdeuq6bNCo/q/XmP7fra4yR9v/1WiJyH8i91q3NRu9+zjsNZ2+4f79VXM3/ei0md64a+CaEN8NK/snzqlmWyfN6vMuSzSF9v69jaZL3tKNVsN/06x1raMcvFpurfhoNZdt6x3oae6SvV1/I8bdXuV9Y11Fey3WXOW61tPxzNNtvSBrCpY9qq2iH1jUP17aFz+ir2t61P/3nbv5c99vrrzut2/f0+0s7fnpv7/bHfXjk35ByRc03ey7FQx1qm5byfx7bM6s/z2jLtUjdhZV1pt/S36sfZbvavLf2+3B4H7TmijoOsoy39fW/fa9eV6UN9u53fcwIECBAgQIAAAQIECBAgQIAAAQIEti4g6N662cJLJDyp35nMQmefffbgSLKECAlgctG65qvn0wmT/+TibjuKqKa3j7kom5C7v2wuzCbMzvRcTO4H3bXe/ntZd390WLu9ep6Rj7m4X6Ud4ZZp7YX/mqd9zEXobKfCg3kX1ysUreWzz7lQvY4lF/RrdFjqNxRuZXp7MTyBQC6cD5VVBBBZb+qVYCr9c15JQJC27fenLJM+1bZ5pvXbovYr/Sqh4axSoetQaJRlEohUH521jkwfGt04KyyZt55Z76WPxq36ac3X9vd1aqOq39Bj9iHt09+Xdt72uO4H3ZkvbVJhVbtcPU+ok/Wnv81q25q3Pa7T39pzVM2z6OO8NmiD3nnrm3cOmrfc0HHRn3/WiOTMt5lp5plVt0X6+jybtFX699DNS9lulaG+MO9YT/vn8yf2s0rONdlu5pkVBi5z3mq/oWCzoDt1XKQdUs+cq/sB7ar2d5bVIvXbyrHX3868PtLOOy/oznyLtFeO9Zy3U99+mdWfN2vLRY7x9tzWD6qz/+nPqf+skvNZ5kvpL98u0//synbrBql2Ps8JECBAgAABAgQIECBAgAABAgQIEBgvIOgeb7fpkqsMurOxhCgZbdYPp3KxOOFDRgD2L7rXchUUZqRgGzpmnbPeqx3MBd8E7e1I37yX7SZ0STjbBrpDQULVvX/xOOFo1pELy3XhuD/CquqRx3Y0V17Pmzfv73Zpf0+1DUTberXh2LwL4asKILLthEqxzLb7JX0k/WmzC/K5iJ91VH/sB5Tzwq92m5sF3Zk3oUq2NRTOJyRL30+9+2VWWNKfb9HXcUvAn/pUadts3dqo6jj0OKsP5BySfcqxWX7xzb9+yTkh7dI/rtMmubEjfSBttlnQnXXkX8pQkNrf7rzXm7VB3s+2hoLXnMtqBO+8bcx7L8dFXPp9NWFejqnNjqvULcv3TbN82iDtMlSqrWKfc+pQ2cwmx3K2PXReyPbTNll/vyxyrM/qK9W3ah3z+sqsPpv6zDtvZdv57EwZ+nyavtH7T5bJv2yzLfV5G4s8n1VWsb+z1p3ps9a/1WOvv43N+kjNv1nQnfnmtVf6cdp+KOTOsrP6c/Z7s7bMsZd5+sd4tpVjI8dWbupIGQqqcxzk76JYtKXOjZlW34QztHwtk+20fxtt9Vsiaj0eCRAgQIAAAQIECBAgQIAAAQIECBCYLSDonm2ztu/kImxdfM+F1/ybd8F9VTuSi7bZdh4TKmS7Wy2pdy5Cp75j1lEX97PdrCOhxU7s+1b3c6/Mn7ZMm1R/GtMmWTbr2Yl+mG2lD6ZUH9qt9k89su0xx8FW+scq2mje9rL+ZY/rLJ9/sUgf2mqbJJSqcHXWCM95+zDmver3eUyd82+Vpfrq2H5abZL2GXNcLrMv1eeWadOh7dd6Y1N9ZWi+zaa168m82+kTg9Q3Zat1buu51WWnG1zgP6nfqttpgc1uaZY4pI55rHNmPLa7LHsMpr75eyWPY84R2X59lXrd1Lfd+2z9BAgQIECAAAECBAgQIECAAAECBA41AUH3odbie3h/c7E5o38rdMjIyIy+VAgQ2LsCbRiUEZcJuhUCBAjsdYH6dobsR759Jt8YoRAgQIAAAQIECBAgQIAAAQIECBAgsFoBQfdqPa1txQIJt/P1oRkFltHc7VeR7tTIzxXvktURIPB3Ajm+M5q7fus7X5k+9NXYwAgQILCXBDISvL4ePaPX8+0zCgECBAgQIECAAAECBAgQIECAAAECqxcQdK/e1BpXKJBgu35DvF1tvgY0oZhCgMDeE8jXlOdfgu78S0kYlJtXtvq153tv79WYAIH9KJBzWcLtnMPyVe1V8s0zbuApDY8ECBAgQIAAAQIECBAgQIAAAQIEVisg6F6tp7WtWKAd7VmrPuqoo7qTTz5ZIFYgHgnsMYGbb755+k0NVe0EQ6eeeurKfye71u+RAAEC2y2Qb5/Jua0t+YmVE044oZ3kOQECBAgQIECAAAECBAgQIECAAAECKxQQdK8Q06pWL5CvNM7F45SM+EzI7XcuV+9sjQR2UiC/XZsRjwm487vcGe1oJPdOtoBtESCwaoF8XXm+qSIlf6/kb5X8zaIQIECAAAECBAgQIECAAAECBAgQILB9AoLu7bO1ZgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDYBgFB9zagWiUBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIbJ+AoHv7bK2ZAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLZBQNC9DahWSYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLbJyDo3j5bayZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBbRAQdG8DqlUSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwPYJrG3Q/aEPf2T79tqaCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGCmwMUXXTjzvXV4Q9C9Dq2gDgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEFgjAUH3yMa48847Ry5pMQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYRuCYY45ZZvFtX3ZtR3QLure97W2AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECgwKC7kGWzScKujc3MgcBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgS2Q0DQPVJV0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgsG0Ct9xyS3f77bd3Z5999kq2cc0113THH398d/LJJ69kfVZCgAABAgQIECBAgAABAgRWJSDoHikp6B4JZzECBAgQIECAAAECBAgQ2BaBhNyvfs1ru0984s7uKV/x5O6cc85ZajtXX31197rf/t3u2GOP6S592lOF3UtpWpgAAQIECBAgQIAAAQIEVi0g6B4pKugeCWcxAgQIECBAgAABAgQIENgWgd/8n6/urr322um6jzzyyO4rvvzJ3bnnjgu7r7rq6u63f+d3u3vuuWe6vrPOOqv7qn946bbU20oJECBAgAABAgQIECBAgMAYAUH3GLXJMoLukXAWI0CAAAECBAgQIECAAIFtEagR3bfeett0/Qm7v/zLv6w779xzt7S9K6+6qvud3/m9AyH3iSeeYET3lgTNTIAAAQIECBAgQIAAAQI7ISDoHqks6B4JZzECBAgQIECAAAECBAgQ2DaBwbD7yZOw+7zFwu4rr5yE3L8r5N62BrJiAgQIECBAgAABAgQIEFiZgKB7JKWgeyScxQgQIECAAAECBAgQIEBgWwWGw+4vnYTd583d7pVXXjkJuV9vJPdcJW8SIECAAAECBAgQIECAwLoICLpHtoSgeyScxQgQIECAAAECBAgQIEBg2wX6YfcRRxzRfflkZPf55w+H3VdckZD797p77713WjdfV77tTWQDBAgQIECAAAECBAgQILCkgKB7JKCgeyScxQgQIECAAAECBAgQIEBgRwSGwu4nP/lLuwvOP/+g7V9+xRXd705Gcgu5D2LxggABAgQIECBAgAABAgTWXEDQPbKBBN0j4SxGgAABAgQIECBAgAABAjsmMBh2f9kk7L7ggbD78ssnIffvCbl3rEFsiAABAgQIECBAgAABAgRWJiDoHkkp6B4JZzECBAgQIECAAAECBAgQ2FGBobD7y770S6Z1+L3X/76R3DvaGjZGgAABAgQIECBAgAABAqsSEHSPlBR0j4SzGAECBAgQIECAAAECBAjsuMBQ2J1K+LryHW8KGyRAgAABAgQIECBAgACBFQkIukdCCrpHwlmMAAECBAgQIECAAAECBHZFoB92VyVOPPGE7tKnPbU7+eSTa5JHAgQIECBAgAABAgQIECCw9gKC7pFNJOgeCWcxAgQIECBAgAABAgQIENg1gX7YLeTetaawYQIECBAgQIAAAQIECBBYUkDQPRJQ0D0SzmIECBAgQIAAAQIECBAgsKsCFXanEkZy72pT2DgBAgQIECBAgAABAgQILCEg6B6JJ+geCWcxAgQIECBAgAABAgQIENh1gYTdKb6ufNebQgUIECBAgAABAgQIECBAYKSAoHsknKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHskoKB7JJzFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsKSAoHtJQIsTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFW4LD7J6Wd4DkBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEFhnAUH3OreOuhEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDABgFB9wYSEwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgnQUE3evcOupGgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAhsEBN0bSEwgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXUWEHSvc+uoGwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhsEBB0byAxgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTWWUDQvc6to24ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsEFA0L2BxAQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQWGcBQfc6t466ESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMAGAUH3BhITCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGCdBQTd69w66kaAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECGwQE3RtITCBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBdRYQdK9z66gbAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECGwQEHRvIDGBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBNZZQNC9zq2jbgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwQUDQvYHEBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYZwFB9zq3jroRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwAYBQfcGEhMIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYJ0FBN3r3DrqRoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIbBATdG0hMIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIF1FhB0r3PrqBsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIbBAQdG8gMYEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE1llA0L3OraNuBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILBBQNC9gcQEAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEFhnAUH3OreOuhEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDABgFB9wYSEwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgnQUE3evcOupGgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAhsEBN0bSEwgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXUWEHSvc+uoGwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhsEBB0byAxgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTWWUDQvc6to24ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsEFA0L2BxAQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQWGcBQfc6t466ESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMAGAUH3BhITCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGCdBQTd69w66kaAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECGwQE3RtITCBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBdRYQdK9z66gbAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECGwQEHRvIDGBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBNZZQNC9zq2jbgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwQUDQvYHEBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYZwFB9zq3jroRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwAYBQfcGEhMIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYJ0FBN3r3DrqRoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIbBATdG0hMIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIF1FhB0r3PrqBsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIbBAQdG8gMYEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE1llgzwTdd955Z3frrbd1d3ziE93dd9+9zqbqRoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgbUXOOqoo7rjjj22O/HEE7pjjjlm7evbVnBPBN3XX39Dd8vHP97W23MCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQWJHAySed1J1++kNWtLbtX83aB93XXPvR7o477phKnHLKyd0JJ5zQHT25s+Cwww7bfh1bIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwD4UuP/++7u7Jt+kfdttt3U333zLdA+PO+647uyzztwTe7vWQXeN5D7yyCO7M898aHfM0UfvCVSVJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwF4RuPOuu7qPfvS67p577un2ysjutQ2685vcV119zbTtzz33HCH3XjkK1JMAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgT0nkLD7qquuntb73HPOXvvf7F7boLtGc+fryh9y2ml7riOoMAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBPaSwA033jj9GvO9MKp7bYPuK668qrt78p3wRnPvpa6vrgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI7FWBGtV91FFHdeefd+5a78baBt0f+vBHpnAXXfiw7rDDDltrRJUjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAXhe4//77uw9/5LLpblx80YVrvTtrH3SvO+Bat67KESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYAsCNSB53XNaQfcWGtWsBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ2M8Cgu4lW4cuP9QAAEAASURBVHevAC65mxYnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDA2gjslZzWiO616TIqQoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgd0VEHQv6b9XAJfcTYsTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgbQT2Sk5rRPfadBkVIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwO4KCLqX9N8rgEvupsUJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwNgJ7Jac1onttuoyKECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYHcFBN1L+u8VwCV30+IECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYG4G9ktMa0b02XUZFCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsLsCgu4l/fcK4JK7aXECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAisjcBeyWmN6F6bLqMiBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ2F0BQfeS/nsFcMndtDgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTWRmCv5LRGdK9Nl1ERAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI7K6AoHtJ/70CuORuWpwAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJrI7BXclojutemy6gIAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEdldA0L2k/14BXHI3LU6AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIG1EdgrOa0R3WvTZVSEAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECuysg6F7Sf68ALrmb+2bxD3/4I93b3/HO7oMf/JvuY9df39133337Zt/sCAECBAgQIECAAAECBA5lgcMPP7w74/TTu0c+8hHdZ3/WZ3QXXXThocxh3wkQIECAAAECBAgQILDvBfZKTmtE977vitu/g6/81Vd1f/yWt27/hmyBAAECBAgQIECAAAECBHZd4Amf+znd137NV+96PVSAAAECBAgQIECAAAECBLZHQNC9pOteAVxyN/f84v/5FT/d/fVfv3/P74cdIECAAAECBAgQIECAAIHFBS655NHdtz3zWxdfwJwECBAgQIAAAQIEDhGBe+65p7vzrru6PObf/ffff4jsud3cqsARRxzRHXXkkd2xxx7bHTl5XKeyV3JaI7rXqdfssboYyb3HGkx1CRAgQIAAAQIECBAgsEIBI7tXiGlVBAgQIECAAAEC+0Lgjjvu6G67/fZ9sS92YmcFjj3mmO7EE0/c2Y3O2Zqgew7OIm/tFcBF9mU/zpPf5P7J//Cf9uOu2ScCBAgQIECAAAECBAgQWFDge77rO/1m94JWZiNAgAABAgQIENjfAjffckt39913T3cyI3SPOuqo7sjJiN2M2lU2Cnzs+uunE884/fSNbx4CUzLS/957752O/s8NEimHH35495DTTluLvd8rOa0R3WvRXfZeJYzm3nttpsYECBAgQIAAAQIECBBYtYBR3asWtT4CBAgQIECAAIG9KFAjuY+YBJUnnHBCd/TRR+/F3djROh/qQXeLna+4z40SCb/XZWS3oLttoRHP9wrgiF3bF4u8+Edf1n30uuv2xb7YCQIECBAgQIAAAQIECBAYJ3DmQx/aPe+5PzhuYUsRIECAAAECBAgQ2AcCCSlvuvnm6Z6cdNJJ3TFC7oVaVdB9MFPbj0495ZRd/83uvZLTGtF9cD/yakGB7/uB53T33XffgnObjQABAgQIECBAgAABAgT2o0C+Wu/lP/6S/bhr9okAAQIECBAgQIDAQgL5Te6M6D5u8nXlGc2tLCYg6N7oVH1pHUZ1C7o3ts+WpuwVwC3t1D6a+Xu+71n7aG/sCgECBAgQIECAAAECBAiMFfjJl//Y2EUtR4AAAQIECBAgQGDPC9Rvc588Gc3tK8sXb05B90arGtWd33U/7dRTN86wg1P2Sk5rRPcOdor9tClB935qTftCgAABAgQIECBAgACB8QKC7vF2liRAgAABAgQIENj7AtffcMP0t5UTTCagVBYTEHRvdMpvdKc/pZxx+ukbZ9jBKYLuJbH3CuCSu7lnFxd079mmU3ECBAgQIECAAAECBAisVEDQvVJOKyNAgAABAgQIENhjAgLbcQ3GbdhtXVz2Sk5rRPdwPzJ1EwFB9yZA3iZAgAABAgQIECBAgMAhIiDoPkQa2m4SIECAAAECBAgMCqxLMDlYuTWeyG24cdbFRdA93D4LT90rgAvv0D6bUdC9zxrU7hAgQIAAAQIECBAgQGCkgKB7JJzFCBAgQIAAAQIE9oXATgWT73rXu7p3vvOd3d13372U21FHHdV9xmd8Rve4xz1uqfUsu/BOuS1bz51efl1c9kpOa0T3TvfQfbI9Qfc+aUi7QYAAAQIECBAgQIAAgSUFBN1LAlqcAAECBAgQIEBgTwvsVDD5Mz/zM0uH3AWdsPsZz3hGvdyVx51y25WdW2Kj6+Ii6F6iEbPoXgFccjf37OKC7j3bdCpOgAABAgQIECBAgACBlQoIulfKaWUECBAgQIAAAQJ7TGCngslXvOIVB8k8/elP74455piDps16ceedd3Y/+7M/e9Dbz3zmMw96vdMvdsptp/dr2e2ti8teyWmN6F6gx73lLW/pPv7xj3dPetKTuqOPPnqBJfb/LILu/d/G9pAAAQIECBAgQIAAAQKLCAi6F1EyDwECBAgQIECAwH4V2KlgUtC9X3vQwfu1U/3p4K1ufCXo3miypSnrAvimN72pe//73z+t++mnn95deumlwu6Jxk4E3Q9/+MXdox/1yKn9n//5u7prrr12S31oP8187LHHdk/6gs+fuUsf+9j13Z+8809nvu8NAgQI9AWOOOKI7ou/6End4Ycf3t14003d2972jv4s++b1Ez//87rjjz9ucH/uvffe7vW//8bB9/bixEs+6dHdRRddOK36O/7knd3119+wF3djR+r8pV/yxd0RRxw+vZnxf//xW3dkm/2NnHbaqd3jP/uzppMvu/yK7j3veW9/lkPm9WGHHdZ9yRd/0bRNbr75lu4tb33bIbPvh/qOHkqfRzvR1rvlKejeida1DQIECBAgQIAAgXUV2Klgsh90L+txqI/ovv/++7vLL7+8e9/73tfdeOON3a233todd9xxXbLA888/v7vkkkum106Xdd7q8jvVnzar17rktJvV04juOUIJuBN0t0XY/YDGTgTd3/bMb52cSB493eAb/+B/db/16te2TXFIPU9o8T3f9Z0z9/m2227vnv+CF8583xsEtirw+Md/Vvcpn/yY7t3v/svuHX+yOzdRJPR46lO+vHvoGWd0b/iDN3WXXXb5VnfD/HMETj755O5FL/yh6Rz56qJnP/cFc+be22+9/MdfMveP0p34TNspwXxWVNCdz818fiobBY468sjuZT/24ukbd99zT/eDz3rexplGTon/F37BE7vrPvax7jWv/e25a3nC5z6++9qv+cfTea6++prupS97+dz59/ObuanvJS9+0XQX77rr7u5Zz3n+vtrdE088ofvKS5822af7u//5W6/tbrvttn21f8vszKH0ebSM06LL7panoHvRFjIfAQIECBAgQIDAfhTYqWBS0L263pNvcX7961/ffWxy/WZWOfXUU7snPOEJ09B71jzbMX2n+tNmdRd0bya0yfvrAHjVVVd1r3nNazbUVNi9MyO6Bd0Pdj1B94MWnm2/wBlnnN790POefWBD//aHX9Jdf8POjwrNaMunPuXJ03rceeddkyD2gVD2QMU8WUpgty6EL1XpkQsLukfC7ePFtjPo/rGX/PCBb/959Wte173hjW+aKSnofpBmvwfd3/Pd/6q76MKHTXf4Qx/6cPfv/+NPPbjzh/izQ+nzaCeaerc8Bd070bq2QYAAAQIECBAgsK4COxVMrjro/qZv+qbpCObdct0pt/7+3TC51v3qV7+6y+CfzUq+DfMpT3lKd+65524268re3y2X/g6sQ07br9PQayO6h1SaaUOjuvP2oR5278ToN0H3gx0xX0H4KZ/ymAcnTJ6dMfn6jK+89KnTaUZ0H0RzyL742q/56uko7AD8yit/vXvPe/96lMXpD3lI94Ifes6BZV/y0p/Y0k8HfO/kYv6pp57S5atffvjFP9bdMxktOaY8+cu+pPuKL/+y6aJZxw+scMTlmPrst2V260L4bjg++tGP6o499piDNv3N//wbD4zy3onPtIM2vo0vjOheDHd7g+4fmQTdR00r8vrff0P32tf97sxKCbofpNnvQXc+Gy/8u6D7iiuv7H78J/79gzt/iD87lD6PdqKpd8tT0L0TrWsbBAgQIECAAAEC6yqwU8HkqoPuk046afpzvSeeeOKu0O6UW7tzd999d/eqV72qu/nmm9vJc58fc8wx3Vd/9Vd38dqJshsuQ/sl6B5S2cK0dQIUdm9suJ0IBQTdG93bKeecfXb37Gd933SSoLuVOXSf/8D3fffka1TOmwL8xm/+Vve/3vxHozG+4eu/dvLTAZ/Uvfc9f9390q/86pbWk68DToiUklHYGY09ppxwwvHdt/yLb+5Of8hp3evf8AfdH/3RH49ZjWVmCOzWhfAZ1dnxyT/xsh+d/BbwEdPt7sRn2k7toKB7MentDLrzm/Bf/EVfMP199Ff89M90n/jEJ2ZWStD9IM1+D7offvFF3dd93T/JN5d3v/zKX+syqlt5QOBQ/zxadT/YLU9B96pb0voIECBAgAABAgT2ksBOBZNbDbpn/QZ3u57dDLt3yq3tS+9973u7P/zDP2wnTZ/na8of+tCHdtddd1130003bXj/Mz/zM7tP//RP3zB9OybshsvQfqxTTjtUv5pmRHdJbPIo7D4YaCdCAUH3web9V4LuvojXqwy6l9FcVdC9TB0su7nAbl0I37xmOzOHoHtnnNd1K9sZdG9lnwXdD2rt96D7wT31rC9wqH8e9T2Wfb1bnoLuZVvO8gQIECBAgAABAntZYKeCyTagXsRrs6D7rLPO6q699trpSOVLL7202+mR3Tvl1lrdd9993dve9rbu3e9+93TyYYcd1j3pSU/qHvWoRx2Y7Z3vfGeXf23JV5c/7WlPaydt2/PdcBnaGUH3kMoWpq0joLD7wQZcl6A7F3JO/ruvi8hXTlz70Y8+WMnes/yWwgUXnN9dfNGF3WmnndZ9dDLvZZdd0V1+xRW9OWe/zOi/T/qkR3ePftQju1tuuaV73/s/0F155VWTr8Q9dvpV4lnyo9d9tLvrrrtnriTzftqnPbY795xzJnW4rnv3X/7VdF35qucTTzixu+/++7qrrrp65vL1xrJBd07gsXjMYz6pu/32O7r3TEbuzvOr7a768fjjjpt+Lft555032e+rJqYf3NLXhqQ++Q3zT37MJdNRc+/+y/dM77qaVc9+W+VrRz77sz6zO/7447v3Tr7q+4N/87fTRf/+3/u07uKLL5yMyLux+/O/eNeGOp144gndqaecOp33qquv7vIB+ahJv8iIraOPPrr7q/e8t/vbv/3QrGoMTk/wcsmkPR5+8cXTrwr/q796T3frrbcNzpuJGfF82qmnHXj/6f/8n03uOjtj+vrNf/hH3dvffvCH8axjJHeqHTOp81C59957u6uvuWborQPTTjvt1O6E40848Pp7v+dfHRgp+x8mv0HaPx4+fuutGzzTH8+b8zsnt99xe3fDDTce2MYiT+LzqZ/6Kd3Zkz/Y0hZ//dfv6+7e5GvUzz33nO7www7vbr3t1smdew98fU361yWT4z7tevnlV3Tv/8AHu9tum90ubd3y9cVpz4c97ILpeSLniw99+MNb3pd2nWOep43Sp9Pfc97KSMLcrfmiFz7wu+f5PZpnP/cFc1e9Fc/qE0N9rvpb/1zZ9ucrJ+eCfPV9jon8wZ2SP/AyMjZ95bGP/ZTJb91eOOlbd3Uf/shHur/54N9u2rb9nVsm6D5yUq98FjzyEQ+fHqt/OTnvLNonUo9VfB5lHemXOe9cM/kfkvdMzjkf//it3V4d0Z1jJOfxtPsHPvg33Yc//JHpebXfbqt6PRR05/PhMz7970/Po/mmlA996EMHPhNmbfeUU07pTprz9V5pm3k/37BI0N1+bqUedXzMqtM6fL6nDmnT/Cb1QyY/h5G/uT78kcumfzPNqnf28yUvftH07XxuPOs5z58+z3oeM/mGkZy/cg7+wAc/OPezsV3/SSed2D3i4Q+f1iXnufx99TeTz/nb77ijnW3weete55/MmPP64x772O6cc87uco67/Iorp8df+m6Vdtma1j7eeNONk3PG7e2kDc9X/Xm0ir9hN1RyxIRVfB5t9e+l+ty54xN3TL9p4ZJLHj3pU5d0N954Y/eWt75t+s0zqdfjP/uzpp/1+Vsw59R55Zhjjp78nXjR9LPoqKOOmvatzT7ft+MzbRWe8/ZzK+8JureiZV4CBAgQIECAAIH9JrBTweSqg+5nPOMZ3ete97pp2J2QO2H3Tn09d/rATrkN9bdc93nzm988HaX92Mn/57clI7pf+cpXtpOmNwF8wzd8w0HTtuvFbrq0+7SOOW1bv3puRHdJLPgo7H4Aah2C7lzc/45v+5fT0CO1uvbaj3Y/+tIf39CSudh66dOe0n3BEz/vQPjWzpQLjb/1mtdO7uJ5Rzt5w/NnfPM3TS5sfuqB7dUM199ww/SCfC6Op7zmtb/d/f4b/qDePvCYQDcBYC62taUu6p8yCe3POuvM6Vsv+H9eNA0s2vn6z5cJur/mn/yj7gmf+zkb9iVh7Wtf9zvdG974pv7mVv76kz/5Md3Tv+kbpheu+yu/YXLh8Wf++/+YXtDuv1evE/J86794+iSov2TDfiSczT687rc3/i7qP/nH/6j7vH/wudPV/O0k7Esw3ZaE3eeff/7kA/3B3yVJG/38L/xy984//bMDs37Xv/r27uEPv3j6Or+/mq+pzYXttmS5P3jTm7vfevVr28kbnme5b/u/vnUaCPTfzIX5//rTPzMYtvyLZ/zzaZ/sLzPrderzvd//7A1vv/zHX3Lgt4o3vDmZsNnx/qJ/84IDN5wMLd+fdv31N3T/9kdectDk/G5pfr90Vslx+vwXvHDW2wdNz7nhWyY2/fbITAnL/9+f+s+DQXPmr5Dl6quv6X7113+j++anf+OGfYvjW9769u5Xf+1V01DuoI3/3YtcAP+nX/c13d/7tMdt6J+ZJQa/8Iu/3KUPbmc5f3IDyXd+xzO74ybnn7bUsZ5zY8q8oHuM579+wXO7h0xuKBrqc//uJ146Ncl5MufLKt8yOZ4fO7kxISW/XZvfsP2CJ35+94++6tLptDf+wf+a3CBxy+TOya848NX40zcm/0mQ+Gu//pvdW9/29pq06eOYoDs3V+XYP+OM0zesP4Y/+3O/0L1ncg6ZVVb1efRN3/j13af//b+3oW8lxMvNUmn3lJx74rauJcHbtz3zW7pHPvIRG/Ylfed///Fbu19/1W/OPM6W2a9+0P2uv3j35H9sNprefvvt3c/9/C9ObpR5/+DmnvucH+jOOvOBz+6hGX75V35tbr/cLOjOTVXPf+6zDhzDuVnnhf/mR2beWLEOn+9P/rIvmXwmfuE0EO6b5Dj5rVe/btK2b+m/NT1n1zk4QXdulMp5IUFeW9I33vb2d3S/8spfn9k3cgzkOKm/q/rLv+99H+h+/hd/aW5g/k8nXzP+OY//7OmiOV+9851/1n37t//LDZ8JmeH5L/g3B7XJP/7qr+o+//Oe0G72oOf5W+O//Nf/dtC09sUqP4+y3mX/hm3rNvb5Kj6Pxv69VH/n3HHHHZMbaO6f3ihY+5G/Gd86+X+Af/CEz6lJ08fcjPril7zsoGl5kZuy/tnXf93g3595P/8/8gu/9CuTm2kvz8uDyio/01bheVDlVvBC0L0CRKsgQIAAAQIECBDYswI7FUyuOujOiO/cxL1bYfdOuc3qWLnGkOt1bcl1xny1+Qc+8IF2cmdE90Eca/VC0D2iOYTdmwdfI1g3LDLvq8v7IXfCq1yMGho1lfAsIdpm5Rd/6ZXd29/xJ4OzPefZ3z8dFTr4Zm9iguLX//4bD5p6+mQ007N+8Psmoe7wqNmDZp68+Ncv/OHpKO/+9Pb1mKA7FwifO9mXjECbV3JB/z+/4qfnzbLUe//wK5/WfeGTnrjpOv7///az0xHv/Rkz+v37v/e7Dwqj+/Pk9RWTUVYv/8n/eNCowIQA/+AJDwTdQ8vMmpaRo896zgOjXzPPd/3f37EhJJ+1bEbtZ1+GysUXX9R9x7c/c0Nw186bD9wEgv0bEPZT0H3RZNR0RqLOKosG3U99ypO7L/2SL561mun0hLz/4+d/qfuzP/+Lg+Zrg4Vc+M7NFP0/dNoFEtT86q/9Rjtp+jzL/dDznz0Neje82UxIu/6nn/ovgzcxNLONfprQOH1k3j7UymcF3WM9v/Vbvrn71E/55OnqX/pjLz/wrQA5d3/npL+nZHT7y37iJ6fP858fet6zpwFyXOqGjDYUuHPyW++bnUP/0//3iskfoR88sM55T7YadGe0cUK3+l3vWet+8x/+7+5Vv/E/B99exefRD37/93TnnXfu4Pr7E9c56M7n4vd973cdFDj165/XH/vY9d0Pv/ilQ28tNa0NujdbUfrkT6VvTUaa98vznvOD3ZlnHnwDWzvPMkH3CSdMQu7nPavLjXIp+RvnpS/7d4PfWrIun+/f+M/+afeZn7H571X1b3TJ/rXn4JinzDt/5caW+PZLPk++e/IZPW/ZLJNv5nnhi1580N8I7bq+fnKz0uMf/1nTSQktzz//vMGbwtpzVi3f3lRX09rHrQTdy3weZZvL/g3b1nvs81V8Hl188fi/lyro3mr9+3/Tp4++8F8/b/BGunbd+TvjR370x6Y3tbXTV/WZtgrPtl6rei7oXpWk9RAgQIAAAQIECOxFgZ0KbLcj6I73boXdO+W2aJ+6fvKNkm94wxsGf6P7iU98YnfJ5NvBdqKsi4sR3Uu29roDHuph92YjPJds/unis4LuR01GfiUYrAuYN998c/fDP/LSwa+t/aIv/ILuKy996oHqZNT277/xTd1tk69OzojijBTMheSUXJT6/h987obRQV/8RU+ajgifzjT5T0Zq5GsOE9I87nGPnY4ASbBVpX9RLNNzwfXiiy+aPHug5OsQ//TP/mLyleG3T0f8PGYSoLRlu4LuBEwJmlJyYTaBzJ/8yZ92J59ycvekyYj3ei/v/7f//nPdu971l3m60pIR1AmJq1x33XXdH/7RW7r3T75O+eEPv2gyWvMpB13U/z/s3QW8FOX+x/GfdAkCCggGB8G+FmKgXrFAEQtsr4gFGH876A4JA68oCordYnBNDCywMAAVW0BKBaQRUP/zG3iGZ2dne845O2c/z33Jzk7Pe+bs7p3vPM/Tp+/AuCZGr7v2Sq/Goh6Hno9PP/1cqjm1bFoedJDzhbOzWb1ba8wOI+2gW8NrfcBBi9b8MgGWjn/YqW1bxWkm9awzT/eutf4DhzjNXP7hzu8PurUZ1FdfneQ2XannU2vp6Q1RU0Y5NdN+8tXe1Wt48KB+3vFqkKfXj1poU63HHdvaq8mmx9mzV78YC31gwQ5YtPaZhkdaPvjgI5n22efusPlHj2u203Ssv7Q7/jin2fyNfwdmmt7cN39jqf7etWnwLWtuaRZ1a2gay7H33u82L+1NdAaWO7Vy/c3kaw2pE45va88mNZxa9SYsTSfo3t6piX/tNVd469AHYPThAA0odt99V6eGYSu3SVKdQYOD7j37xDSrrufL1CY0K9m4jrdkvlPDW7sbaHtca+8zQ89Jrz5aiy+2+dn2p5wk/z7sEHcVOo/WStVrdMP6De5nRuvWR3kPNug5v7H75gcozHbDeNVjMdegfr599NEn8uXXX8uOO+zgBFH7ylZbba4lGRR05+J56KEt5VSnRqOW51/4n9uygQ7bNSQ1tLvuhh462i3Dbxrs1gDV8Ek/A7XYoYA7wvnnxx+dZoWcz66Vzuf4rk5zxvrQjLne7GXN/IleMwm6df03DR3onTetbfrmW5Plyy+/dv5Wt3P24fCYWt76AIM/FA3j+6jV4YfJySdtrOGux2W+j7TJcq2NrGG8+bvV6fkcdHe/0akJvakVE/07mTL1Q/ncefhE/zYPd45zb+f71ZT33p/q1NiPf6jETM/mNSjo1s9HrQG/YsUK2XfffdzWP4ynfl/3cD6D/UVr/Grz3HbZZ5+9vL+9bINuf8itLtrSQaKuJPLh+72oqLH7e8dYaDcJr772uvt/DrULhxNOaCvaeo0pg4eOiAntAz+DnRZe9Jzog2vbbddIjj+ujVe7Xa+bPv0GxrWAM8z5W9VmzrXo34a2CjDrm2+drj62kmOOOdJtDcHsg153Tz71jHkb82oH3fYEffhCW/P52zkn+j2srSjoubGL/tbRblHsUuSMM9d8JkG3WUc230dh/IY128/lNdfvo1x/L9lBt7bO8+57U+TYNse43T+Y49Ia+/oAmz6MaR6Q1c+EW0fdYWZxW/Qwrfno36S2HKQPyVaoUMFt9vyYo4/0vo+CHtIJ6zstV0/vgEIeIOgOGZTVIYAAAggggAACCERKoKSCyeIKuhV7/frNNbu1+fL27dt7//+6uE5GSbmls/+zZs2S999/37035Z9/R6cLxdatW8fcd/PPE+b7fHHJ95zWmFOj20hk8Zos7O7QoUMWa4zOIqmCrzCOJCjo9ofcy52b0YOdml4aFgWV66+72un3d1t3UlDtS72pOmhAH/cGlc4UFE7YN0w1uNB57KI166675krvQ84fdNetW0d69+zmLWKHPmakBjV6Y82U4gi6NQDpfPEF7ib05vDIW0bF9ZV55ulOM50HbWymM5PAyOx3Oq9qoSZa/DcQdZwGSnpOTFPL/qBG+6TVZr616HHc4zT9+fWsb9z35h8NiLXpVC06jx1G2kG3hqAT//eSO5+uU9et5ZVXJ7n/6bBdA9OuYW4H3Rq4aa0wu0UBrYHX06mhqgGulnlOk8IjRt7qDpt/7NBP+x7u1WdAzDr0xq7WFjT9bn/hNK07/oGHzOJxr3odahCg5dnnXpC333kvbp50R2QSAvrXOWL4EC8Q1BA30d+nfzn/+0xbLTA1gnU92vT4cMdbz78p2gSwzmPC30+dBwEefOhRM9kdbwfd2s+6NhtvF+2bdejgAd6NbG3SWB+ysIu9H0FdGejDCFrj2wRo2oy7NmUeZrEDUTXQG/V2M6q67W43XOuFLkFBt30cmXpq+NO/b0/3kKbPmCn3jX/QHbb//nXETcNudvuXtkNHfRDonk3u/lBAm4vXBwfsot8Ll13axRultcHt8+5N8A1kco2ffloH9+EVXYVez9qCiD5kZYp66meFPqCiRaf17T/YTHZfS+L7aA+nFr126WCuLf/nZ8wOleIbfRhJv+O16LkKatnA/l4Muj5z3X37mtN16YNwjz3xVMxqd965mdtFihmpTdP7W4Iw0+zXG5zfHtq/spZsgu6gkPvW2+5wm/O3t2OG8+X73f470f8jNOr20WYX3Vd9KHBAv15un1Y6QkPwl195zZvHH3SrtZrbxf8ZrM2X64NEpuiDV1qDWYsGkT169Y37Dmp/yonOw0iHuvMEhZHuBOcff9CtD7Tdfvud7meWmSeTV33wUh940ZJp0J3t91Guv2EzOb5E84bxfZTr7yUTdOs1oQ+1arE/A7RLgOs3PXilrQb167Px+0v7ZNPfd6bY3xvatP6PP/1sJrmvGoJr9xZa9LPNtE7ijnD+CeM7LQxPsz9hvxJ0hy3K+hBAAAEEEEAAAQSiJFBSwWRYQff48ePdYDuZ8R577CGHHLKxMk+y+XKZVlJuqfZR++ueNGlS3Gx6L0P7727RokVgK29xC4Q0Im9cnPs7Woqc1vPyuRB053B2tBmDiRMnxtVWrFSpknTq1CmHNef/oqURdGv4oWGGuYG/atUqp5/fYaIBYaLyr3/t4TU5rk1cas0ef7EDS39TmkVFm2sn6Q2r7j37Bm7vSqfJZfPH7g+67WA1WXg8/KZBXm3T4gi6r7nq/7wQJuiGvrroB/eIYYO9EK9bjz6Bx6vzZlM04Ovda2Por57aF/nKlaviVmUHHFoLa8zd47x57OPQJoq1qeKgMnhgX6/mrfaTrQ8YaLHPx6TX33BqUL/qjte+mLU/ZS0Tnn3BqTG6MSS2A3A71LSvG/85d1fi/KPr0/VqCbrhaQfCd44Z69bkdme2/tE+GLUGuxa91vWcJCqFHHTb15b62LXvbS+tYXf2Wae7o+wb3jrCDll0mtY21vPmL/o5pOGqFq1pqGGiXTTgNd0D2NeSPY8GLVpjWsuTT01IWEvTXiaT4Z7d9QGJbdxFEj0gYd/M9weJYXiOdB540FpudpBk+uc2x2I+c+3g89nnJjoPabzrzmKHAgsXLXKDcbOs/WrXbtMHHLSv6lTFDixSfaeZ2ua6zkTnVB+kGNi/j/cdpc2ya8sfpuT6fdSkaHNrGMm+j+yHc4or6NYHeSpX2Vhj1hxfolfTCoY93d7HZIGfHdKF/X1kh1z+zwJ7X7VLBW0KW8usWel165FL0H3HnWPch6RMc+XaGoO2CBLUGofZT/t7Mdvvd/1tpZ8J6ZQ1a9YG/jboeO7ZXm3pRN/PB7RoLgcfdKC7mQ8++th9wMBs0/4M9rf4YObR1y7OQ3u7OQ/vafE/xKgPl7U8+CB32m+//S6ffzHdHbb/2dJpLUT/VrUk244ddOvfnD68or/jsi3ZBt3Zfh8VFeX+G1aPNde/91y/j3Qfcv29ZIJu//k2waz23a2/8bXob2GdX4s+UKstC5miD0hoFxpay2Hy2xu/p8w08zps6CB3Hn0/9KaRMS3YhPGdFoan2dewX41n2OtlfQgggAACCCCAAAIIREGgpILJsILu6dOny7Rp05KG3TVq1JCzzz67WPlLyi3VQUyYMMG5h/l7zGx6n6Jt27ZOK45bx4wviTf54kKN7hzPdr4DJgq59bC1CYPGjRvnKJDfi6cKBcLYe7tG98wvv3KbZNWbT1q0Vo3W5PY3GZxqu1pLquG2Ddzmnk2zlgc7NZhNIOTvW/VwpznvU04+0V2tvz9Ze1tae8M0ZegPPe1QzIQ69rJm2L6hXxxB95BBThPZ1TbWLtZQ1f/Bbfbj0q6dvSZ4NWDWoDms0mL//eScs890V/frr7+5tSKD1q03VU3Ncq2F+v0PP3qzaW1v/ZLVov1v27VUvZmcAW2O++ijjnBH2TfciyPoDqq1a/bFvjmrN8lNLVCtlabhmRa9ia19OSYqWrNWr/2gsNxeppCDbg1Pzj7rDJdDm3MeOmykTRMzbAJYHWmHZ3bIok1T39BtY42umIWdN3bNsi+mO7Xs738oZhZtBr/5fvu64/ScaY3vSa+/mXUtwJiVp/nG/jzxNw9sVlHTaUJYa1dq8QfdYXiapqlNTTm7dqyGBzWdJpB+mTfPbfLX/nu1H1KwQ4GgFiDMsdg1xdOtdZtu0K2tTOi8WpIFojrd7j87USCu82nJ9PvIrsVn3DauKfZfO5gtrqC7T+/uKfugN3tlau2b9/pqf47f5tT6/XnT06H2PDqsNdTr19vG/ezTFio09A2r2EG3uUaD1q1Nk+vfvBYN7fX6TFWyDbr1e7Fatared5x+fvx39Bi3uf5k2wzj+303pxuALp0vTLYZb1qi30Pab/CFF5znzafddWit7XR/R6T7GazN9+vfg5ZUDx/o3+82zv8h3db57adBvnlY0nRrk+xv2g66v/32O9HfTrmUbIPubL+PwvgNq8eb6997rt9HYfxeCivo9p//OnVqS4MGDZxrrK73kOixbVq73XDovP7fqWF8p+Xq6T+GMN8TdIepyboQQAABBBBAAAEEoiZQUsFkWEF3Kl+znc6dO6eaNafpJeWWaifHjRsXd9+pXbt2Tot9DVMtWizT88Ul33Nag0+NbiORwWuykLtVq1ay8847Z7C2aM5a0kG3X0lr22qIkE7RmhcdnL5itXat1rZPVvxBd0yglaTZ6GRBd7oBjH3jqjiCbn9NymQOZpo26/3Gm5PN25xf9Twc5vTdq0WbG7/7nnszXqe5WakLJmuiWGtPXnj+xhvuS5YulQEDNwZVJR109+vTw+sLeey99zt9+n7lHrP2Ed/5ovMzPn6tBR/UMoGuqJCDbvu86oMx4xzrRMWuca1N33/19Sx31nRDFrsP7hkzvpR7xz8Qsyn9zNFm6zXItYsGKtq/7vTpM+U9p39QfWCnuIr5e0/2cESyoDsMT+3f/sAD9ncPUR8oOMUJp7Tvd61Rp7Us92++n1eb8tJLOsvOzZrGBcnphgImVNeNPfTwY6J9sKYq6QbdRUWba0VqNwXaxUCiYtdo/fiTT+WRRx+PmTWX7yNticD0+RvUpLPZUBSCbvtzXJsR1r+Nki7pBt36IJzWoNRi1/pMtr/ZBt3+dSZ7IMye1/y92+NSDfu/3+3mz1MtG9QVh1lGv9f0+80u+jmk3TNo4P3uu+/H1HC150v3M/j4tm3kmKOPchfVfsDvCgig9SGJdm2PdQNuexv+4XSD7mR/c/51JnpfHEF3su+jMH7D6rHkGnSb6zPb76Mwfi+Zz5xca3SrR/169aRDh5Ol6U5NUjZbl23Qnew7LVdPPYbiKgTdxSXLehFAAAEEEEAAAQSiIFBSwaQJoNM1yTaoNtvJdvl096+k3FLtz5NPPinafZUpWotb+ygvrZIvLgTdOV4B+QpIyL3xxJZ20K03yzQkTVVLSGvy9Hb6wt1qq61irkhdfoNzY/2fv/+RihUreDV8/EG31kzSGkpapkz9wG1iOGZFm94kC7q1eUxtJlNLUH9+m1YhxRl02/3lmu2l8xrULHM6yyWax24e/KOPp8mjjz2RaNaE481NtGQ3THVhu59Ou8lvO8ALq+nyZDW67ZuVT094zg04df/0Jr3erM+0DBt+S8Jmrgs56NaHGvThBi0ffPCRPP7k0wlp7b6S7b7M0w1ZkgULZqNaA63DKSc7/bc0D7wRrtevhr0ayoZZS1W3rzUW9Ua4Fl33Ndd1c4f9/yQLusPw3Huvf8n5nc51N3vf/Q/KSSe2E20SXWuDassXnZ3mh7Vo066XXnKx29z7b7/95rRuMMIdr//kQ9CttfO1lr4Wuxl2d4TvnxPatZWjjmzljv3u+x/cvqfNLLl+H9kB4rvOgxLPOJ8nQaUkgu5999nb6989aB/scW+88ZZojWm7pPs5bi8T9nC6QbfWvNeuMLSsW7fOaelhYysIyfYnrKBbt6HNIz/3/MSEmwvr+12P0zyIlnBjmybMmfuLaHcyiYp2P3Jsm2Oc5puDm7ef+8svct/4B90a8vY60v0MThV02y1S2OvXUPuvvza2CqCf0VrKctAdxm9YNcrl7z2M76Mwfi+FFXRv7dTc1t91+nluF/2u3bBh4wM75trS6WEH3WF42vsd9rD5bA97vawPAQQQQAABBBBAAIEoCJRUMGkC6HRNsg2qzXayXT7d/Sspt3T3J1/myxeXfM1p/eeJGt1+kSTvCbk345RG0L169Wr3xpK5cao3JwcMuslrCnrz3m0esoMBnV+DbG1G0+7X2w50/EH3aae2l0NaHuSucPqMme6N2c1r3zyULOi2+9Kz+3jevPTGoeIMuu0b+rq1V16d5N984PvPP58eanPLGnTpDXAtiWphBe6INdKuhakBXqKQcK+99pQLOnV0l1y8ZIkMdK4VLSUddPd3moau5TQRrUUfztCa7Frs5nC12WjtRzydEhQameUKOei2w+cvnQBm7LjxhiXu1a7RbTfPn27IYm8rqEa3vUFtcl4fltnfaba/SVFjr994M88vvzhNd98yyrwN7dXU+NIVJvq8ThZ028eYrafd3Ox77091P0v1Jr124aBBtwYP5r1+Lmhw4K8FnQ9B9w47bC/aB7KWVatWSc/e/d3hoH/O63iOGwrptA8/+kQee/xJb7Zcv4/OPP1Ur0uHRP2u68ZKIuj2DirLAftz/PobesQF4VmuNqPF7O/FZE2X2w9NlUSNbv1OW7JkqdeFiB6UPiiiLUEEFfs4dHppfb8H7Zv2bX7QAS1k512aSW3ngUP9ezdFv/f0N5z+TZmS7mdwsqBbP9f0M95sS5v5f+65iTFdoOj2TCBXloPuMH7DmnOTy2uu30dh/F4KK+jWh170oRAteu3+78VXnM/6j2N+i9oPuIYddOt2c/XUdRRXMX9XxbV+1osAAggggAACCCCAQD4LlFQwaQLodC2yDarNdrJdPt39Kym3VPvz6KOPirbkaEpJ9E9uthX0mi8uBN1BZyeDcfkGSMgde/ISBSexc+X2zu6jWz9kBg0ZLlvVqiU33nCNdwNz+fLl0m/AkJgbTPZW7fDYf7PJzJcs6NaQW28Ualm4aJFoX6NBJVnQ3bXLRbLrLhubs9c+RrUGaVCx97U4mi7X/qBNLZOgPlOD9inscVorSIMgLdqM6cDBG8PnTLZj30C8w+m71O6/215PolC9pINuO9DRgMzc1Ne+Qvv12dgHdLIb7fYxpRou5KA7k1q39jnRfri1/1Mt6YYsdgicKuj2n7MaNao7/f2eJns6zeqaMuq/d4r2ZRtmGTK4v2hf91pG3HybW4vav/5kQXcYnrq9m4YMcF31s1q3p0VrcC/69Vfp08vp59np33TBgoVe88L+h4HyIejWhxU0JNGiIaQ2ta018oPKjddf4x3LU08/K+9PmerNZn/GZ/d9dLD7oI6uUM2GjbjFW7c9EIWge0D/3l7T/qPvuke+++57+xBKZNgOiJMF3XY/x8XdR7deX/p5oA/AaFir/6dKi44fPHS4+70ZhJMP3+9B+2WP07+jtse1cVs8MCG0/n3o34kp6X4GJwu67b7sk/2dmEAu2fev3Ud3FJsuD+M3rDk3ubzm+n0Uxu+lMIJuu3UHbQK9R6++3u8H28f+ner/rA/jOy1XT3tfwx42f1dhr5f1IYAAAggggAACCCAQBYGSCiZNAJ2uSbZBtdlOtsunu38l5ZZqf8zx2vMV97Hb2/IP54tLvuW0fifznhrdRiLJKyF3PE5JB912E9r+Jim/dW6Q3+ncKPcXrR2ogZYWDSW0P+egctWVl0vjHXdwJ/lrdG/boIEbrOtEXYf2j7xy5ebaR2Z9dsCoNRUnvf6mmSR2n4xaK71Hr37eNHtgxPAhojfetRRH0N2923Vuv4K6/u+dJnXvuPNuHQwsGsYFHWfgzBmMtIM1XWzAoKFuzTX/KrQfX73BrOXbb7+TO8eM9Wa58v8ulaKixu77n3+eLbfdPtod9v9j3wi0mygvjqDbf87NvtjXatA1aG686vzaNK02UZuo6A1WE5InmufyS7tI06Y7uZMz6cc+aH12GJzp37sd6N186+0y12nmNpti//2tWrXaqUnbL+Fq/M33avPX2gy2vxzScnNY6O+rM92QJVnQveuuO8vFF57vblZ/kGioG1Tsv0f78y1o3mzG2U0naysC2pqAv9g1VbV25Y3de3uzhOGpK7va+XzdcdPnq763zU2IpH8bJvi6sXsv+fPPdTqrW8IIBcy6/K+ZXOPDhg5ymmGu5K4i0d+7mvXr08M7FhPo60JhfB81bLit6HnVkuz7yG6a/4WJL4peX/lW7IfD9GElfWgpqAwa0McLe7Wfd7s1lqD5MxlnB90aJGuT5Hp9+ov9t5qsZRd7Ofvv7/EnnpYPPvzInhwz3PLgA+X00zq44+xgVpv57+H0DW6aSNbPwL79B6Xcx9L6ftcD0N8x5Zxa2387f9M9nPPlb7Je5+nQ/mSviXT7eHVaup/ByYLui50+wvfY1Ee4uqu/v6ht717d3NFlOei2v0OTfWYk+w3rt8vmvf33kM33kW4z199LZnn7O0jXa4JZu7UG++Gm5StWSJ++A3VWOaDF/nL2Wae7w4ke1tTvMv0NVKnSxu+L4gi6w/B0D6IY/jGexbBqVokAAggggAACCCCAQN4LlFQwGRTIJsPJNqw128l2+WT7ZE8rKTd7m0HD5njtacV97Pa2/MP54kLQ7T8zGb7PF0BC7uATl2nwFbyW5GPtGt3+IOjss85wbjg191agTXUGNddpN+/3wIOPiNbIsctxx7aWNq2P9ka9P+UDp3bRBO+9Dgxwmp42NRH1pqzWjtSb4qZos8TaD6Ip/hBEa1UOHtTPCz+CAki7SVpdT3EE3XYNTd3GxP+9JG+8OVkHY4rWuNaa19n2oR2zsoA31197lTRq1NCdokHkTU6f03qj2ZQKTtjft3cPr1/z51/4X0yz3ttvv51ce/UVZnZ55NHH3eaOvRHOwKkdTpFDDznYHaXr7t6zj1frpjiC7hUrVsqAgUNibuprv+w9u9/g3rjXHfH316vjtP9i7cdYi15T+sCGv4a63vjXm9B169aRJ558JmlgYgcIqfoSdjea5J9MQkD/auxgKFV/2f5l7ff2TfpUQbcuZ19b2lz9TU7IbActGm5oixDmBnS2tQmTBd36964PWZhiN41uxumrBqJbOU35atHzOvWDD93hsP6x+8fWgEP7w50x80tv9Rry9evb02uC1R9064y5euo67Ad99L02Izzy5o1NtTdr1lQuu6SzjnZL0D7kS9Dd7vjj5OijjnD3U4MSPQZt5cMUDSM1fKhfv547Sj/b7L7GdWQY30d2VwhB30f/PuxQaX/KiWa3JF+DbvtzXK/PBx96NO772X4oJeja8A4yywE76NZVBAXudgit86Rb+9wOorINunV7++y9l3Q67z866JbZs+fIraPuMG+913z5fh/qfPZV3dSShHZX8PQzm2trm53V/u51f7X4Q88wgm59aEDPmxZtSUJ/T9lFawdff+3VzmdfNXe0fvdqNyhBxTyMo9OiWKNb9zvX37C6jlxLGN9Huf5eCiPobrpTE7n8sq4uh35uaYtSy5Yt83g0IP8/Z3pRUWNv3O1OCw0//vSz9z6M77QwPL0dCnmAoDtkUFaHAAIIIIAAAgggECmBkgomx48fL+vXb2ydMh2gbMNaE/xmu3w6+6bzlJRbqv0xx2vPV9zHbm/LP5wvLvmS0/p9/O+p0e0Xsd6vcJ7if+aZZ5yAbHPtMjO5VatWsvPOG5ujNuMK6bW0g261tvu+1htOWmNx1jffxpyGXj1u9Pq51Hm+/nqWzHfC6mrVqjr95u7phalmoaBmiO3mtnU+DTW1VteiRb/K7rvtKrs4fU+a2og63R906zj7Bp2+nzd/gXz11VeyZs1aabF/c6+pW52mxR90a2j6n3PO2jhx079VnQBU+47VojdqNUi1y1dfzZK334mtIaz9zJpldN75zn585vTDrdd6kyaN3SbWTaiv6xw0ZFhgjWt7O5kO27VIdVmtLfPJJ5+6tX4bNKgvrQ7/t1dzUv/21MJfi8+uuazr0D6E9ZxUqVzF6Qt5X9l+u+10tFu0T/aXX3nNvC2WPrp15bqP2s/2b7//Ljs1KXLPqwlUdXpQDWMN9YcM6ucFrzqfXqNfOf9tsUU55zOmqezcrJnX5Lxds0jn9Re9HjtffIE3et68+W4wvn79BtHaoDs618uTzoMcOt4uGjyYJnLN+J2dENJc19qful2WOv3HPv5kfC05M4//QRT9u9JzpB7bbddQ6m2zTVxN/DCu8a23rus+XGD2W8Nx7Ttzzpy5jmUz9+EYNdeizZV369E75qGVdEOWZEG3rrv7jU7rCZsCT/3cmTnzK/nok09kvbNNfcjj8MMP85pt1unde/aNu8Z1PbkWOxTV7Wjz0LPnzJF69eq5f+uVK1f2NhEUJObqqSsvKmos2gqDKa9NekNeevlV8zYm/J37yy9y8y23e9N0IIxQQNeT6zWu15SGeHqNaNEHaPRzS79zGjZsIAceeIB3TnW6BuEa6tsljO8jO+DQdZvvIw3ftTWHhts28P5udXq+Bt26b1decZkUOX04a9HrUz8j1HOt872orXo029Q6hU7Xft3/9+LLOhha8QfdumINRqdM/dD93aefGabbEZ0W9PCCjj/m6KMc+yY66JWixo29z21dbsnSP7xpeqzjnT63/9zUcoEdpvtrOOtC9sNZ+l5b/tAWQPwlH77f9SELfdjCFP3s1cB7ifPgUf369eXAA/aP+Q3y0MOPybRPPzOzh1KjW8/bpV0v9tapfyO6Df1Ns/12jdy/E/MdYWZK9Js2l6B7d6dWuTZ7b5cGzveCtvygZfWaNXGtnUxyPh/Nw25hfR+F8RvWPoZsh3P9Psr191IYQbdeN7oec/3o5+60Tz93PjeWOf9fY2vZ03nwVT9X7OK/xsP6TsvV097HMIcJusPUZF0IIIAAAggggAACURMoqWBy+vTpMm3atLTD7mzDWhP8Zrt8uuevpNxS7Q99dAcLEXQHu6Q9Nh8A9QND//OXdELumTNnOjcx//QvGsr7xs4N1Lp164ayrmxXkuimYLbrC1ouWY1unV9rTmqNRBMmavAwYNBNMbUrNFS1+/QO2o7edDY3rRLdyD7/PKfm7d4ba94GrcMeFxR06w1LreGl/dGmU/xBd2MnDNB+VzMpeoNZm0y0izZ/revZZput7dFxw3rz7rZRo+OCmrgZsxyh/UZqrWvjHrQaPZ8aFi1YuDBusnpqk8gmTIybYdOImV9+JePuvT9msh0a2E2aa605rT2nZcKzL4jWvNdi97Fu9x98hRPeNSlqrLOkLMlCGl1Hl84XeeF+opUtWbpUho+4NWUgavcRHLQu+5jNdHMD2LxP9aohsfZtnahoX/CDB/WPu+Frz+8P/sO6xtO5tnT/x9wzTn788Sd7l9IOWVIF3Xp99u3d3avZGLMR35snn3rGDdZ8o0N5q6H6lf93mRe4+Vdqf/YFBd06fy6eZnt2TWb/ebfD36AAL6xQIIxrXGsh60M29gMC5hjNq5pqaxlBzYWH9X1kWt0w20z2ms9Bt36Ha9ch9eptk+wQ5Evnc3ys73M86QJpTgwKuhMtqi1DjBx5m9u3vH+eHt2uT3kM/mXsbjtSBd26rF1DXN+Pv/8h+WL6DB30Sr58v9t9xHs7FzCgLUzce98DMVPSDXeTNV2uK7zECbp3cQLvdIu/1q1ZLpeg225Zxqwv1au2TKT/aUnXItX3ka4r19+wuo5cSxjfR7n8XjLfAfr79robeniHY4LZdJou14UOO7Sl2/y+t4KAAfu71d9SVFjfaWF4Bux6zqOMZ84rYgUIIIAAAggggAACCERQIF8CW0OXa1Cd6/JmP1K95ptbqv0tqen54pIPOW065tToTqKktUknTpwo2ny5KemE3DNmzJCpU6eaRUJ/1ZC7Q4cOoa83kxWWRNDd2elnUWvEaEkUFGrfr3pT1QSmQc01b9eokdv0p9ZOtIsGqVrLR2s5nnP2me4kHXft9d3t2bxhbeb8yCNbxYR3ejNLA2UN27d1atJpSRQs6D526XyhU0O3qWjzhqZoLaOPnZqBWutGA0ItPXv3j+mPWWsoX3vN5ua6zbLJXpP1X6010LQZXtPnrFmPHo/WQtSb6EucWrvFWbSG8UUXdJLatbfyzp9uT/dBa/U95DRlqzWekhW9wXuQU5PSuJl59Yblc06T5x9++LEZ5b3aNc7sm8p2c6rafL3enNTSxakhvdtuu7rD9z/wsHz+xXR32A66P5n2qVMjvkjq1I59kEFr7E147vnA/XBXsukframk4ZXWyDb9sZrpek1qYK9N7+u1kqpoc6znd+oo2sSmv+i6nn/hRS/EN9PtZsrNuGSv+tmpNcLuAABAAElEQVSo/dkmK3rNXnhBR695bntePT/aR7xdszzMa7y+U2O5c+cL3PNhPht0+3r8v/wyT+66e1zgAwP696B9MWtJdownn3SC0/LAYe58ej3odeEveh51Pg2x/OdU59WHap559nmZNSu2FQr/enJ9rzXltT9krW1mW2gLBBoyaSCkn0f6XvtADirZepp1Dejf263trGHh9VbAoNNPO7W9G6br8GjnmvC3TGGHCvrDatTto3XWuGI/4GH/nZoZw7rG9bOm88UXitZEts+rfm798ccyGXff/THXtdm+eQ3r+6j1MUfJMc5/GtaaovugLUIsdfZDH1DQ8uxzE+Na9jDz58urdt2x3377uN+j9j6tXr1anp7wnHzq1JgsjqKfuyOdPqW1aFcHH3zwsbRuHWuq0/R3xZ1j7kn4ndjtxmtFH2LIpPTtP9h7KE9rr2ugqkU/E7V7FH/Rz6YB/Xp7D1noZ1m37r1jumYwy+TD9/vBBx0oJ5zQ1n0g0eyXedXzOun1N2O6JDHT0v0MPrbNMaL/afE3f67j9LPupBPbObXLD4n5vaXTljq16x965DHp5HznmhZs/N3j6HxaTj+tvfMZvvFvadq0z9zlNk5J/e8pJ58YV6M71VL2g5LpWqTzfaTbzfU3bKp9T2d6GN9H2f5eMt8B/gf1zINYel326NXPPYyYProDmr/fv/l+bksL/oee9IGxF1961W0hyTw46W+lIazvNN3RMDzdAw7xH4LuEDFZFQIIIIAAAggggEDkBPIlmDRwuQbVuS5v9iPVa765pdrfkpqeLy4E3Tme8XwBNGG3Nu3csmXLtJorT1QTPEeSmMWLu8mImI0FvCmJoDtgszmN0puGTYqK3D5ptXbsTz/97Iaqma5Ug1ldj4Z12u+eBkR2H4h3jhkr334b29SzfxtaM1DDoz/++MNdhwYUerNNiw5ffe2N/kWK5b2GJM2aNZUqVavIH87NXw259SZgSRetmaJBgdbe1ubUMy1a80mbudWb/986Dy5ojZ3iLnbQrU3q6sMY2iTpjjtuL+XLlXf3Y9WqVRnvhtbK02bkK1Ws5DaPP3/BgrQCbv+G1ETD9xrO+rTfFm0WWgObki7aH+qOO+zgXmOrnabE9W8mG5ds91sD9G3qbS3at+3ixUuyXU1Oy2nN1cZFjUW7G9Cm7fUaL4lr1N5pvXGvTeFXrFDRDZP1cyubkg+e2ex3cSyj3wVFRY3d60qvr0xKmN9Hug+/Lvqt2FrgyOS4cplXP7N2ch7QKVduC+cBtB8CH0bJZf3pLKsBqT6Epd0r6Hex/ha1++BNZx35Mk8+fL9rKKndpdR1WrP5Y9lyt6nubD97snXV31p6TqN+PrM9fv9yYfyG9a8z0/dhfR+F9Xsp0/038+uDEu5DTxXKy/x5C2ThokVmUom+huUZxk4TdIehyDoQQAABBBBAAAEEoiqQL8Gk8cu0L2+znP1asWJFOf/88+1RoQ/nm1voB5jlCvPFJV9y2lSM1OhOJZTFdGp0Z4GW54toc9fvvjsl8CbWNs4N8R7drvNqS2qTiP4QS284n332GfLoo08E1sDSfhy19o8WDQG1RjclvwWCgu783mP2DgEEEEAAAQQKTSDX37CF5sXxZi9A0J29HUsigAACCCCAAAIIRF8gX4JJI5lpX95mOfOqIXfz5s1lr702dvVpxof9mm9uYR9ftuvLFxeC7mzP4KblogIYdJhaC3zKlCmitcCLo+gHTMOGDYtj1WmvM4o1utM+ON+MpqlrrS388COPy2eff+HNoTWF/u/yrlKjRg13nNbQ7jdgY1Oo3kzOQPcbr3P7k17uXBN33TU2pt/pvfbaU8479xyvGdxMm8e0t8NwyQkQdJecNVtCAAEEEEAAgcwFwvgNm/lWWaJQBQi6C/XMc9wIIIAAAggggAACKpAvwWTUzgZuwWcsX1yiktNSozv4OmJsCoFCCrrtvsKVRZvd1D4eq9eoLls6Abfp91abxRw+4taYENswDh7Y120y3bzXwFv7A6y55ZZSrVo1M9pdt/YRmE5fzN5CDJSKAEF3qbCzUQQQQAABBBBIUyCM37BpborZEBCCbi4CBBBAAAEEEEAAgUIWyJdgMmrnALfgM5YvLgTdwecn7bFRAUz7gMrYjIUUdOupO/+8c51mOvb0Qm3/6Vzv9An91FPPyEcfT/NPct9rH71XXnmZ2y934AzOyOXLl8vdY++TefPmJ5qF8XkkQNCdRyeDXUEAAQQQQACBQIFcf8MGrpSRCAQIEHQHoDAKAQQQQAABBBBAoGAEFi9ZIloRrvZWW3kttxbMwedwoPkS6OZwCKEvqteRXk9atq5bN/T1Z7LCqOS01OjO5KwyrydQaEG3HvjWW9eVNq2PkaY7NXFrYWs/3Ev/WCqzZ8+R556bGNj3tge2aWDXXXaWww8/TLZr1EgqV67k1uD+ffESmTFjprw1+R3/7LzPY4ETTzhe9t1nb3cPJzz3vHMOv8zjvWXXEEAAAQQQQKBQBcL4DVuodhx3+gIE3elbMScCCCCAAAIIIIBA2RNY5lRiW79+vWzptOBauVKlsneAxXREBN3xsJo7/bFsmfvAhD44UZqFoDtH/agA5niYkV28EIPuyJ4sdhwBBBBAAAEEEEAAAQQQKEYBgu5ixGXVCCCAAAIIIIAAAnkvsMrppnTNmjVSpUoVqVG9et7vb77sIEF3/JnwrqXKlaWG03VuaZao5LTU6C7NqyTC2ybojvDJY9cRQAABBBBAAAEEEEAAgRAFCLpDxGRVCCCAAAIIIIAAApETMLVwdcdrOrW6K1GrO61zSNAdy2RfR1vVqiUVKlSInaGE3xF05wgeFcAcDzOyi19zXTf5+++/I7v/7DgCCCCAAAIIIIAAAggggEDuAuXKlZNbRt6U+4pYAwIIIIAAAggggAACERbQGt1aG7e88/u4mlOrmybMU59Mgu7NRhpyaxP42kd3lTyoza17FpWclhrdm68jhjIQGDJ0hPz6228ZLMGsCCCAAAIIIIAAAggggAACZU2g3jbbSI/u15e1w+J4EEAAAQQQQAABBBDIWMD01a0LVnWaMa9YsaLb13L58uUzXlchLFDoQbeG2n/99Zf8uW6d2/S9nnN9kLhO7dp5cfoJunM8DVEBzPEwI7v4k09NkClTP4js/rPjCCCAAAIIIIAAAggggAACuQu0PPggOf209rmviDUggAACCCCAAAIIIFAGBEzN7jJwKBxCCQvkS01uc9hRyWmp0W3OGK8ZCfz882y57fbRGS3DzAgggAACCCCAAAIIIIAAAmVL4KorLpPGjXcsWwfF0SCAAAIIIIAAAgggkIOANkOttXT1Vf/TmrsUBIIEtLZ/Racv7ipOCwCl3Se3f/8Iuv0iGb6PCmCGh1WmZqdWd5k6nRwMAggggAACCCCAAAIIIJCRALW5M+JiZgQQQAABBBBAAAEEEEAgMgJRyWmp0R2ZSyo/d3TMPeNk1qxv83Pn2CsEEEAAAQQQQAABBBBAAIFiEdh1152la+eLimXdrBQBBBBAAAEEEEAAAQQQQKB0BQi6c/SPCmCOh1kmFqdmd5k4jRwEAggggAACCCCAAAIIIJCWADW502JiJgQQQAABBBBAAAEEEEAgsgJRyWmp0R3ZSyy/dlz77P7o42ny/fc/yO+LF8vff/+dXzvI3iCAAAIIIIAAAggggAACCGQlUK5cOdm6bl1p2nQnOaBFc/rkzkqRhRBAAAEEEEAAAQQQQACB6AgQdOd4rqICmONhsjgCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQNwJRyWmp0Z03lww7ggACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCJSuAEF3jv5RAczxMFkcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyBuBqOS01OjOm0uGHUEAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRKV4CgO0f/qADmeJgsjgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCOSNQFRyWmp0580lw44ggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACpStA0J2jf1QAczxMFkcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTyRiAqOS01uvPmkmFHEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgdIVIOjO0T8qgDkeJosjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACeSMQlZyWGt15c8mwIwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDpChB05+gfFcAcD5PFEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgbwRiEpOS43uvLlk2BEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgdAUIunP0jwpgjofJ4ggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDeCEQlp6VGd95cMuwIAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggULoCBN05+kcFMMfDZHEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgbwSiktNSoztvLhl2BAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEChdAYLuHP0NYOMdd5Atttgix7WxOAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAMoF//vlHfp49x52lqPGOyWYt9Wl5W6P7l3nzZf369dKw4bZSuVKlUodiBxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGyLPDnunUyf/4CqVixomzXqGFeH2reBt2LFy+R5StWSK1aNaVO7dp5jcjOIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAlEXWLJ0qSxbtlxqbrml1K1bJ68PJ2+D7j///FPmL1jo4lGrO6+vIXYOAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQiLmBqc+thNNy2gVSuXDmvjyhvg25VM7W6K1SoIPXqbUMT5nl9KbFzCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQRQENuX/99TfZsGFDJGpzq3FeB926gwsX/Spr1qzRQbcZ8+rVq0slp034LbbYwh3HPwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACmQn8888/sm79elm1apXbXLkuXbVqVWlQv15mKyqlufM+6FYXU7O7lIzYLAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFCmBaLQL7d9AiIRdOsOa5/dK1eukjVr18p658kCCgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBA9gIVnZa0q1apIjVqVM/7Prn9RxmZoNu/47xHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEChMAYLuwjzvHDUCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQWQGC7sieOnYcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKEwBgu7CPO8cNQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBBZAYLuyJ46dhwBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoTAGC7sI87xw1AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEFkBgu7Injp2HAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEChMAYLuwjzvHDUCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQWQGC7sieOnYcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKEwBgu7CPO8cNQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBBZAYLuyJ46dhwBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoTAGC7sI87xw1AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEFkBgu7Injp2HAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEChMAYLuwjzvHDUCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQWQGC7sieOnYcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKEwBgu7CPO8cNQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBBZAYLuyJ46dhwBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoTAGC7sI87xw1AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEFkBgu7Injp2HAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEChMAYLuwjzvHDUCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQWQGC7sieOnYcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKEwBgu7CPO8cNQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBBZAYLuyJ46dhwBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoTAGC7sI87xw1AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEFkBgu7Injp2HAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEChMAYLuwjzvHDUCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQWQGC7sieOnYcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKEwBgu7CPO8cNQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBBZAYLuyJ46dhwBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoTAGC7sI87xw1AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEFkBgu7Injp2HAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEChMAYLuwjzvHDUCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQWQGC7sieOnYcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKEwBgu7CPO8cNQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBBZAYLuyJ46dhwBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoTAGC7sI87xw1AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEFkBgu7Injp2HAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEChMAYLuwjzvHDUCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQWQGC7sieOnYcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKEwBgu7CPO8cNQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBBZAYLuyJ46dhwBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoTAGC7sI87xw1AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEFkBgu7Injp2HAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEChMAYLuwjzvHDUCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQWQGC7sieOnYcAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKEwBgu7CPO8cNQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBBZAYLuyJ46dhwBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoTAGC7sI87xw1AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEFkBgu7Injp2HAEEEEAAgfwR+Oeff2TdunWyYcNfzn8b5K+//hIdR0EAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoPYEttthCypcvLxUqVHD+Ky+VKlUSHVcWCkF3WTiLHAMCCCCAAAKlJKDB9p9//ilr164tpT1gswgggAACCCCAAAIIIIAAAggggAACCCCAAAKZCFSpUkUqV67sBt+ZLJdv8xJ059sZYX8QQAABBBCIiMDq1WtkzZo13t5WrFhR9D99KlCfDiwrTwV6B8gAAggggAACCCCAAAIIIIAAAggggAACCCAQMQFteVNb4dRKS+vXr3f/M4dQtWpVqVatqnkbuVeC7sidMnYYAQQQQACB0hXQH0SrVq1yfxzpnujTf1WqVHabvyndPWPrCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAskEtNvJtWs3t9KplZaqV68eydrdBN3JzjTTEEAAAQQQQCBGQJ/8W758hdv/tv4A0qf9tBY3BQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCA6Alq7W1vt1Hu+2jpnzZpbui11RucIRAi6o3S22FcEEEAAAQRKUUBrci9fvtwNuStXriQ1atQoxb1h0wgggAACCCCAAAIIIIAAAggggAACCCCAAAK5CqxcuVL+/HPdprC7ZqRqdhN053r2WR4BBBBAAIECEVi2bLn7dB8hd4GccA4TAQQQQAABBBBAAAEEEEAAAQQQQAABBApCwITd2opnrVo1I3PMBN2ROVXsKAIIIIAAAqUnoE3YrFmzxm26Jko/dEpPjC0jgAACCCCAAAIIIIAAAggggAACCCCAAALRETAVnapWrep2WRmFPc/roPv333+XkSNvSepYoWIF2XrrraV+vXpSv0EDOaDF/jSlaokNHjxUVqxYYY3JbPDGG6+X2rVrZ7ZQKcw9dty98sP3P8Rs+YQT2skhh7SMGccbBBBAAIHMBbTJ8mXLlrkLaj8t9MmduSFLIIAAAggggAACCCCAAAIIIIAAAggggAAC+SygfXYvX74xU6xVq1YkmjDP66B79uzZ0uqIozM+56ee2l7OPPMMab7ffhkvm8sC//zzT9zi2nl7aZb9WxwkixcvznoX3nt3sjRq1Cjr5UtqwfM6XSDvvPNuzOb69+8rHc/9T8w43iCAAAIIZC6watVqWbt2rVSpUkWqV6+W+QpYAgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCDvBaJ2L7hMBt3mKrns0kvkqquvlArly5tRxfb62muTpEvXS2PWv++++8iEZ56KGVfSbwi6CbpL+ppjewggULYE9CGuJUuWuge11Va1pHwJfKeWLUGOBgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiIbAX3/9JX/8sbF1zzp1aktpV+hNpVamg249+IMOOlAeuP8+qVSpUiqLnKYHBd077LC9vD35zZzWm+vCBN0E3bleQyyPAAKFLfDnn3/KypWr3ObKtdlyCgIIIIAAAggggAACCCCAAAIIIIAAAggggEDZFdDmy7UZ8xo1qkvlypXz+kAjGXRXr17dQ121apU3nGjg4osulB49uiWaHMr4KAXd6rf77rulPG69eO8c/V/Zcsv8DzZoujzl6WQGBBBAICsB01RNtWrVpGrVKlmtg4UQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEoiGwZs1aWb16dSS6soxk0D11yrvSoEED92rQPkPnzJ0rP/74k4wefafMnPll4FXy8EMPyCGHtAycFjRSa7Bl8pRCmEF3ptsO2n8zLqhG9+2jbpUTTmhnZsnb17///lu0iYSKFSum3Mdcg+4NznbEaZ63QoUKKbeV7gyrV6+RSpUrpdV0vp5zbXUgrCYg1E3XqcEUBQEEEMhFYNmy5bJhwwbR2tzpfB7nsi2WRQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEChdAa3NrbW6NTOrVatm6e5Miq1HPui2j0/DyvHj75chQ26yR7vD//73YW4T5nETNo3QgPzhhx+RL7/6Sn766WfRmuJa81n72d5l552lpROSH3lEq7jF+w8YKJ98Mk1+/32xLFy4MG76nnvu4Y178IHxUrt2be+9Gch222b5ZK9hBd1Tp34gQ4bGu44be4/Ur18vZhdmz54tl//flTHj9M2o226VJk2K3PEnnHhy3HRdV92t67rn8J133pXPPvvcPQ/6UMO/DztUOnU6T3bbbde45XREpkH3mjVr5JVXXpUJzz4n33//g3fu6tat6+5ju3bHy0knnuD8AdcK3N5TTz8jDz74UMy0/5xztnTo0F7uvmescwwPyOLFi93pNw0dLGeccXrMvIsW/SpPPfW0THr9Dfnhhx/c49QZtLn7gw48UE455WQ54IAWUq5cuZjlzBu/n4ZPjz7ykLOuH2X4iJGiflr0eD75+AOzGK8IIIBAxgLaP7f20x2F/lgyPjgWQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEIgR0PvBel9YK2fqfeF8LmUq6DbQGrK++OJL5q33+sHU9+NC2eXLl8uVV10jkye/7c2XaEDDx359ezu12jY/vXDhhRfLm29NTrRIzHjtr1uDTFNy3bZZT7LXsILudevWyTGtj5U5c+bGbK5jx3Olf78+MeOucjyff2FizLi99tpLnp3wlBfcFjVpFjNd37z04kQZNGiITJk6NW6aGdG9243SufNF5q33mknQrevv3PkSL1z2VhIwcOutN8vJJ50YN2XMmHtk2PARMeMvvaSrVHf6Kxgx4uaY8f3795WO527sK1w/HMbde1/gwxgxCzlv9MGKESOGOR8idfyTJMjvnbffkuPatos7rp9+/C5ueUYggAAC6QosXrzEnbVu3fjPonTXwXwIIIAAAggggAACCCCAAAIIIIAAAggggAAC0RGIyn3hMhl0z54zR1q1Oiruahk4sL9orVtTVqxYIf85t5NMnz7djEr5qrWLn3j8US+wzjboDmPbKXfWmSGsoFu39bpT+/jizl3jNmsH+DNmzpQTTzwlbp7nnn1G9t57L298UFDb8uCDk4bcZuGePbvLRRdeYN66r+kG3c86Nbivufb6mGVTvbn6qivliisuj5ktKOhu1erwwAcm7KC7e49e8vjjT8SsK9kbvd5eefl/cTXLg/yOOeZomTTp9bjVEXTHkTACAQQyEIjKD5oMDolZEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBJIIROW+cJkMuvW8nHbamfLJtGkxp+jCC86XXr16eONuG3W7jBr1X+99ugNas/uWmzfW5s026A5j2+nsb1DQffPI4dK27XEpF9fmsMuXLx8z3znndIwLo089tb2MGD7Mne/cjp3kvffej1nGnm4mBAW1Zlo6r2+9OUkaN27szZpO0K3Neh99TBtvmUwGHnnkQdEg3pSgoNtM87+aoPu551+Qq6++1j855fs2bVrLmLtGx8yXiR9BdwwdbxBAIEOBqPygyfCwmB0BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAggUBU7guX2aBba+1q7V27aLg7+o7b3VHaR3OLAw6Oa+ZZQ+yuXS6Wpk2byrfffisDBwY3pf3D99+4zXDPnz9fVq9eI1OmTJG+/QbYmxOtjfvQg/d743bccQfR8DisbXsrTjIQFHQnmT1m0rixd8tRRx0ZM27WrFlO89gnxIzTN6+++pL89utvTg358+KmBTUZnyio1X7Rr73matl9991kzty5csstt3n9Z9sr7tSpo/Tt09sblU7QHTSPrkAfgDj99FOlcuXK8tprk5y+yDeG9t7KnQFtcn7yW2+4/RHo+GRBtx7Dcce1kYYNG8qihYvkCKcJcu0jPuh60z7cB/TvJ/q6cuVKefyJJ2X48JH2pt3hF154Vv61557e+ER+OsP+zZvLfs33da/PuXN/kTv+O8pbjgEEEEAgU4Go/KDJ9LiYHwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCBYICr3hcts0H3rbaPk9tvviDk7++67j0x45il33O+//y733/+gLF6yRBYvXiz6vmqVqjJu3N1StWpVb7n3358SGN5OnfKeE2TX9+bTgLRL10u99zqg4ag26+0vYW/bv377fdhBt667T59+8tDDj9ibEW02e8GCBTJz5pcx47vdeIN0cR4c8JdEQe3LL02UXXfd1Zt90aJf5eRTOsSF3XXr1pUPP3jfq3EeFGKbmtS6Ml3PQQcf4q3XDPgDcx3/4IMPxT20oOOffvoJab7ffjqYMOhu0WJ/uevOO0T3zy4vv/KqXHppbPPn+iDEpNdelho1atizun14Dx48NGacvzWCRH63j7pVTjihXcyyvEEAAQRyEYjKD5pcjpFlEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBDYLROW+cJkNuu+8a4yMGHHz5jPiDGmt2YkvxNbyjpnB92b9+vXymNOfct++/X1TRN54/TVp0qTIG59J0O0tlGQgk20nWU1gH93J5renBdXo1un6YMDhTh/oq1atsmePG9ag/7VXX3ZrSvsnBgW1Z5x+mtx00xD/rPLAAw9Kv/4D48bb5yBV0K39Ymv/2P7if2BBp//555/SfP8D446va9fOcuMNG/v3TlSje8r778i2227r34xcddU18vwLE2PGDxk8UM4668yYcfpm7dq1stvu/4oZ36xZU9fSjAzy69jxXOnfr4+ZhVcEEEAgFIGo/KAJ5WBZCQIIIIAAAggggAACCCCAAAIIIIAAAggggICTBS5xFerWrZPXGmU26O7Vu4888shjMfjaDLeGt/7yzz//uM1kf/XlVzJ9xgz56aefZfbsOaLNdCcqdsiq82QbdIex7UT7qOOLo0a3rve+++6XgYMG62DCMvaeMXL00UcFTg8Kau8ff68cfvi/4+bX5uEPOfTwuPFPPfmY7L///u74VEH3yJG3yOg774pZhzbx/dRTj8eMM2+uv+FGpwb3BPPWfT3++LZeM+BBQXeyBylOOrmDTJ8+PWZ92sR5vXr1YsaZNz/99JMZdF913pkzPvfGBfnde+9YOdJpJp2CAAIIhCkQlR80YR4z60IAAQQQQAABBBBAAAEEEEAAAQQQQAABBApZICr3hcts0H1ux07y3nvvx1yD55xzlgwaGNuP9rvvvicais+ZMzdm3lRvwgi6w9p2sn0NCrrP/c85TgC+MSBOtmyL/ZsH1k7WZdatWyfHtD42oduhhx4S0z+5fztBQe0rL/9PdtllF/+ssuGvv6RZs83NmZsZ7rprtBzbprX7NlXQHRRcn3zySXLrLSPN6mJeR436r9w2amN/7maCHYwHBd1tnH0Z4+xTUAk6D0HzJRv31ZfTvWb1g/xeenGi7LZbvFOydTINAQQQSCUQlR80qY6D6QgggAACCCCAAAIIIIAAAggggAACCCCAAALpCUTlvnCZDLrXrFkjLQ44OK7p6euuu0Yuu/QS9wxqTeru3XvKE09u7LM7vdO6ea5cgu6wt715r+KHggLWMPpx/vvvv+Wkk9vH9clt9sCu/WzG2a9BQa2/f24zv25rp6bxAfjoO26Xtm2Pc2dLFXRfceXVMnHi/8wq3dfTTztVhg2L7QvbzKC1v7UWuF3sGtuZBt17/mufuOvRXnc6w599+olstVUtd9YgP4LudBSZBwEEMhWIyg+aTI+L+RFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSCBaJyX7hMBt13jblbhg+Pr6n7wgvPyr/23NM9Y2++NVkuvPDiwLNXVFQkhx12qFOLuKk7/8mndIibL5egO+xtx+2cNaK4gm5t1ltrSScrdtPi/vmCgtpHHnlQWh58sH9W+e233+SAA1vGjX/0kYfk4IMPcsenCroHDx4q4+69L2YdyWqd9+nTTx56+JGY+e2m7zMNulu3OU6+++77mPV16NA+pp/3mIm+N1WrVJWOHf8j5cuXd6cE+RF0+9B4iwACoQhE5QdNKAfLShBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQToozuMa2D27NnS6oij41Y1dcq70qBBg7jxOuLnn3+WI448Jm6ahtdvvvGaNz6oKWtdp/bhvcceu3vzrV27Vnbb/V/eezOQS9Ad9rbNPgW9FkfQvWzZMrfP7FWrVgVt0hu36667OrWon5MKm8JZb4IzEBTUXtK1i9xww3X2bO7wC05N7CudGtn+Yjd1niro1pBbw25/+XLmF1KtWrWY0Vrj/qij2zh9tcf2k61Nvg8Y0M+dN9Og+/wLLpLJk9+O2c7NI4dL+/anxIxL902QH0F3unrMhwACmQgQdGeixbwIIIAAAggggAACCCCAAAIIIIAAAggggED0BaJyX7jM1OjesGGD3Hff/TL0pmGBV0+vXj3kwgvO96adfsZZ8vHHn3jvdWDgwP7yn3POjhn3ybRpctppZ8aM0zfpBN3Vq1eXzz+fFhf0hr3tuJ2zRhRH0D1o0BC5977x1lac4Np5kGD58uXOEx6LY8ZrMKwBsb8EBbXq9c7bb0qdOnW82bUv8A6nnh7YRPqsr2dK5cqV3XlTBd1ffvmVtDvhJG+9ZiDonGvf6R3P23ytmHntPsEzDbrvuWdc3LW51157yYRnnvRqaZvtaA32m2++VarXqC41t6wpNWvWlNq1txLtU9yUID+CbqPDKwIIhCkQlR80YR4z60IAAQQQQAABBBBAAAEEEEAAAQQQQAABBApZICr3hSMZdJ9x+mmy5ZZbuteXhoLffPudzJo1K+H1tu+++8iTTz4eEzgHBaPnnddR+vXt7a1n6dI/5Iwzz4prclpn8Afd778/Rf5z7nnesmZAg9Rj27QWDeJNLfSwt222FfQaFHSrn5qkU449to3UqrWxX2id/5tvvpFjj2sXt+jTTz8h8+fNF+0L2y4aXr89+Q2pW7euPTqwRrfOoIG51nTebbddZc7cuW4t7HfeeTdmWX2joe+tt2xunj7ItH//vtLx3P94yx55VOu4Wto68c7R/5XWrY9xA2d9sKFTpwsD+9P++qsZUqVKFXd9mQbd8+bNk0MPa+Uua/9z1llnSq+e3b1a5Vpb/qKLuojuh100FH/+uWe8UQTdHgUDCCBQzAJR+UFTzAysHgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKBiBqNwXjmTQnclVpEHryy/9T7bffruYxYYNHyEaVvqL9sN84AEtZMnSpfLQQ48EBp66jD/oXrBggbQ85N/+1Xnv7abTw962t5GAgaCgO2C2hKNee/Vlt69ynUGb9D77nHPlgw8+jJn/+OPbyh3/HSV///23nHraGfLZZ5/HTNdg/aabhsSMCwpqY2ZI8cbf/3c6Qferr74mXS+5LMWagyf3dMLoiy68wJuYadCtC3br1kOeePIpbx1mQK/RvZ0ge/mK5YE113W+0XfcLm3bHmcWCXxQgBrdHg8DCCAQokBUftCEeMisCgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKGiBqNwXLtNBd7NmTWXMXXdKkyZFcRfjlKlT5ZxzOsaNT3eEP+jWELjVEUfJnDlzE65i+hefujXRw952wg06E8IMuic6fWX7a2zrtic7NbZ33GEHdzc+//wLOaX9qe6w/c+zE56WffbZ2xuVS9AdFJynE3TrxhOFzd6OBQy0PPhgefDB8TFNjGcTdGuf5m2PPyHpNRKweTn00ENk/Ph7Y1okCPIj6A7SYxwCCOQqEJUfNLkeJ8sjgAACCCCAAAIIIIAAAggggAACCCCAAAIIbBSIyn3hMhl0aw3Zs50moa+++kqpWrVqwmsyUc1qe4FLunZx+/L2NyXtD7p1mRkzZsiJJ7W3F48ZfvyxR+TAAw9wx4W97ZgNWW/CCro1pD281VFxfXB37dpZbrzhemuLItdce708++xzMeP23HMPee7ZZ7ywOCiovX3UrdK9R6+Eteh1hW2cZuC1yXL/eU036N7w119O/9e3BNbmj9nhTW+0ifQhgwfGbS+boFtX+euvv8nl/3dFXP/wQdvWcUce0UpGO02rmybTzXxBfgTdRodXBBAIUyAqP2jCPGbWhQACCCCAAAIIIIAAAggggAACCCCAAAIIFLJAVO4Ll4mgW/u+1qbJt9tuOzcY1ObH/UFo0MX4lxN6vvTSyzLSCT79NbG1T+R2xx8nF198kZx22plxfSYHBd26jRkzZ8ott9zm1HJ+O26TGuSecMLG/q2LY9txG3RG5Bp0m+McO3acDBk6LGYT+kDB+++9HdOHt86wcOFCObjlYTHz6pu77hrt9leuw4mC2jp16kjPXr3ljTfe1Nm8ssMO20vXLp3lNKcZ9Arly3vjzUDnLpfIpEmvm7fuqzaXrrW/g8pHH30sDz70sLz44ktBk6VVq8PlnLPPkqOPPipw+vjxD8iAgYNipvn7DY+ZaL3RsP25Z5+XJ554Mu66MrNpLW493pYtD5YtttjCjPZeg/zMufJmYgABBBAIQSAqP2hCOFRWgQACCCCAAAIIIIAAAggggAACCCCAAAIIIOAIROW+cF4H3SV1JWnf0r/99rvM/WWulC9XXnbeuZloiJtLWbt2rcyfv0CWLVsmderUlvr168fVytX1F8e2c9nvklo2KKi1ayRv2LBBfvjhB1np1CRv1LCR1Ku3jZQrVy703Vu9erV7nn797VfZwvmfnit9cKJWrVqhbytohUuWLJFFi36VP/74w5287bYN3GslnQc1gtbHOAQQQCBsgaj8oAn7uFkfAggggAACCCCAAAIIIIAAAggggAACCCBQqAJRuS9M0F2oV2gpH3eqoLuUd4/NI4AAAghsEojKDxpOGAIIIIAAAggggAACCCCAAAIIIIAAAggggEA4AlG5L0zQHc75Zi0ZChB0ZwjG7AgggEApCUTlB00p8bBZBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgzAlE5b4wQXeZu/SicUAE3dE4T+wlAgggEJUfNJwpBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgXAEonJfmKA7nPPNWjIUIOjOEIzZEUAAgVISiMoPmlLiYbMIIIAAAggggAACCCCAAAIIIIAAAggggECZE4jKfWGC7jJ36UXjgAi6o3Ge2EsEEEAgKj9oOFMIIIAAAggggAACCCCAAAIIIIAAAggggAAC4QhE5b4wQXc455u1ZCgw+s67ZN26dTFLXXB+J6lVq1bMON4ggAACCJSuQFR+0JSuEltHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTKjkBU7gsTdJeda44jQQABBBBAIHSBqPygCf3AWSECCCCAAAIIIIAAAggggAACCCCAAAIIIFCgAlG5L0zQXaAXKIeNAAIIIIBAOgJR+UGTzrEwDwIIIIAAAggggAACCCCAAAIIIIAAAggggEBqgajcFyboTn0umQMBBBBAAIGCFYjKD5qCPUEcOAIIIIAAAggggAACCCCAAAIIIIAAAgggELJAVO4LE3SHfOJZHQIIIIAAAmVJICo/aMqSOceCAAIIIIAAAggggAACCCCAAAIIIIAAAgiUpkBU7gsTdJfmVcK2EUAAAQQQyHOBqPygyXNGdg8BBBBAAAEEEEAAAQQQQAABBBBAAAEEEIiMQFTuCxN0R+aSYkcRQAABBBAoeYGo/KApeRm2iAACCCCAAAIIIIAAAggggAACCCCAAAIIlE2BqNwXJugum9cfR4UAAggggEAoAlH5QRPKwbISBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAYnKfWGCbi5WBBBAAAEEEEgoEJUfNAkPgAkIIIAAAggggAACCCCAAAIIIIAAAggggAACGQlE5b4wQXdGp5WZEUAAAQQQKCyBqPygKayzwtEigAACCCCAAAIIIIAAAggggAACCCCAAALFJxCV+8IE3cV3DbBmBBBAAAEEIi8QlR80kYfmABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgTwRiMp9YYLuPLlg2A0EEEAAAQTyUSAqP2jy0Y59QgABBBBAAAEEEEAAAQQQQAABBBBAAAEEoigQlfvCBN1RvLrYZwQQQAABBEpIICo/aEqIg80ggAACCCCAAAIIIIAAAggggAACCCCAAAJlXiAq94UJusv8pcgBIoAAAgggkL1AVH7QZH+ELIkAAggggAACCCCAAAIIIIAAAggggAACCCBgC0TlvjBBt33WGEYAAQQQQACBGIGo/KCJ2WneIIAAAggggAACCCCAAAIIIIAAAggggAACCGQtEJX7wgTdWZ9iFkQAAQQQQKDsC0TlB03ZPxMcIQIIIIAAAggggAACCCCAAAIIIIAAAgggUDICUbkvTNBdMtcDW0EAAQQQQCCSAlH5QRNJXHYaAQQQQAABBMqkwJIlS+Wff/7xjq1mzS2lYsWK3nsGECgEgRUrVsq6deu8Q61StYpUr1bNe88AAggggAACCCCAQH4LROW+MEF3fl9H7B0CCCCAAAKlKhCVHzSlisTGEUAAAQQQQACBTQJffT1Lht88Ksbjsq4XS4v994sZxxsEyrrAkGE3y7fffe8dZs0tt5RbRw6V8uXLe+MYQAABBBBAAAEEEMhfgajcFybozt9riD1DAAEEEECg1AWi8oOm1KHYAQQQQCACAmvWrJE5c+bIVlvVlvr160VgjzPbxaVL/5BFixZKw4YNpWbNmpktzNwIhCDw199/y7XX95A/li3z1rbddo1kYN+essUWW3jjGECgEAR+mTdfevUdGHOoJ5/YTk4+8fiYcbxBAAEEEEAAAQQQyE+BqNwXJujOz+uHvUIAAQQQQCAvBKLygyYvsNgJBBBAII8F7rjjTrn5llu9Pdy/eXO57babpVGjRt64qA6sWLFCuvfoJS+++JJ3CGeeeYb069tbKleu7I1jAIHiFpj44svyzLMvxGxmYL9esr0TdlMQKESBhx55XN546+2YQ795+GCpW6dOzDjeIIAAAggggEDJCqxfv97rakcfyKSbnZL1j8rWonJfmKA7KlcU+4kAAggggEApCETlB00p0KTc5AMPPODOc95556WclxkQ8Av88MOP8uyzz/lHyxbltpBLunaRannQx+XixYvl7bffkffenyK//PKLU5N2kehnxpZO06TbbttA6tWrJ82b7ydHHXmkNGlSFHcspTFi2qefyltvTo7bdB3nhvv5559XZmtcPv30BLn+hhvjjnvPPfeQF55/NvLHrcemx+gvXbpcLN1uvME/mvcIFIvAH38sk2tv6CFaq9uUnZs1lR43Xmvepnz96efZ8s6773s3HXWB7Ro1lKOPOiLlsvYM2j/4Dz/+JB98+LF84zQdvczZt1WrVkk5p8norWrVcj6ft5aDDmghB7RontPDIIuXLJE33nzbbZ5a+yVf5tRk15ukjZx9LipqLI133EH23ftfUr16dXv3Qhv+YvpM+ezzL7Je39Zb15V2bY/NaPkPPvpYZs36NmaZ/Z3vuj332C1mXDZv/vrrL9Fg2C76/XRiu+PsUUmHZ8+ZK29Nfidunu23306OOuJwb/ynn30h02fM9N4nG9Cb3zVqVJc6tWu73+277tIs7ebH9e/iquu6xaxera67+oqYcbxBAAEEEEAAgZIVaN++vWjYrUV/v02YEP//p0p2j9haPgpE5b4wQXc+Xj3sEwIIIIAAAnkiEJUfNHnC5e2GhtxPP/20+/7UU08Vwm6PhoE0BSZPflvOv+CiwLlfefl/sssuuwROK4mRs52mr4cOHSavvvpa2pvTQHVA/36y7777pL1Mccz44EMPS9++/QNXPXPG58UWxgRusARHXnjhxfLmW5MDt/jO22+JBiBRLRoMNW22a+DuN2jQQKZOeTdwGiMRCFvgsSeellcnvRGz2uuvuVL22D34+rRn3LBhgzzx1ASZ9MZb9mh3WMPFW0YMiRufaIT2iXz32PGiIXSqUr5cOTn9tPbS5pijUs0aM33FipVy97j7ZOaXX8eMD3qj22jT+mjpcMqJaYejQesJGnfPvffLlKkfBk1Ke9z94+5Ka14NbPWYv/aF3LrwYYe2lAs7nZvWepLNtG7dOul86ZUxs2Ry/vV3e7de/byb1vaKzu94jhz+70O9UQ8/+oS8HvDglzdDkgE9py2chyTOOv1UqVWrZpI5N066ffQY0WDdLjcN7icN6te3RzGMAAIIIIAAAiUoQNBdgtgR3lRU7gsTdEf4ImPXEUAAAQQQKG6BqPygKW6HTNZvh9xmuXPPPVdOP/1085ZXBFIK5GvQPXbcvTJkyE0p9z/RDJ06dZRePXuEHnYk2p5/fKEG3frQhF5TQWXSa69I06Y7BU2KxLhkQbfWItUHGCgIFLeA1uLuetlVMQGj1oC947aRKTetwfQdd94jy1esCJw3k6DzCadlg5dfmRS4nmQjd9t1Z7nOCeU1wExVvndaHBl5y+2y9s8/U80aM10fqLnh2itlyxo1Ysbn8mbIsJvd2uS5rCOdoPutt9+Vh52a1nZtfXub+RB0r1q9Wnr2HhDTP7zZxxPbtZX2J59g3rqvuQTdZkV6vVza9SJpvt++ZlTgqz4cMGzk5q4zdCatXX7uOWcGzs9IBBBAAAEEECh+gbIWdJ955pmiDw0mK5UqVZLHH49tPcc/f1jr8a83qu+jcl+YoDuqVxj7jQACCCCAQAkIROUHTQlQpLWJoJC7Zs2aMnjwYGncuHFa62AmBFQgH4Puu8bcLcOHpw5tUp3Bjh3Plf79+qSarVimF2rQfd9998vAQYPjTLXG83vvTi61Bw/idijLERdd3EXeeOPNuKVPPbW9jBg+LG48IxAIW+Cjj6fJnXePi1ntoS0PkosuSNx9yZ9OUPzI40+5TZXHLOh7k27QrX2Dax/h2ZZU+6vr/fW336R7z34JA99U265fbxsZMrBvaJ85197QM62a68n2K1nQ/fvvi2X0mLGiTconK6UddGuLAP0HD5O5c3+J283DDjlYLjy/Y9z4MIJus9Je3a+Xpjs1MW/jXvUBgc5d/y/mutEmUu/67y1SoUKFuPkZgQACCCCAAALFL1DWgm77eBLppdNEe1jrSbQPURsflfvCBN1Ru7LYXwQQQAABBEpQICo/aEqQJOGmgkJu7at4yJAhhNwJ1ZiQSCDfgm4NETVMDKto+KghZEmXQg26tdbz9Td0i+n3XUPusfeMEW1WPupl0aJfneuzs8yc+aV3KC1a7C/33D1GttqqljeOAQSKS2DgkOFun9j2+q+4rKvst+/e9ihvWJv8HjP2Xlm5cpU3LtFAOkH3L7/Mk179BgWuYvfddpUWzfeVbbbZRlauWinff/9jwiartT9x7Vc8qOjnSI/e/WXRr7/FTa5SubLbj3hR4x3cad87/YO//sbkmBruZqFWTvPZnZxmtMMoF19yRcw2qlatKk2Kdkx71dr3dFBf0drHuTZD/9TTz8aEs4lWXJpBt+7rqP/eJZ9PnxG3e9psvh6fHqe/BAXddZ3+wLffrpF/VreW+O+LFye8XrX1gv/eOiJwO2Zlw28eJV99Pcu8dV87X3S+tDzogJhxvEEAAQQQQACBkhGwA910AuCS2avst2IfT6K1pHOcYa0nOnp2TAAAQABJREFU0T5EbXxU7gsTdEftymJ/EUAAAQQQKEGBqPygKUGSwE0lCrm1JndRUVHgMoxEIJlAPgXdfzs1sY5vd5LMmhV7g9q//3vttZfUr19Pli5Z6vRhOktWrUoc4Oyww/by5huTQqvV59+XRO8LNeg2Ht9//4P88MMPUscJMzTg1lCorBQN4WbMmCkLFi50gprtZPfdd5NyaTTDXFaOn+MoPQH9rLvsyuvidmDM6NtEA2B/mT9/gfToM8A/2n2vofaSpUtjpqUTdAeFiHojT2va7uh83vqL9t89cPDwuGau/+V8Llx71eX+2d337035QMbd90DcNF2/bke3Zxdt2rz/wJvcv0l7vA4PHzpA6jnBey5l/fr1okG3XY44/DA579yz7VFZDb/u9JX+8GNPBi5b03mI0d/MfGkG3bqfur/+sp0TWPfr1S1hjemgoPvSLhfJAU7f24mK1hgfN/5BmT1nbtwsV/3fJbLP3nvFjTcjgkz1IQxtzp6CAAIIIIAAAiUvYAe66QTAJb+HmW3RPh5dUpsp9xc9znSaLtffmf5iN4teFrz8x5fofVTuCxN0JzqDjEcAAQQQQAABicoPmtI8VYlC7kGDBkmTJombcSzNfWbb+S+QT0H3Rx99LGecmTg4uPzyS+WiCy+QWrU215xds2aNTJjwnPTqnbiJ8vH3jZNWrQ4v0ZNR6EF3iWKzMQQKRODzL6bLbU6NWrs03nEH6de7uz3KG57jhIV9+sd3JdDhlBOdfotbyaVXXOPNqwOpgm6t0XtRl8vjah4P6NtTdnD6xU5UZs+eI30HDo2ZrH0u33vP6Jhx5k3fAUPiAs5tnZYh+vbuFhjo63Lab3SffoPjmhf/92GHyAXn/cesOqvXhYsWSTenGXW7nNbhZDn+uDb2qKyGX3rlNXnSqc1tF31ooWvnC+Tvv/+R20ePsSdJaQXdr016Ux594qmYfdE3es0M7N9LqlerFjfNjMgm6NZl9aGiG7r3yficajPw13XrZTbvvupN4rF33R4zjjcIIIAAAgggUDICdjBcFoLb4j6e4l5/yZz1zLcSlfvCBN2Zn1uWQAABBBBAoGAEovKDprROSFDIXaNGDdGQe6eddiqt3cpouwud2o+/zJsnC5waZvPmzXduSq+SevXqScNttxVt2rhJk6Ksa30uXLjI6S9yrrPeeTJv/nzZsH6Duz59ACDd9eoNVQ1a/aWac/N2b6vm0BKndtqHH37k9k+pTbPq/us52H//5gmb0vzCCSe+++57me/sW/kK5Z3mWpu5yxQVNU64jO7H7NmznWUW6GBMqVKliuy77z7euKVL/3BqNn8tXzrNKa9evUYaNWokRU0ay7777JOypmkYQbc+hTx9xgz33C5YsECWLVvuNF27tdTfdF71eNMpjz32uPTo2Ttw1gsu6CS9e/UMnKYjX3rpZbns8tgad2bmHj26ycUXXWjeinoF1Rr3u3oLbBpY6tR+nDXrG/9oCVou3aB7xYoV8s0338iXX37l1q5s2LChExhtL9ocdqr+RKN0zerf+k5J+lU1qMX1ObF27Vr5+eefRT8r9G9q4aKFUtkJkxo53mreqJH+F9+Mrtkv8zp9+nSnBYHV5q332rTpTm5zzd6IJAMrV650z7fWCNe/l9VOQKc+GuRt74SFu+yyS5KlN09KdB1vu22DmG4sdL6PP/7Y/dxd7DQJrNto6jQZ3azp/7N3HnBWFNkaP6s+V58BxYCi7hqIKoKikjOSJINkQclxYMiSYcg55yBIzjkjkhFEEVARzAEkmN11d5+7++pr6DvVfbv7hrl3mJ756veDW11dVV397753uuurc042uf3225M7jGMuYAWvzvec+vfD9z8Yrt7h4vohZaWbL2/eqLwuwAvEFyqm8fnvzqvfn++M31iIsfcpBvhtvl/9e+SRh137/umnn+Wjjz5yPPMsWbIYfz8cd14tPHP2rHyvRDWn9Pzzz7ke16l+OGVLl6+Srdt3WqrWrF5FqlauZCkzN+xCN659u9bN5T51brCCbt2uk1nV+AwldF+6fFm69bT+RsPKemC/XpZ+nDacYlxPnzzO+P3U68PFevtOwVbrE8eOCHm/OgmcENRnTZ8U8m+hPgZ7/oMPT8uosRMsxW1aNpMCzz9rKYtmwy50F1LutRs3rK+eh26Sd997P00I3cfefU8mTZ0ZdHoQ5BEHPXPmO4P26QXRCt3ow2lxR+5cOaRH10T9EEH5hMTuQdbwo4YnyT133x1UlwUkQAIkQAIkQALxJZDehNt4n0+8+4/v1Y6+d7/MC1Pojv4asyUJkAAJkAAJpHsCfnmguRYXwknkvuWWWwyRO5sSKtJyguBw8OAhmT5jpuzff8BzqDinli2aS6NGDQx3x56Vr+48evQdmTFzliCus1eC++o6dV6SJo1fFiwQcEoQHJ/K+4zTLtm7Z7cS47PImLHjZMaMWY51cuXKJQMH9BOIG2aCiDxi5GhHURV1YGU8dEiSIcaYbfTPESNHyfTpwZPLcFP/5q7tKoblb9Lztd6yadNmvVkgj3pt2rSS2rVqugrqKRG6IRouXbZMFi5crLwyOIs9GAzYNGhQT+rVrRPkcjYwWJVxO1/UWbJ4oRQsWECvbsnjXitcpLgSMr+zlGOjoRINBiclu+/1Oucd27cKREun9Obut6RZsxZOu2TXzu0WUSyU0I2V7EOGDpcFC95w7O+uu+4yrt0rTRq7imV+umfx/T518rjjucbzdwKLE3B/4nfCy8U9Bla4UCFp27a1FC5cyPX7UqVqdUt8bvOEqlWtIuPHjzU3HT8/UjFjFy1eLIsWLXHcbxZmVyJ0k8aNpXr1qgJubsntPsbCoUMH9xkC+shRY2T+/AVuXUjHjh2kXds2nt9L18Zh7EBM8+XLV8h8dZ97/UbgPPH7gN8r3PuhEtz5rV+/UaZOmy6ff/65Z3X8DrZt01qqVq0c5FbwE+Vi/4VyFRzbY7HJ8mXe16p0mXKuxz986IARYsGx8ygLByQNky+UdbSeBipr7r8qq26nZArdEHvrvlRLXihbKnBvRyN0//HHHyqe9JuWWNXZ1AKWJ5/I7XR4S9nQEWPkjFrwpacJY0YoDx3WxRafq4ULAwcP16sJ4jmPGRlsmW6pdHXDSeDsr9xqP/LwX52qh1W2d98BmTt/oaUuXKjj3FOaTKEbsafhzhsuts2UFoTuTz/7XBAX3p5wTw1UlvwPqkVCoVJKhG68H3TpYV3kFs7iCri+hwt8PTVv2kSKFi6oFzFPAiRAAiRAAiSQCgTSm3Ab7/OJd/+pcMmjOoRf5oUpdEd1edmIBEiABEiABDIGAb880KT21XATuZOSkiR7mFayqT1m83iwfG7eopW8956zuGXWc/qcM2eWlC5V0mmXUQbrzPYdOoYUuO0dQEx5Y8HrFmtos46XaLhl8wYZpQQjCJ2h0sI35kuRIoXljYWLpF+/AaGqG/tXrlwm+Z8JFtndhF8IN28smCcNGr4sXznEr7QftEaN6jJ82JAgkQf13MQy7Nu6ZaOrdemePXsNC+pQ4iH6MRNia0+eNMGwKDXL9M+hSvidNXuOXhTIjxo5QmrXrhnYdsrAMhoWmvYES13EEDVTtOfsJXRv27bZsNQ3j+EldO/b+5bBDtbBoVLx4sUMZrepWK325Kd71k3ojufvxDIlsPbs2cuOLeR20aJFZNbM6UGWpmjoJnRXqlRRpkye6Ng3hPyZM2fL8BHBYpFjg6uF+J7PnDHNdeGF230MoXi7uh9fbdpCwrnHEEMdx4H1cyzTvn37pU3b9iEXGOjHxH0yauRwqVjRWXxGXZxT02YtPYVzvU8zjwUAy5YuFix80lPFSlVcFyO99+47huW5Xt/M47e3RMnS5qblE4tysDgnlgn3kZPb8GmTxhnWv07HOnf+O1mgFnq0aPaKIRbrdaIRuvX2keZhpQ1rbT29Ptvqhh37jr7zrkyZbl1QFom77hmz58mhw0f0w0i9OrWkQrmylrJINlavXS/rN26xNBk/erjrvWGpGGJjjxLR4ZUAY4SXCT1da6H74qVL0rtfkmVhgzm+nt06S66c4XlrSYnQjcURWCShJ8T2xqIAr7Rx81ZZuXqdpUok95GlITdIgARIgARIgARSRCC9CbfxPp9495+iixnHxn6ZF/ad0I0XPzwYX7502bh8D6sVwNWrvpiiSwm3jsdPnFQv0mfkw48+losXLxpu6h7PnVNZuuSQfE/liXg1PVY873rzLeWu8oya2PvJaJ8t26OSM0cOY2V1SlYumyc7b8Ei+fnqpOEN/3ODwE3X9ddfb+6O6BOrat9RL69mqlK5ojymXJXq6fTHZ2Xrth16UVD+xj/fKHfecYdySXefchX6jGdMqKDGLCABEiABEkhzBPzyQJOa4Pwscv/888/yUp16hrvuaJm5ib8QuVu1bit79+6LtmtZvWpFkNjtJRrWVdbgEM3CSRCZZsyYqkTZuuFUN+pA0Nq2dVPQc6Cb0A2h5i9/+UtIK3l9AOXLl5Pp04LjobqJZWjrJnRHIuLrY0AeQtamjeuUBWKwdd3KlaulW/ce9ibGNtq9/voceTZ/fsf9kRRGc87oP1ZCN67Ftm3bwx4y3NSvWL406PnbT/esk9Adz9+JaEVu86LgGkG4tr/zRCp0w2V3YueusmHDRrPriD8XLVpgWJvbG7rdx/gNKqoW26xbv8HexHX7hRfKGmK3a4UIdyxctFj69u0fYavk6tPUb1UFdQ3s6ZQKz1CvfsOIxHO9D/x2rlq5XO7W3BfPmjVbhg4boVcL5HEPYBGDU1q8eKn07mN1423Ww8KiuspCPZbpl19/FVgr25OTWGyv47SdmkL3u8eVC+7J1ljTWe69R0YMHRQ0tMNvH5Xps+ZaysuUKiEvN6xnKXPbmDNvgew7cMiyu4Cyzm/TqpmlLJKNmXNel4OH3rY0mTdrqoqh/R8VLuW8CjPylXz59TfyX7WNsB3wAPPk47lDhp+wdOiwcS2F7l+Vx5jefQcFuf/GMCEyQ2wON6VE6MZc1J69+y2HqlGtslSr4j03d0DNO81SVt16ypE9m/Tq0UUvYp4ESIAESIAESCAVCKQ34Tbe5xPv/lPhkkd1CL/MC/tK6D71wUfGKuLff/89cFEQM23Y4Ohf1hEvcejIMfLNN98G+rRnYGnSu0dX1xXZen1MmixeukJ27d6jFwflw3kJCGpkK2jTobPoLFo1f1UQOyrShFXobRO6WPrCCydePPWEVc3z5ke2Ar5k8aLSqEHdFL9M6uNgngRIgARIIPUI+OWBJrWIOInciBUNS+4cajFbWk+TJk2RsePGp2iYEMaOvfN2kIWTm/gbycFgwbh+3ZqAC1e09RINI+k72rqIP4041HqKxbnq/TmJNm5iGdo5Cd2IN169Ri2924jz4L9yxbKga3tSxfmuWs3bahtW4Q3q1zUsPqONLxzpOZsnGCuh2+wvks/evV+T5s2aWpr46Z51Errj9Tvxyy+/SN584YswFqjahtN3MlKhe/acuTJkyDCt18izYPfW7p0WcRa9eN3HkR9F5PV5c6REieLRNLW0icVvBDrcuWNbUFz3+g0ayeHDVsHRcvAwNsqUKS2zZ80I1MT7ebHiJQPbegZeJOBNwinBY4lb2Ix3jx2RO+/0jlvs1KdXGWK6v9ZnoKUK3F1PHj/aUhbuRmoJ3WeVe/jhI8fKv5UIrCc3N9JOFryRzMV06tJTflKL7fQEd+Ddu3TUiyLK292u33zzzdK0SSPDnbk+T6J3ivAUZUuXlGrKWAKxrKNJ10rohoEG3ORDxLenBnVfknIvOHsysNc1t6MVug8qy/yZykJfT3CZPmr44JBxwT9UoSJGjpmgN5UHst4vQwb1s5RxgwRIgARIgARIIP4E0ptwW69evYDHGzzzLV26NKYQ491/TAcbw878Mi/sC6EbFkLzFy4JcnWF6xXJy5X9+mKyZeDgEfK9cuEZKiH+VP8+PSTUxN2kqTPk2LvhuQKFKN1CxSO6Tr0URJPsQvdDDz0oSf2tcZLC6ddpJXeshG4cHwsFBvbrJXj5YSIBEiABEvAXAb880KQGVTeRe9CgQa5upFNjXOEeA4vxIDK5ubSGyFC8WFFl8XSfHDlyVDZt3uIYVxnHs7swR0zqgoWKuvYNARVxr+H+c8eOXZ5ue00X4+Z5XWvREOOeN3e2ORzjM1yhGxaceHYMFasWghkEmBtvvDFwHC+xzC50Y0K/QsUXXd2lYxwVKpSTrFmzytdff2NYsbrdB50TO0mHDu0C40AGcXcrV6kWticAuAcuVLCgFCpUUPLnfybsZ91IzlkfYDyEblyTrGry/awtdq1+XDN/9Mghi9jpp3vWLnTH83fCy+MA7tEa1avJM+p+QcxoCJW4H5wSfk82rF9r2RWJ0A1X+hUqVra0t2/ATXpW5TL86DvHPL+/ThbXXvex/TjhbMfC3Xao3wiMA9cgX768ckl5TvNyrW4XmU+fPi1wM+6W6rxUWwoUeF5uUJNNYLN9+w7XvxXvHjuqhOg7Al299FI9eefYscC2mcF9+/7xY0GW/TjPx594yqxm+bQL6ZadKdiAYDxkuFXUTolwF2uhG0L25cvfy3//+x/55Zdf5fx3F4z7GkYE9oRxDx7Y17LYzKzz448/SWK318zNwOf0KeNDCsaYb+nSPXieIpyYzoEDOWTQZzhzOQ5NJbNa8NCjW6LAgj3SdC2EbsRCHzN+kjhdt/IvlJH6dWtHehoSidCNubNvvj0nGzdvU14QTwcdq07tGlJJPWeESnDb36uvdWHIHZkyyfgxw0M15X4SIAESIAESIIEoCSxcuFAKqvfzbNmyWXoIJXR/8sknajHrYWnUqJGlHTcyFgG/zAuneaH7pHKDNnXGHIu1sX4rpUTo7t1vkGU1LFxvFS1SSB7+61+MicK9+w9aLLNDvbDufPMt42XBHB9e3F6sWF5y58ppWIN/+tnnyiJ6kXx34YJZxVhxXLxYkcB2JBm70I22Q9VKWEzKRZLsK6HRNpTQjdXPnTu1txwGluGIv4iXl+0737Rcs6qVK0nN6u4TIJaOuEECJEACJJBmCPjlgSbewJxEblgOQeTOlStXvA8fk/4vXLioxGjnZ44WzZtJr149LceB++IyZcs7xluFcDFiRLI15KJFS6RPX2drnGFDB0u9elZ34WvXrjPcBlsOeHXDbq0ZSjSEMD9u7Gh5TnmiweLI2bPnyvgJzjF5zeNBqOneratyZXqP8cw3KGmwqwUg+j900OqOPZTQjTbz5s4K3Bs4B7gL9nJZDDEdorqZvMQyu9C9Xrlf7tgx0Wxq+axWtYoMHNhfMqmJZDNdvHhJunTt5uhiHWLX24cPBAlIocQss2/7J1jUVZZmVatUkUdtYXHsdSM5Z71tLIVuCGhzVSx63E9/+tOfjOfZceMmuMYoxzjs7pD9dM/ahe54/k70HzBIFix4Q790gfzhQwckS5Z7A9vIzJgxyzV+9p633rTEdI5E6EZ8cLeQB3ChjXjRuG/N5OUKG3XsceC97mPUx3csadAAKaLcmMPS4MSJk/Jarz6ugrr9GqGPSNPadeslMdHdNfCY0SOlZs0agW6PHn1HxRJv7iFIJ1tGb1aLotq1Twi01TOw0IbArKdj777rGkJi7JhRUqNG9UB1L/Zr16ySvHmtojbijzdu8mqgvZ6ZOGGcVKnivcBBrx9u/rjypjF+0jRL9SefyC1dE52ZWCo6bMRa6F6zbqOs27DJ4UjWIoQtw5hvvvkm6w5ty8kqO7cK9datc0fXBU2///4PZYU8VC6ovzv2dM/ddysr4CR7cdjbzVq2C7JID7uxqojv34ihAw3RO5J210LofirPE/KWzVU4xpwSt99OQnckHFAXCwVebljfCM8XTlvcD206WJ9XcB1mTfN+bgunb9YhARIgARIgARIIJjB79mxZt26dEaoM81e6J0IvofvMmTPSr18/432gWrVq0rx58+DOWZIhCPhlXjhNC92Icz1wsHVlZwnlCvvuuzLLqjXrjRspWqHb3neFcmWlXp1gd49Ll6+Srdt3Bm7a/n16ilt8bcTmQowupFw5sxsvijfccEOgLTJwN5XQuUdABIa18+ABfSx1wt1wErohmsNdV7gJq7u79gw+fiihGyugx44a6noYWP6MGD1eIO4jRXudXA/AHSRAAiRAAqlCwC8PNPGE4SZyDxw4UHLnzh3PQ8e0b7yolK/gHDuxVasW0rNHcIzRN9/cLYuXLDWskmGZnCnT7XLbbbep884lRQoXDowPrq3fe++4YVn1w/c/GOI4rAIfe+xRGT48+HkB8TMfy5Yz0F7PNGnSWAb0T46vGko0dHKj6yVkQcCEkAUR00wQyMtXqORqEf3hByfU5P/NZnXxErohSu3auT1ItIOlbMdOnWXTps2BfvQMFgNgUYCZvMQyu9ANQQr17QmWr4h7jklke/rxxx+VW+BSjkKWW+xhxDNOcBHU7f07bcO9+SAlutvFKbNuJOdstsFnLIXuHdu3qpXuj+ndG/nBg4fKnLlWV61mpeLFi8n81+eamyHd7aele9Yuosbzd6J9h46u97+TS+k//vhDxYbvaXgU0H9/kMcCDnyaKVyh28viF305Ce4onzhxsowbb3W3i3KkhIT2ktgp2f2y132M+nv37BZ44tITFgsXLVbC8fuIenZLZ71tOHm33wi0naAE4KoOAvDkyVNlzNhxjt0vWQyrkALGPq+FTnbvH2ZnU6ZOk5MnT6m/J7dKptszGdcSf1+KFi1qcYt++fJlee75QmYzy6eT9wmv7+mpk8eNCT5LJzHYQNxpxJ/WU7GihaXZKy/rRWHnYy10L1qyXHbs2u16fPx96JrYQXLmyO5ax9yxcfNWWbl6nbkZ+Mz/TD5p36al5e8qduJckoaMsBgXBBqpDLzmwVI5moT5hpZtk7939j7Q90NqruVnZYmMuR+3hO/ioH69gsbuVh/lqS10e40F1tAQ6+ExJ9KUUqH72fxPS+sWTSMOU/dK8zZBQ402pn1QRywgARIgARIgARIIEDBFbrMAYfcwj2Uaa7gJ3Vjk3r9/f/n73/9uNhWK3QEUGS7jl3nhNC10f3zmrAwbOda4eW5Xk6rt2rQwXsC273hTFi9bYZRHK6BOnjZTuUF7z+gDq5f79gqe3DXv2qShIwOCLR7m8RJnT3AhNXrclVWoeNnAC9v1119vr2ZsH377qEyflTwZhhjjOI9Ik5PQDffg0yaPs7i+9OoXHMHTnlIqdKO/H9QEauduvQJdz1BuzaJ5AQt0wAwJkAAJkECqE/DLA028wDiJ3DfddJNhye0nkRt8flCuQ/M/e0WYcOIFq9/KL1ZS8RUzO+2OaZlXzGe7W1wvoRti1/jxV54V9QEeOnRYGjR0FhkmTRwvlSsHC/4QdCDsOKV3jh42rDDNfV5Ct130Mtvg08sq2m457iWW6UI3BLKnn3lWP0wg7yYymRVGjRojU6dNNzcDn3BdDhHJKcH1ea/efRytwZ3qO5X1eq2HtGgRvCI83HO29xkrodt+7+nHuXTpkjxfIHlxh74P+U/Ong48+/vpnrUL3fH8nfASIeEqHPfcU0/lCXC0M/baDlfoxuKdZs2D3+XQd0NllTg4aZDjYeBO/dnnCjrugxU4LMzN5HUft2/fVrp0tlozmu28YqOvX7da8uTJY1aN6NPrNwJjf3PXDkfm8OqR72nn35akpIHSqGEDYxxwRd6qdVvHMaH/Pr17KQG7iGWxkGNll0I3kR4LZ9atXWVpVahwMceQG25/KyyNo9x4+8g7Mm3mHEvrsqVLSqMGVk8mlgoeG6ktdGMomEN49tlnpHGj+nKLmgB1S39Tk51d1Ps9xmhPsOwtp1xow6sdFqkgpveWbTsDC/zt9bENkTma0GtoC8vgaTNny7ffnlde5X4KWHaXKllcateoalnUgPF8dPpjmTJtluPY3eKS4zhOKS0J3RhfAbWAD/M3kaaUCt04Hu6dAgWeM4wt7EYebuOxW+Kjjzkzp7hVZzkJkAAJkAAJkECUBBYvXixLliyxtMYC/gEDBsjjjz+uPDrVtMS0Xr16tXz44YfGfiwQ1lP9+vWlQYMrz/96OfPpn4Bf5oV9IXSXKlHMiDlkxi1MqdD966+/SYfEboG7EKtQC6qHc7dkF6YnjRtlrD7X648cMyEQqwiW4bAQ90qwSDdXxZQtUzJmQjeO2US9oOIFL1SCZVHr9omBHzS9fiyEbvSni/E9VQysXDlz6IdhngRIgARIII0T8MsDTbwwpiehGyFGYBkHwcYrYXXv00/nkzzKGviJJx43XoDCnby094uXo8+Ud5evv/lGzqnYjl9+9ZV8+MGHjjFXzbZ2sdFLNOzatbO0axtsGYTjlCxZxuzS8rl+/Rp1bk9ayrCxfMVK6dEjOP4o9kUidNtdKqO9nqpVr+Ua//azT88ErMq8xDJd6P5A8UT8bKdUq1ZNFTv6LqddRtmWLVsdrdjtruntHeBeMuK4K+v01WvWulqh2tvp20OHJEn9+vX0IsMqHaKWU9LP2b4/VkL3iuVLlNjjLOzhmF4Wyfo94qd71i50x/N3Au7C4W3BK2E8BZ5/TgneTwk8Ejypvqt2l+ZO7cMVuufNmy8IVeCUVq5cJvmfecZpl1HWtm172bJ1m+N+faGD13d31szpUras82/Ttm3bpXWbdo79r1ixVJ7Nn99xX6jCUyoUGPg4JbdFJ2bds0qs/OXXX8zNwOcDWbMG3LvjN75M2XKBfW4ZjB8xwPOoxQxPqIk1hDLQPWu4tfMKdXHk7YNGCAq09RrHnNkzpXTpUm6HSFH5kaPHVKi12ZY+3BbHWyq5bMRa6F69doMSnHcYR4N3Oa906623GG7IIVa7pY9On1Ge25wt/d3aoPy+LFksIdxQ9rjyDtO9i7tVNuqEmxBD/Prrr7N4erC3PXfuvPRPGhY0/4HQdQP6Ov/9t/eB7bQmdGNMLZo2UZ52nBfjYL9TchK6ITpf52Cw8R81d4R4724J1xKeAa5T7b0S+oDQrScK3ToN5kmABEiABEggtgScxG4Yb8BiG67JzedDePmBa3NYfMPjnZ4ocus0Ml7eL/PCaVroxkvet2pSFBbXekqp0K2/mOCherqyNHZy6WgeE1/41u06BR7sE9q1lmeezmvuFqxsbpfQxdhGf5MnjPGMbRVomMKMLiIjZtOJkx8YPWI19YihztYI+iEPHT4iM2ZfccGIcWPFvenWKxZCN9ySNtVeYqK1XNfHzDwJkAAJkEDqEvDLA008qTiJ3VgF6zfX5WA0a9ZsGTpsRES4IDxVrFheypcrJ6VKlXS0/NM7PHnqlFo1vEwOHDjgKKLqdZ3ykQjdulWh3tc333yr3HKX1IsCeXs8XXPHGiXYdu6SvBDSLMenLmJi28uiWxe8UNeeIKZDVHdK7x8/Fpik9xLLdNHXS+R1OkY4ZXZX3F5t8Jx85OhRQ/g+qFz4vnPsmFd1y759e9+SBx98IFAW7jkHGlzNeDGwX+8FbyxUL/UD7V0Y225uq83KEyZMco3/vn3bFsmePZtR1UvoTmv3rF3oxgnE63cCi3xhFf+3v/3NRBrWJ0TvShUrSKVKFYNcfpsdhCt0e3137d9zs2/z08vrg37veN3HmzdtMEI/mH3qn16xq1MidHt9P6ZNmyIVyocWqfVxOuXdrK6d6ppl8GJRrVoVdfzyhgBults/vb5P48aOlurVryz0Qfx3xIG3J9zjcI1vLpq370/p9ocfnRYsetdTKI9xel17PtZCt94/FrL8qKyfz537zojbffaTT/XdRv4m5QJ7wtgRnp7Y9u47IHPnLwxq61YAy+0undoLYnzrKVpLZL2PSPNffvmVIXbb201U56yHQ7Dv17f1+SSzPCXu6s0+8BmOS/Z//uuf8ttv1t9RzOcMHTzAiJmt9+eVdxK627ZqLs8/57yoBt4hvvr6a1m2YrWjO3q4sO/QtpXXIYO8/qEyFlhMHj/asx13kgAJkAAJkAAJRE/ASeyG1108d+D5EAkLUPG8/E+lx+mJIrdOI2Pm/TIvnKaFbrdbJ6VC9/adyvX50iuuz8NdRaxbbDeqX0fKlkleEf7lV19L/0FDjeHqL7UXlYtDvARduHhJYD0NARqxr7I99qjbqUVUrgvdXRMTAq7T0Um/3j3k0UceRtY19R04ROB+Egmxzy9evKhcep0xtmMhdO8/eFhmz51v9IcXr9kzJoe1at9owP9IgARIgATSBAG/PNDEG5ab2I0Vr2Z8o3iPIRb940WmjbIYhPARTYKF5aiRwx3PGSLWuHETZPacudF0HWjjZ6HbSTQMnNjVDOL8It6vU3pr907561//auzyEst0odvLEt3pGOGUQbCFcBtNgigF1/Hbd+yUVatWe3bRonkz6dUrWfgI95ztnXoJeZEI3aEWKSxdukxe69XHfnhjG3Hfn1eWyEhewpwfhO54/k4cOHBQGr3cxOAUzX+Ihd2uXZugBTfhCt2JnbsKLISd0qeffOxpjbhw0WLp27e/U1PZuGGd4QEDO6O9j+MldHtZ0q9etcLw4OF4UhEUnj9/XmrWquPoNjycbqqoGOEDB/SXO++8w7G6mzcFtJuoYowjNXmlqezduy+ovZdL+qDKURR8pd6n+6n3aj1lvvNOGTvqyvyAXh5OPp5Ct/34J1Sc9LETptiLpWL5F6TuSzWDyvWCzz7/QiZPnWkIl3q5PZ8922PKSjxBhU/5UXr2GWDZXbZ09C7eLR1FuJGQ2F15KvjV0qp3z66CsYaTrpXQDUF4aFJ/+eWXX6VP/6SgoWK+CfvdQujZG0QqdJvtMSG+dv0mY7GEWWZ+9urRRXKoZwi3hPtmkIrbrqd77r5bRg0PPh+9DvMkQAIkQAIkQAIpI+AkdofqkSJ3KEIZY79f5oUzpNC9dPkq2bp9p3EnFitSSJq92jjkXTln3gLZp6xUkOCWHO7JzXT8/RMyftI0YxOrX1s1f9WI02XGADfrmZ94AUns2M5w32WWRfOpC92w4J43/w05/fFZoyuMA6tx3dK3ym1X737JK97xQvTGoiUxE7rfPf6+TFEvvqZ7q0IFnze4uI2H5SRAAiRAAmmTgF8eaFKDnpPY/b8qliXE7pw5c6bGEGJyDLihQlxmxIONJkHMXbN6ZcB6FX2cO3dOatWuG7XIoY8jvQvdXhaluoVzuGLZipWrpHv3ZLFYZxlt/pFHHlFxe7dH2zzQ7rvvvpMeylW1k/iESpHENtbF/cABrmZiJXSf/uiUpxWjlzW47vba70I3sMbjd8K8bu+9d1z69R8gcKkdTUJsaCwY0FO4QnfXbj1cF2CEWujgZjGMceiW2uF+d/XxIx8voXvlytXSrXsP++GM7eXLlshzKr5vLBK+74OHDJNNKqRBNAmW+0sWvyH4u2pPO9TCmZatgsNU4O/R8ffeMSxPnsyTz97M2F6yeKEULFjAcV8sCr//4Qfp0r23pSt4i5s1baKlLNyN1BS6MSbMi2B+RE/wWjNt0li9yDV/6oOPZO/+A3Lu/HcqNMoPRr277spsxOourcKpmd75nCzfX25YT8qUKuHad7x2IKY6YqvrqU3LZipsQnjfhWshdMNwIGlgX8l6/33GsHfu2i0LlyzXT8HII+xfk5fDi58ZrdBtHnT4qLGB+SezDCEBERrQLR179z2ZpOaJ9PTIw3+V/n1i+xyj9888CZAACZAACZDAFQKRiN0UuXnXmAT0Z3yzLC1+Zkihe8q0WXL02LvG9ajyYgWpVeOKuzOvC7RqzTrZsGmrUeW5/M9IuzYtAtV37d6jROKlxjZiY8NK+pNPPwvsd8rg5XFQ/16C1avRJrvQfV5NLoyfODXQ3dSJY9VEwc2BbT0DV2NwOYZkxqRCrK1wLbrxolXHtsobK3svXbqsXnbOWFxZ4cWlz2vdwl5ZrI+TeRIgARIggWtLwC8PNKlFyU3sTkpKkhw5cqTWMGJynO++uyA7d+2SXbveNCwQI+m0aNEi8saC1wNNIKJATPFKEDURbze3igGeJ8+TMnDQYPn888+DmvhZ6MbJhBJLO3XqLOvWbwg6bxR8cOpE4NktXLHMqx6sHSEERZruxaJMZTmLdOLECVm/fqNjF4mJHUP2D8G0VOkXXBdB6OKi17noFrP2wXjF8I3Eonvvnt2urrFxTK9FCrvf3CEPP/ywMbT0IHQbJ6L+i+XvhNknPuHt6vDht2W38i6xafMW1/tDb6Pn9YUFKA9X6B41aoyx0Efvy8zr7sfNMv1z+IiRMmPGLL0okNdjRXvdx14LNuIldO/Zs1deebVZYKx6BtbQ+J2IZTp9+rTsVB7Utm3fHvFihu7du0qb1sGuj/E7kvvxPI7DXLF8iWHh2qx5y6D9d911l7x9+EBc30PdXE3PUd7MwrWs1Qee2kI3vovNWrXXh2DkZ06dEFN377qxgXkwLNaHEUBqp42bt8rK1VbPDpGI7tdC6O7ZLVFy5bQ+Z+peB3WGCe1VqL18efUix3xKhe4zZz+RoSPGWPp+IOv9MmRQP0uZvuEk0Od96klJTLDG7dbbME8CJEACJEACJBA7AuGI3X4VuevVq2e4YwctuGFfuvSKXhcrevHuP1bjjHU/fpkXzpBC98DBwwOxqO1uyN1uBP2B3L7idMWqNbJpS7DVC8TgXLlySB7l5vPy5e/lveMnBCu+zQSxe4halZs5851mUUSfdqH73nvulg6J3QLxmuBuDG7H7AmxFtp26BywtjZXL0cidNv7dNt+pXFDKVGsCF2WuwFiOQmQAAmkcQJ+eaBJTYxOYjfERIjd2bNnT82hxOxYf/zxh3zyySfykYo1euqDD2Tdug3KMut7z/5NUejnn3+WfE87W0GBS7Nmrxrxde1W727ClN+Fbli753OZYP7Pf/6jRN+yrrHLP//simcegA9XLPvggw+lchXnRZteoprnxdV2eolv8+bOlpIlQ1vjNW/RylhQoXUbyIYbl3zG9KlSrlzwcy06gneCsePGB/rUM5EI3aHiFdepW1+OHrVaAZrHglVppkyZjM30JHSb54fPlPxO6P045S9fviwfq8WysPI+cuRIyPAKjRu/rFxdJ4spbr8niOs9ZXKyZa2XVf7r8+ZIiRLFnYZnlDVs2FgOHrri4cteKdwFG17fSa/vWkpidON3vdKLVexDNrbbtmkt3bp1cdyHwu3bdzguQnj66XxqwZKz8Kx3hpAWHynh+8MPP5L3lRe0UOEMvLxJ9FTeIeCG3Z7atW2jhO5f5I2Fi+y7pGXL5vJaT2dr9qDKKShA7Omf1N9CPY0ZOUTuypxZLworH43Qjff7/9p6/7Oa3EOsxXBS5269glyQDx7QRx588IFAcyxqh8tsPd3wPzfILQ4W+Hod5OHlrXW7TvJ///d/gV2IBT59ivPvdqCSRwa/R38okV5P4Z7z5Gkzxe59L1xxGMdLbaHbzRX+3/72N8ObAO4ZPcGjwEi1iMAtFIBZN6VCN+67Vuq66ilUvO1lalHklq079CZSs3pVqVq5oqWMGyRAAiRAAiRAAvEj4CV2+1XkBq2aNWsGnjfxPLR6tbcxRqSE491/pONJrfp+mRfOkEJ3nwGD5ZtvvjXuhVchxKr41KHSnr37Zd6CKy/PeOHDi5+Zps+aK4ffPmpuGp9YmTywXy+56aabLOV212CmNbWlUpgbdqEbx1y/cbOsXnvFSugONdk2fszwoN52v7VX5i9cYpTjBXOKcksGUT4eQjcOkv+ZfAIx/YYbbggaCwtIgARIgATSNgG/PNCkNsX0IHbDiuv333830N16660WhNi3b99+QVxUTKQ6JdOi0ktodXMZi/6zZc/l1K34XeiuUaO6jB0zyvHcvMRre1xsr7q6WOa10GDI4CRp0KCe41hQCNEB1ohwFewmiECoeuJJZ8uwF14oK9OnTfGMa4yJ8CJFSzgunIC15TtHDwfG5yX0vdyooQoTMCBQ18zgHKpWqymwInVKkQjdcOEMV85OCf1XrOQsFqL+Z5+eCTBMT0J3rH4nnJji3vj733+X22+/LcjyFW6wx4wd5+opwu5VIlyh+4BHjPDy5csZ97PTWL/88kspWaqs0y4jjIMe0z7c7669M6/7PyVC92+//SZ5nnrafjhjG4uRjrx9KOBJQq+Ea583X37HvwGjRo4wfqv1+mYe7XDMP6v3TPu7MP6eLFu2QpIGDzGrB33qiwb0nW7XDr+diP/stDhr3dpVApfo8U5TZ8yWI0ePWQ7TumVTKfj8c5aycDaiEbp79OonFy5esnTfNTFBnnwit6XMaQMCdnNl0W2GHDPrjB4+WO6++y5z0/iutk3oHNhGBhOIEKsxl+CVwAaM9IQ5gg5tg6339Tpe+Vlz58uBg8l/P1C3swoP95TyGBMqte/UNWAcYNZNUvM7D6l5nnBSWhG6MVZ45MM8jj099NCDMkjNR7n9bUf9lArdFy9dku6vJS84Qp+hhO6koSPl08+s3nxCxfVGv0wkQAIkQAIkQALOBPA+X6tWLcvODRuuaEOWQtuGk9gdrshdpYr1vXjVqlUx9QRkG2rYm/EWouPdf9gnmsoV/TIvnCGF7rETJsuJk1diwoW7enT9xi1KQF5v3EZP5XlCvUQlu/eyrwjGittB/XsbD/lO953dAnz44AFy331ZnKp6ljkJ3b/++pth1W027NG1k3IRmtPcND71F2FYfMPyGykSoRsCec0aVY12+n+Y2MPL1ueff2F5WX7i8VzSpVMHz4lQvR/mSYAESIAE0gYBvzzQXAtabmL34MGDJVu2bNdiSJ7H7D9gkCFew/JNFwQgdOzbu1tZ/gR7mBk5crRMmz7DsV9T6PaKjxypYIED+V3oxjksXbJIChR4HtlAgiDd6OUmrq58YZXYtWuyiBCJWNasWQtHC1hc2wP79wQsjQODURm8EGM8poUy6t57772GBWKxYkUlISH5WRcCr5uQ/MorjaVH925BghaOhYUUiZ27yrZtwZ6PsL9ihfIydepkZI2E+/LZ5wqam0Gf9njCENSGDRshc+bOC6prFkQidKPNpInjpXLlF83mxifOo3Wbdq6xxqtXrybjxo4OtPGz0B2v3wlYFXfv0dOwvMXfFX0BTZfOidK+fdsAPzPjxbF48WIy//W5ZtWwXZfDChQeKPTjBzpRmdWrVgislfUETwyI7b1mzVq9OJCHxTAsh80UyXfXbIPPSIVunMucOfOUm/Bdhtew/Pmflo4JCRYrXLP/du0TZLNyEe+UwB7XwJ68rN/XrlklefM+Zbigf6lOPfnpp5+vXtvvA90ULlRIFi6c7yi0VateywiLEKisZdz+bngJ71rzQBbhMt7avcvx+IFKMcroC8nNLp9/Lr+0bZV8X5jloT6jEbonTZ2h7p/jlq6LFC4oLZo2sZQ5bbx7/H2ZOHl60K55s6YGsXOy/G7V/FUpVND6907vDItZ+g4YbPFsh/3hCvF6X3rebkCAfaHiQ6MOYoqPHjcR2UCCUD9t8riwJ2jTktCNk3ByC4/yShXKSZ3aNZB1TCkVupcsWynbduyy9G33fqjvxMK0Fm0S9CIjj3j2WDTBRAIkQAIkQAIkEDmBaIVuHEkXu8MVudGOQveVBZ+xthgH27SY/DIvnCGF7vlvLJbde/YZ902ZUiUE8ZhCJcTgRixupFIlikmTlxsEmujxrlHYplUzKaAsQtwSJiWat+4Q2B2u+/RAg6sZJ6EbuyZMnma4SUfeLsp/pgToQUNGYJeRxo4aKhDmkSIRut3cZxkdqf8wEYFxmAsKUF6zehXlkqqSWYWfJEACJEACPiDglweaa4XSSeyGdTTE7scee+xaDcvxuNOnzzRiDDvthFA3ZvRIy4I0TE43afKqcu9ptVIz28MSFxa5iOEM0cIpLZg/TyCa6ukzZckDF9C62K7vTw9CN85n6JAkgVvv22+/XTE6Kf0HDJSzKp6lW7K7PI9ELNuwYaMkdAwWqnCsJ1UIHVxbPYY8RL6kpCGOboDRpm+f3tK06SvIGqlv3/6ycNFiczPoE/dB45cbGZatmZWr3h9/+skQxmfPnusqKKITu0CIsifz5PNsA5fV+ZTA9uOPP8mKlatcBXj0hRSp0I02EO4rVixvCP8ffqREkdFjjXjS2OeUINZDtDeTl0CblDRQGjVMfo8w28DbVLHiJc1Ny6f9HMydEF47d+lmblo+ze+nWegWXxwLHE6dTBbI4vU7gcUCjz/hblm7ZfMGFfLJ6uVhx46d0rJVG/MULJ+4P3Gfmilci27UHzx4qOfiCPxuFS5cyLAyx8Kg4SNGyZIlS81DBX3u3/eWPPBAsiVoJN9dvbNIhW4svrAvIsH13L5ts2TNmlXv2qiH+m4JHhPaqsU2WHwNS3t838HJKeEYuhV4k1eaui4Cwe9g/frWd+0vlXX8i5WrOX7P7Z4t7McPde30+omdOloW7Oj7Yp3/9tx56d1vkKXbaF1zRyN0O4m+GEyTRvWlVEl3d/zn1LghQtutue0e7MwT0+dEzDKIxIjJ7LRwH39rMPdgtzaHtXGSMgxwShCiV65ea1jpYxyYK8mq4j7bk1N8aNRp3UJZ0hdwtqR3a1O4UAFp2ewVNA8rpTWhG4txevdLkvPKE4Y9ORk+mHWiFbrhBWDPvgPy+lWPh2Z/+CxbppRxzfQyM3/i5CkZO2GKuWl83pcliwwfMsBSxg0SIAESIAESIIHwCaRE6MZRIHYjNWgQ/I5q7HD4j0I3hW6H2+KaF2VIoXvDpq2yas06A364LrP0VdK1alSTKi9WCFy8ZStUnKFtOwLbUyaMFkwAeKW+A4fI119/Y1Qp90JpaVD3Ja/qjvvchG77C9zEsSOMSVZ0ortVezx3LunepWOg71gK3egUL0DjJk4JiN0pcdMeGCQzJEACJEACqUqAQndo3E5i92233WaI3Y8++mjoDlKpBsQFN9e7GAKeXcqWKS2IkQrxGpbabgmCFIQpJC/X1tgPER0Wklgkd1xZjnlZ36J+ehG6cS7hJvDctHGdZaFBJGLZP/7xDxWDt6ryqGN1B6ofH9fg4Ycfli+++ELeey9Z2NTrII/74PCh/coz0a2BXVicULVaDUdhKlApiszuN3cYY9Kbvtq0uRGfXC9LSd4uEntZqUZzHIj8e/fstrh+9rPQHa/fCbDt1r2Hqyty7IeL6cLKChWLbBA6wet+njVzupQtWwbNjBSJ0H3u3DkpV76S5/2M7wE8HHiNAQdu2LC+DE6yCpyRfHevjP7K/5EI3fgO16zl/P7oNCYstK5Vu66rFbU5Dpy3m7W7Wcce13ulirmLa+uWYFldonhx4zcFHiTcFk+hffNmTaV379fcujJ+u9zO295ox/atyrtK6iw4w3tvm/aJYo+VbI9zbR+j03Y0Qjf+BnRI7B6ISaj3mytndqletYpke+wRI5QYRO1Lys35lu07BeHZnFJiQjvJ+9STQbtgud+pa8+gcojd1atVkSKFC8idd9xhhIn7QHlx2L7jzaDY32jcs1tnwbjsCS6t4dpaT7D0Rbzz29VzlT317D1AvrtwwV4sxYoWltpqziZTptuNfVhI9P7JD2S1mgOyi/qoEInbctRPa0I3xnT58vcCz33288OCi1EjBstt2t901EdyErphmODk8v5f//o/uay8ruA4Bw+97Xhd0efIYYPk3nvuQTYo6QYn5s7yL5SR+nVrm5v8JAESIAESIAESiJBASoXuCA9nVKfQTaE7mvsm3m0ypNB98PARmTn7iotDvDBNHGd9mXKCnqBeHH9RbrmRWir3XIU191w733zLeEnAPryIwfVSqKQL50XVhE7zMNyK2ft0E7pRr0v33gH3YHhZgTX131SMxXYJXQLdwP06LL7NFGuhG/1+fOasDBs51jgEXoBnTZ9kmcQ1j81PEiABEiCBtEmAQnd418VN7B4yZIghHIfXS/xrNW/RSnbtejPFB3p93hwpUSLZSqxc+Yqe1sqRHDAjCt1OcWQjFcvgGrrSi1UiQe1Yt0sX5Ua6XbAbaS8X9Y4dhSicMX2qlCv3QlCtaM7DS5yLt9A9c8Y0QaxyPflZ6MZ5xOt3wkuc1fmFykMQX7tmpcWtciRCN/pfr7wgdHTxghDq+OZ+CLhbNm80YtybZfiM9Ltrto1E6F62fIX07NnLbGr5hBeHDeuD3axjwUqZsuUsdSPdwHcNC1Tu0YQshGUoU7a8q5eOSI5h90RgbwtBuXCR4oIY7l5JX4zlVS+W+5YpwX/L1uTF7+i7lgr1VeXFihEdJhqhGwfYuWu3LFyy3PNYmKuA+2ivlDtXDunR1dlDCNqtXb9J/dvo1YXnPi/r6dkq7vZ+W9xtdOZmmf6h+rs3cswE1+NB5IXw63XO0XidS4tCNyAcUvNcM67Oc+lQcmTPJoiDbU9OQre9TiTbBZ5/Vtq0bObapFOXnvKT+r3Qk5cwrtdjngRIgARIgARIwJkAhe5kLnoMbZQ6hUa58cYbZelSd29daFevXj0j1BvyetKfKdE3XZfrdK59PkMK3V9+9bX0H5Tsiq1f7x7y6CMPu14Nu7vvgf16yV/VxIaZYJkNC20zjR2p3IFnvtPcdPzUhfOqlSsqIbqqYz2vQi+hW3/Rvfnmm2XqxDGG1fnylWuMLm+99RaZNG6UZYIoHkI3XiybtUx2kzdUuTVzcj3mdZ7cRwIkQAIkcO0IUOgOn72b2D106NAgq9Xwe41tTVhfN2nS1NOiLtQRO3VMUAJRcggW1H/nnXfkpTr1QzW17IcYc+rUB5YybPhZ6IYIBPHWLZZv0MmqAjf3utGIZWvXrjNiYjsdJ5wyuAXv36+P66LEyZOnypix48LpyrOO0z2kN0Cc6AUL3tCLPPOIj41Y4E4pEqEbsYpxjuGmunVekuHDk98pzHZ+F7rj9TsBPlu2blMuspPjv5vMwv287777ZMXypUFxqCMVunE83MuRXG99jLDkX7RwvuTMmVMvNvLRfHfRMBKh28ute+lSJVXs7llB40LB9u07pFXr4IUsjpUdCs3Y3PZdsfD6MG/ubCPcg71v+/a48RNk4sTJ9mLLNqzCYR2emun7H34wFpvrx4zGo1m0QjeO6yS26+MJlcccR5/XujlOCuptYam9eNkKvSisfL6n8kjHDm0scxB6Qz0Em15u96in70NIOlgKR5Mwnk4JkX8f0qrQDQa6Bz+dSe2a1aRypWSvhNgXS6EbYnqPbokC4wan5PT98Irn7dQHy0iABEiABEiABIIJUOhOZmIXupP3JOfCEahj1U/yUf2d88u8cIYUunFrwa2TGSuqRPGi8mrjhq53HGIPvXXVrVeWe++REUOt7umwsrx5q/YBN1GI34043m7phx9/lM7dklfgt1IW4oU0C3G3dvZyL6EbL8jtOnQOjCmhfWtZ8MaSwApap5fFeAjdiGuX0DnZld3MqRMEK2eYSIAESIAE/EHALw80aYWmk9iNGM2w7H5YuY1OCwl/mxHXN1LLboi4fZR4UK9eXcfTmDdvvgxKGuy4z16IGLhHlTg+adIU+y7fC90H9u8x4mXv3bsv6NzsBQMH9BOIy04pWrEMbudbtW4X0uLRfkwI7h06tHMVIMz6cCkNkcnL/bBZ1/4JK8vOiR2ljHKRf53LZDjawHX1sGHDPeOCm32PGzdGSpYoIU8/86xZZPmMROg+/dEp6dHjNVm3/opbfktHto02rVtJ166dHc/D70I3TjVevxPoe+PGTdLztd4hXWSjrp6KFy8mI4YPc4wFHI3Qjb63bNkqbdtZF+7ox3TKFyxYQCZPmiAQu51StN/dSITuH9X7ZLHipRwZjhk9UmrWrOE0NKPs9OnT8mrTFhH9RiCkBRaU5M3rHmcd/SYoK/mzZz9xPbbTDvQ9cuQweTZ/fqfdQWUff/yxVKhYOahcL8DvsC+B6IgAABPMSURBVD1Oub4/XvmhI8YIwojpCd7jnNxu63X0fEqEbvQDd+Sw7NYtTvT+3fIVypU1LNCdLF+c2ryrQpFMmTozMN/gVEcvgyV3s1cbuwqhqLt9pxLQlwYL6BDfsz32qN6dJf+2cok/e+6CiM75pVo15MWK5Sz9hLuRloVu3D/de/YNeCPUz8lu4BELoRvCdu1a1QX3z5/+9Cf9cJb8pi3bZcWqK0YX5o42rZpJgeec/3abdfhJAiRAAiRAAiQQmgDia+Md9FokhO0z43xfi+Prx4yVQB2rfvSx+Tnvl3nhdC10f61iMf2vsma+667MQfcSXgDnKQHbTD3V6tNcOXOYm4HP0x+fleGjrrjeRiEEcQjj9vTGoqWya/ceoxgP+wP6viYPPfSgvZoRt2tg0nA5f9XdGupOmTRW4FYr0uQldKOvWcr114Grrr9g1f37778HDjF5/GgVJ+2WwDYy8RC6J0+bqSZC3zOO8+CDDwjilDGRAAmQAAn4h4BfHmjSElEnsfvll1+WOnXqpKVhCsQCiNNwgeuVsmfPJpVffNEQZO+4I5NXVTlz5oyMHjNOYG3olBAnGq6xixQuLGPHjXcUul95pbGyKu4baI7Yo7kfzxPY1jNJSQOlUcMGepGR/0FZ1uV/tkBQOQrswqdZCVbYWADglE6eeM8Ss3rEyFEyffrMoKpYDHDq5HH597//rSwq58m81+c7ikk1alSXFs2bSe7cuYL6MAu8rOS3btnoaElqtoVF7lZlObtkyTJPQRrjbdWyhTRq1EDuVDHUI0kHDx2SWbPmCGLuesX0xTEefzy3tGzRXEqXLuUoDLsdF+7SJ0yY5BhXGLHfW7VsLhDPIYw/8aSzAGe/3l4xuj//7KwxlBUrVylL3ynylfICZU9wU96yRTN59ln3yfm0eM9OmTpNRo9Ofqcxz8u8Z81t+2c8fidwDDCC+3B8j0LFwa5UqaI0qF9PihQpbB9eYLthw8YqbuyhwLaZQdspk73DSn2v4s6uVt//hQsXOV5zs6+KFcpLfTWOwoULyfXXX28WB31G+931Erq3bN5g3Ov6wQ4dOiwdEjpZXIa3bt1Sunfr6ik4oQ+8F27btl0WLVri+RsBN/G1alZXf79ekptuukk/vGMeC8D37z8gM2fNNj4dK10tLFyokFStVkX1X8OIHe1V177PK1zGc0o4W75sib1Jqmwff/+EjJ80zXIsiKkQVcNNKRW6cZzff/+HbFUxuA+/fSSwuN/p+JgjQEi2smVKyv3KW0Kk6auvvzHcmL///klXwTuP8t6CGMxZ7w/d/3+UN7jxk6bKCRVP20xVXqygBPhq5qbrJ8K0bd6yTc1/vB1Y3G+vDBG/aJFCUr5saccFM/b6btvxFLrx/NBMGVHo6YGs98sQ5Rkv3PT5F1/KwMHDg6rflyWLDB8yIFC+as162bBpS2A7nAzumVvV3/WHHnpA4Ko8r7KKDzWXhd+FDond5Lff/hY4BNpgHgzzYUwkQAIkQAIkQAIpIzBixAj17L0/ZZ1E2bpo0aJqsXiPKFvHthlcjoda7InnwXBcl8ein9ie3bXrzS/zwulW6IY4DZEaqdwLpaVB3Zcsd8Mff/whEIrNmxYP2HC1BJdLZjr7yacyXMWXhvttJHwRpqmH8RtuuMGsEvjEBe/So3dgG3W7JnYw+jNXtl6+/L1A+P3iy68C9ZzGFtgZIhNK6IbQ33fA4KBe8j+TTzq0bRVUHiuhGy4z4B4ebtLB0ExY5VuvTi1zk58kQAIkQAI+IOCXB5q0hlIXu2vXrq3chTdJa0MMjAeTqpcuXZYLFy4YC/GwEhjWZ1go+OCDD0U1GYw+IBJeunRJ/qMmOP/85xtVXw+q0C9/CRzXz5lQQrd+brC8PH36Y/ldCXuYrH7ooYeCYvrq9WOdv3z5shLbL8jFixfln8rSCwIdLB3vV6JD5syZQwpi4YwHQvO5c+fk/PnzguNluuMO41zhZjpTJu/FEeH0j3v0CzVxD0EyS5Z71T15n7qnIl8kGs6x9Dq4jyH0/vzzL8YxsYgVHhoyYorH74TJ8bfffjPuUfwG4d916h69S92buD9z5Mge0o2y2U9KPyGyIfbzhQsXjXv5T+r9EIum77svi/GdgbVCWktghxAQ+IQbdaeF1qHGbP5GgD1+I8D/nnvuVt/hrMZ9H6q92370BZboF1z/+c9/SWb1d+VO9fuQLdtjkhZ5up1LJOUQGCE0mslrDsGsE89PeGeAJzvERv73H/82/ibjGiDUGrzVxSLhWn977rzg791P6vcS/d9/fxa5V8Vy91oU4nZsjBdzJw88cL/cEcXfkIvq2QPtf/jxJ/kfNXfzv/8L44e7jPONZjxu42R5eASOHD1muFTXa7do2kQteiyoFzFPAiRAAiRAAiQQJQE8zyckJKS6VTee5ydOnCh33313lCNnMz8Q8Mu8cLoUuj/7/AsZNGSE5T6ZNmmc3HyzdRX64bePyvRZcy31sEIVq43Pnf/OYgGNSq1bNJWCBZ6z1Nc3YNENy2494cX2wQeyqom5H4LcRz2X/xlp27p51BOMoYRujKOPErq/UYK3ntxcf0UidKM/sLKnf6mXXHNhgL4Pk1VwlZUpU8acHNRZME8CJEACfiLglweatMgUYjdSWha50yI3P4wpEqHbD+fDMZIACZAACcSGADy3vdZnoKWzqpUrSs3qVS1l3CCBjEAAi6S6dO9tsbJHLPiB/ZJD+WUEDjxHEiABEiABEog3AYjdc+bMkffffz/ugjcE7rx580qzZs0ocsf7wqaB/v0yL5wuhW4IuxB4zQRr7anKEtvJ8uP9E6dk3MQpZlXXz8SEdsot05Ou+80d+/YfFMT0dhJ7zTr4hAuvzh1Dx0HU29jz4Qjd+w8eVvGqrky0o/09aoXNqOFJ9q6M7UiFbsdOHAqffCK3YUHuxN+hOotIgARIgATSEAG/PNCkIWQcSgYgQKE7A1xkniIJkAAJRElgkYqRvWPXbkvrkcMGGRbOlkJukEA6J7B67XpZv9HqHh1u2OHhhokESIAESIAESCA+BOBtN57pxhtvjGf37DuNEfDLvLAvhe639u43xGRcc8SSGja4f9DlnzF7nhw6fMQor12zmlSuVCGojlnwyaefyZp1G+TM2U8DrsyxD9bYObI/JjWqVZFsjz1qVg/5+aNykTVbxbw8ffpMkOANK+gK5coY40mp26yExO4BK/Gxo4ZKZofYinYX7S83rCdlSpVwPIfR4ybKqQ8+MvZ16tBG8uV9ylJvz74DMm/+QkuZ0wa43Xbrrcol3aOGBfwz+fI6VWMZCZAACZCADwj45YHGByg5xHREgEJ3OrqYPBUSIAESiDEBxNmG2P3fqyHQ0P3zKnb4U3meiPGR2B0JpG0C8Hr4ufK4aKZHHnnYdT7KrMNPEiABEiABEiABEiCBtEPAL/PCvhS6w73MiBd43XV/kptusros92qPeE7ff/+jiuF0Z4pXXP9XxaS8qGJefvvtOcNtOmJdwqLajNntNQ7uIwESIAESIIG0QMAvDzRpgRXHkHEIUOjOONeaZ0oCJEACJEACJEACJEACJEACJEACJEACGZGAX+aF07XQnRFvPJ4zCZAACZAACcSSgF8eaGJ5zuyLBEIRoNAdihD3kwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJ+JmAX+aFKXT7+S7j2EmABEiABEggzgT88kATZwzsngQsBCh0W3BwgwRIgARIgARIgARIgARIgARIgARIgARIIJ0R8Mu8MIXudHbj8XRIgARIgARIIJYE/PJAE8tzZl8kEIrA2HHjZdKkKUHVbrnlFjl18nhQOQtIgARIgARIgARIgARIgARIgARIgARIgARIwE8E/DIvTKHbT3cVx0oCJEACJEACqUzALw80qYyFh8vgBH799Ve5cOFiEIXrrrtOHn30kaByFpAACZAACZAACZAACZAACZAACZAACZAACZCAnwj4ZV6YQref7iqOlQRIgARIgARSmYBfHmhSGQsPRwIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQALploBf5oUpdKfbW5AnRgIkQAIkQAIpJ+CXB5qUnyl7IAESIAESIAESIAESIAESIAESIAESIAESIAESIAESAAG/zAtT6Ob9SgIkQAIkQAIk4ErALw80rifAHSRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAhER8Mu8MIXuiC4rK5MACZAACZBAxiLglweajHVVeLYkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkED8CfpkXptAdv3uAPZMACZAACZCA7wn45YHG96B5AiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiSQRgj4ZV6YQncauWE4DBIgARIgARJIiwT88kCTFtlxTCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiTgRwJ+mRem0O3Hu4tjJgESIAESIIFUIuCXB5pUwsHDkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEC6J+CXeWEK3en+VuQJkgAJkAAJkED0BPzyQBP9GbIlCZAACZAACZAACZAACZAACZAACZAACZAACZAACZCATsAv88IUuvWrxjwJkAAJkAAJkICFgF8eaCyD5gYJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEDUBPwyL0yhO+pLzIYkQAIkQAIkkP4J+OWBJv1fCZ4hCZAACZAACZAACZAACZAACZAACZAACZAACZAACaQOAb/MC1PoTp37gUchARIgARIgAV8S8MsDjS/hctAkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkkAYJ+GVemEJ3Grx5OCQSIAESIAESSCsE/PJAk1Z4cRwkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIk4HcCfpkXptDt9zuN4ycBEiABEiCBOBLwywNNHBGwaxIgARIgARIgARIgARIgARIgARIgARIgARIgARLIUAT8Mi9MoTtD3ZY8WRIgARIgARKIjIBfHmgiOyvWJgESIAESIAESIAESIAESIAESIAESIAESIAESIAEScCPgl3lhCt1uV5DlJEACJEACJEAC4pcHGl4qEiABEiABEiABEiABEiABEiABEiABEiABEiABEiCB2BDwy7wwhe7YXG/2QgIkQAIkQALpkoBfHmjSJXyeFAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAlcAwJ+mRem0H0Nbg4ekgRIgARIgAT8QsAvDzR+4clxkgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEBaJ+CXeWEK3Wn9TuL4SIAESIAESOAaEvDLA801RMRDkwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEC6IuCXeWEK3enqtuPJkAAJkAAJkEBsCfjlgSa2Z83eSIAESIAESIAESIAESIAESIAESIAESIAESIAESCDjEvDLvDCF7ox7j/LMSYAESIAESCAkAb880IQ8EVYgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIIi4Bf5oUpdId1OVmJBEiABEiABDImAb880GTMq8OzJgESIAESIAESIAESIAESIAESIAESIAESIAESIIHYE/DLvDCF7thfe/ZIAiRAAiRAAumGgF8eaNINcJ4ICZAACZAACZAACZAACZAACZAACZAACZAACZAACVxjAn6ZF6bQfY1vFB6eBEiABEiABNIyAb880KRlhhwbCZAACZAACZAACZAACZAACZAACZAACZAACZAACfiJgF/mhSl0++mu4lhJgARIgARIIJUJ+OWBJpWx8HAkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkkG4J+GVemEJ3ur0FeWIkQAIkQAIkkHICfnmgSfmZsgcSIAESIAESIAESIAESIAESIAESIAESIAESIAESIAEQ8Mu8MIVu3q8kQAIkQAIkQAKuBPzyQON6AtxBAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQEQG/zAtT6I7osrIyCZAACZAACWQsAn55oMlYV4VnSwIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQALxI+CXeWEK3fG7B9gzCZAACZAACfiegF8eaHwPmidAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAmmEgF/mhSl0p5EbhsMgARIgARIggbRIwC8PNGmRHcdEAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAn4k4Jd5YQrdfry7OGYSIAESIAESSCUCfnmgSSUcPAwJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJpHsCfpkXptCd7m9FniAJkAAJkAAJRE/ALw800Z8hW5IACZAACZAACZAACZAACZAACZAACZAACZAACZAACegE/DIvTKFbv2rMkwAJkAAJkAAJWAj45YHGMmhukAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJRE3AL/PCFLqjvsRsSAIkQAIkQALpn4BfHmjS/5XgGZIACZAACZAACZAACZAACZAACZAACZAACZAACZBA6hDwy7wwhe7UuR94FBIgARIgARLwJQG/PND4Ei4HTQIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJpkIBf5oUpdKfBm4dDIgESIAESIIG0QsAvDzRphRfHQQIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJ+J+CXeeH/BwAA//+/OMUyAABAAElEQVTs3Qm8DtUbwPHHEm7IvoWiKBJZs4e070TqXylb2feltCMJUcqSQqJFRaS9SFFkl32LNvu+Zv+f51wzzbtd773uve5cv/P5dN95z2xnvjNvnzHPPOekOWWKUBBAAAEEEEAAgTACO3fusrW5cuUMM5cqBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgdQm4JfnwmkIdKe2S4/jQQABBBBAIPEE/HJDk3hHzJYQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHzW8Avz4UJdJ/f1ylHjwACCCCAQJwCfrmhifMgmIkAAggggAACCCCAAAIIIIAAAggggAACCCAQtYBfngsT6I76lLIgAggggAAC55+AX25ozr8zwxEjgAACCCCAAAIIIIAAAggggAACCCCAAAJJI+CX58IEupPm/LNVBBBAAAEEUoWAX25oUgU2B4EAAggggAACCCCAAAIIIIAAAggggAACCKQAAb88FybQnQIuFpqAAAIIIIBAShXwyw1NSvWjXQgggAACCCCAAAIIIIAAAggggAACCCCAgN8E/PJcmEC3364s2osAAggggEAyCvjlhiYZSdgVAggggAACCCCAAAIIIIAAAggggAACCCCQqgX88lyYQHeqvgw5OAQQQAABBM5OwC83NGd3lKyNAAIIIIAAAggggAACCCCAAAIIIIAAAggg4Aj45bkwgW7njPGJAAIIIIAAAiECfrmhCWk4FQgggAACCCCAAAIIIIAAAggggAACCCCAAAIJEvDLc2HfBLpPnDghJ0+eTNDJiO9KadOmlXTp0sV3NZZHAAEEEEAg1Qn45YYm1cFzQAgggAACCCCAAAIIIIAAAggggAACCCCAwDkS8MtzYV8EukeNHiODBg2WQ4cOJ8vpjImJkS5dOkmzpk2SZX/sBAEEEEAAgZQq4JcbmpTqR7sQQAABBBBAAAEEEEAAAQQQQAABBBBAAAG/CfjlubAvAt3lK1SS3bv3JOs1kCNHdlm4YF6y7pOdIYAAAgggkNIE/HJDk9LcaA8CCCCAAAIIIIAAAggggAACCCCAAAIIIOBXAb88F/ZFoLv4FSXl+PHjyXotpE+fXtauWZms+2RnCJwrgVNmxzP/3iMFs2SUy7PHnKtmuPtNae1xG8YEAuehgF9uaM7DU8MhI4AAAggggAACCCCAAAIIIIAAAggggAACSSLgl+fCvgp0BwefFyxcKA0aNLInsEaN6jLu3Xfckzl02HAZOHCQ/d61a2dp07qVO+/hxo/KrFk/2++ffDJBKpQv785zgurB+3IXYCLRBFbsPCitv1ttt/d01SJyw6U5E23b3g2dPHVKJq7ZLrNMIHf2pr2ydMcByZgurVQucJFUuTib3Fo0l/30rpOY0wPn/Smfr99h9plGvmlYLjE3nWjbunPSEttG3eCHd14tjUrkS7RtJ2RDKa09CTkG1kEgtQj45YYmtXhzHAgggAACCCCAAAIIIIAAAggggAACCCCAwLkW8MtzYQLdBLrP2W/l24275OaPF9n9v3r9FdKhQuFEb8uuf4/JHROX2AB3XBvvUflSeem6YpImroUSOO+6DxbYbGld/VS3ugncStKudsEr0+X4Sc2jFqlXPI9MuqdM0u7wDFtPae05Q3OZjUCqFvDLDU2qPgkcHAIIIIAAAggggAACCCCAAAIIIIAAAgggkIwCfnkuTKCbQHcy/iwCd5XUge6VJmO8pgky7zx8zN1xhXxZTdfcF8r+o8dlwdb9su3QUXdezULZZXqj8pI+beKGu/0Q6O78w1oZPP9Pe+xfNSibZNn1LvYZJlJae87QXGYjkKoF/HJDk6pPAgeHAAIIIIAAAggggAACCCCAAAIIIIAAAggko4BfngsT6CbQnYw/i8BdJXWgu8r4efLr5n12p1VNF+UfmC65L70oU0AjpqzbLg2mLHWzmYfUvULalU/czHI/BLoVZYd5ISAmfVrJfEG6AKNz9SWltedcObBfBM61gF9uaM61E/tHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRSi4BfngsT6CbQfc5+c0kZ6N568KjkHzbTHlveCzPIhseqyYURArhfbdgpt32y2C5bLm9WWfjItYlq4pdAd6IeNBtDAIFUI+CXG5pUA86BIIAAAggggAACCCCAAAIIIIAAAggggAAC51jAL8+FCXQnU6B76fYDooHdOZv3yrLtByXPhRdI8RwXStvyhUSDq3GV9XsOy4jFf8vKnYdEpzNfkNZ2v9346vxya9FckjZNaFfb3/+xS95ZttludsSNJWSf6ar7s3U75PP1O2TTgSNyU9GcctfleaRawWzuruea7OdvN+607Txw9ISUM918tyhzsVQx2dDB5e/9R+SJn9bZ6o5mbO2K+S+SSWu2yXdmv79u2mfaJFIiV2ZpcnUBqXtpzuDV7fdoA93a9uGL/pFZ/+yRDXsPy0UZ0kt507bbL89tjz/cxmf+vUc0wKylSekCMvqWq8ItZut0ZOqbPloke48cl6LZYmTCXVe7y3qPs1npi6XOJTnced6JD1ZulS9+32Gr3rn1qoDuz4MD3RqEH7diixm3e7esMuf0EpNlfm2Bi6S9ySTPlzmDd7Pu9OsL/7LZ6YWyZpR+ZixxvZ4+M+fyC/NfhnRp5U5jcY8ZW/vy7DF2naMnTsr0P3fbczn9z13WTLtt73btpXJxlozudp0J7/UyoHYxKZA5dJktpt1T12+XOeb8ztuyTzKYk1zMXMP3XZlX6l2RN87xzeN7/UfTHm27Xg9DF/0ti7cdkL/2/Wt/VyVyZpbHriloTZ3j837q+W793SrTff0JualITmlcqoAsNN3Yf2NeePjG/Eb3HDkmZc1v8i5jWt8c19mWWeZaHL1skz3X/x4/KVfnySzVC2a3166e+0jliDmHn67dbn5Pe+0Y84fMukWzZTLXYE5paY4vk8m+95ZuM9bKZnOO9Lc3ylzvF8TRBX/Lb1fJgWMn7HU6xlyvof8H8W6Z6fNdwC83NOf7eeL4EUAAAQQQQAABBBBAAAEEEEAAAQQQQACBxBLwy3NhAt3JEOh++dc/3KBwuAvsmapFpVeNy0JmHTKBqCZfrZSPVm8NmedUFMsRI4saV5YsGQK7m+5ixlweZMZc1rLIZChXfW++aJAtuDhddWvQWtsZrjxf/TJ5rlrRgFneIPW420vZAPqEVeHbWckEwb9vVM4GW70b8W7j1euvkA4mYB5cZvy1W240QejjJzU8GVpqFc4h7952lQ0We+cu3rZfyo2da6vUaG3zat7ZUU9H00bd2N2fLrEvEuj04U51AoKQ3kD3kkcr2wC8BtXDlRE3lZDHTRAzuFw9Zo4s33FQNNA98uaSbgZ68HLzHq4kZfJksftwum33LqPjj0+tf43cYl6Q8Bbv9bK8aRW5yryk4C0aeL5z0pKw15Aup13D//xgxbAB04Rc/2dqj+6z75yN8tTM9ToZttQzgf+J95QJadOJU6ck/cDpdh19CeKqXFlEg8Thir6k8bnxCg4qh1s2uE73oz0F6DUUrui5eO+OUuZFgXwhszfu/Vdu/HihrNt9OGSeVujvfX2LaqK9FTjl5o8XufuaXK+M3F0sjzMr4FNfaKlsuvXXUip3ZlnWpErAfL4gECzglxua4HbzHQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCBhAn55LkygO4kD3U+bQNyLJiDnFA1Q1SqUw4yHfNQdP1rn/fy/igHZ1Vr34OfL5f2VW3TSLZr9nSFdGllgMlCd4K9m6s5vHNjdtjdQmCvmAtlpxl/WwNo1Jgi6zmSFewOt95fIJx+eDlJrUFhLcIBt8SOV5Zq8Wdx2eAPAmv27+eARO0+PT7OKNSjrtE9n6HZXNq0akOns3Ua4QPfqXYdEA7ze7Wi27vFTJwPap8Hf3x+rHpDBeswExjO8EhvM1P2/XvdKmz2v0/EpZ2qjs61oA93OudD1nGzeP00msre8aYLdmpHsLU6gW+v0PKqJWl9pMqqXmOxux0jr9BrRjHZdTgOZmpWu598p2TKml02tagR05e69XoID3ZoZXnfCQmd1u13NSM5orkOd5+z7xZqXS88qRdzldCKh139c7dHtjjcZ8Q9/sVwnbdFrUK8xvQ69126bcoXkjRuudBazn95At3pp7wVa9DoqaLLdV5nrzvv76FH5UptFbxeKx59mX6+U0Us3uWtoUFp/G2t2Hwo4H4PqFJdOFS9xl9tjXoLI88ZPrqvOKG1+tzq+vJ5Xp201C2WXnx6o4K6nPSrca8ab13JXsdwypd417jzvRCuTzT5i8T+2Sm3UiIJAXAJ+uaGJ6xiYhwACCCCAAAIIIIAAAggggAACCCCAAAIIIBC9gF+eCxPoTuJAd24TsHKCzD80Ki81THDKKdqlcc3T3WtrAHRz65pusFazuBt9tsxZVMaarOV7TTfKmU+PM63BywrvzpVth47aZTY+Xt0GwpwVvIFCrWtRpqAMu/FKN0ja5vvVMnJJbLBL52vG6rT7yrvBdg2+NjWBumkmk1fLI6YLcu2S2yneALBTp5ndD12V337VrrO1G/N7Pv3NDdi9YDLDn/Vkhnu3ERzoVrMrR812A4K6XtdKl0pWE5jU8odp3+0TF9uAun5/0gRY+5pAq7cEZ6nrCwHdTdBSu2yPNkM3rjZ69xVtoFvX0YDszAcqSv7T3ZT/Y7qS7/3LBnnz9PnQAPWfLasHdB/uDXTrNsYb6wdPW6tV3Y8WyhLTfbdTrjAB8On3l7eBW62bb7oav33iEvd6ef+Oq+WBkv9lEnuvl+BA9/1Tl4mTra+BUc041zZq0evvqtFz3PO0wmSDl/Rkgyf0+o+rPT+Y4Pr1nsB7cPayZvPX/nChGxB+xQSSO3sCyd5Atx6DBrsnm8xvp4v9kyYTu/fsjfL8z7/rbFv2d6gd0muCMy/cpzeLXV8s+P6+crZ7f11W+yZ4bcFf0mn6GruqXou6fcd0zNLN5re3ws7Tl1A0g9+57vV3dZs5j87v0vtShL5wkPW1GTbrXre1t32tgJcZdIO674vMMk5wf2+HWiE9Ldgd8wcBj4Bfbmg8TWYSAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA4CwE/PJcmEB3Ega6V+48aIOAeh1pwOqDO/8b+9m5trR7ce1KWMdX1rGOnfG2/9r/rwlEbzLjbG+SXtUvt+NMO+s4n5rtrVnfWnTbug+neAOF1c043LNMxri3aLAv+5Af3YDXUBPAbB2U2amZpTnMMloqmzGk5zxUyd2ENwCslW+ZYFxzM553cPlk9TZp+FlslqkG9A6Zbr1jQ6Riu1nW7pa1BAe6209bIzoutZZwQWyt17G78w2d6XanfaRzHTtetc7TosfY0GS46jjHwUWPR83vL5FfiphxjyMV73EGt9G7TrSBbg2qbjEvNDgvLDjb0ABkJfPigmbqa3nadGff29OdvTfQrd3ca3f33uK91rR+VbOqcmXOC72LyGTjUG/yb7ZOXxzQFw+c4r1eggPdMYN/sMb6MsaOttc5q7ifP5kXNl6cvcGME57HBt9zZEpv53nbFN/rP672FH/7FzdrW19u0OsjuARnoR/oWNs1Dw50f3FvWbntslzBm5Bqprv/2WZ8bC3aJbyOQx9N2f3vccn5euzvRpf/22TPa6Z4cPF2Ne79/Wp351+Z8cK1bGpdI+CFB63bYV5suM/8prTb/gdL5rcvTmi9lse+WSVv/Rb7Aot2i/4/M99bdCiAOuYlAC23mu7rv2xQ1jubaQTCCvjlhiZs46lEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiLeAX54LE+hOwkC3Zh0XefNne/FogHOrCXBeeDojO95XVJgVNMBY63RGuHZ9rF0gO8UbKAwXxNblvIG8P0xGuNOVtrMN/XQycrXb5a1tarqzvAFgzVjVAKiTkeoudHrCG9DTrpa1y2Ut3m0EB5Gd/Wpw/KAJUjovAJzepPvRy2RCP3c68/Y3M/61dvEcXDTY/uTMdW5wNHi+ZnprpnIVM850cImrjd5low10B2e1e7ehWfqFR8yyVZr17R1X3Bvo1nqdH1zSDJhmqyIFpDVL/9LT16Nmg2tWuFO810twoFtfiHC6y15oxnvXrtGjKWdz/Udqz3rT7X6xt36xu9es9VXNq7ovTgS36dGvVsjYZZtttTcD3hvojmSlK2nWdcfTWdfhgsbB+3O+a6BZA85a2pUvLEPqXuHMCvjUgPVPJvCspbK59pxguL4YoteslpdrFZPu115qp6P5o5n7lcbFjr+tGeqaSe4tjb9cLuOWxw6HoGO133F5bu9sphEIK+CXG5qwjacSAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIN4CfnkuTKA7CQPdetU4AVud1qBtS9Ptc5PSF9uArJPZrPOiKRoYW73roGzY+6/ppnq/vLdiqzs2to7prN0YO8UbKFxkgpNlwwQntftn7QZaS3A2tLMdDYxqgDQ4IOgNAAcHTZ11nc+X5myUnmasci3esbK92/AGunf9e0xyvf6TXV4D16NvKWmnw/354vedbhfT3qzYcMtq197jV2yWyeu2hw16h8uUjtTG4O1HG+gODiIHb8cbVD7Vra472wl068sEx7pc79Z7Jy4wY5Jr99U6jvnKZlW8s+y0N9M4OMPae70Et1G70Neu9J1Sz2TCawBXewrIkC6tUx32M6HXf6T2aBfq2pW6lo4VCsvg68MHkXW+t/t/b8DZG+iuagLMvzwY2NuBrqtl6vodctekJXZag9W6jWiKd2zu+GSCO9seZcb1bm6GDXCKvojR1QS7bzEZ2NnNSyVnKvqyhL40ocXbNbm3a3P9f5FmuadLE9//C51p78xPjQJ+uaFJjfYcEwIIIIAAAggggAACCCCAAAIIIIAAAgggcC4E/PJcmEB3Ege6vzfjVN/40aKQa1ADTXVM18M69nUD02V5uICTBuQ+N8G2Nxf/Iz+YzM9/j58M2Y5TEVegOzhw6ayTWIHu4G6wne07n1+aYLSOp63F2w15pCDysh0HpPSYX53Vo/58ynRh3SdonO5IK28+eMRmtmr36E5QUJcdZYLqTc2LCE6J1EZnvvMZbaDb24W2s673s8b78+Xnf2K7y/YGKZ1At143h0337+FKfALdjUw39x96utKPFFjW/Ww5eFRKmPHSnaxu7741CPtwqQL2Og4XhE3o9R+pPdrVv87TomPG6+8nUlm965Btt87X8e0/ubu0XdQb6I6r+27tPly7EdcSn0C3twcD7zm0G4rij3ZjX9NzHXhXKZotRu4rkVdalCkol2cPzerXZfuYbuSfmRU7vrh3SIEvft8hd5jxvbXoMAXa0wMFgWgE/HJDE82xsAwCCCCAAAIIIIAAAggggAACCCCAAAIIIIDAmQX88lyYQHcSB7r1UlllsrC7/rBONNAUrlQyY/9+3bCs5Mx0gTv7yImTJji8RKaZQHm4ot2Fa1fjS7cfsLMfNxndIyJkdCd1oFvHktYxpSOVX0zgtroJ3Glpb7JiXzvdlXOkILJ3+UjbDFfftnwhmzEebl6kOs1y7fHjOtEAqhbtDnu16Q7bKZHa6Mx3PqMNdJ/oen3Ebth1WxqIdK4T7/jM5zLQre3S8dqf/GmdGTN+c9gXLjTjf9p95eWavKFdxyfk+o8U6H7BdFX//Omu6iONra3t1eLtGeCmIjnlm4ax3XgndaC7yvh58uvmfbYNmn0fqUt/u0CEPyfNSy6D5v8lg+b96fbaELyotzt277xNB45IweGxXeDrWPRzHqpkZ9c347M749UvMd38lwnTzb93O0wj4Aj45YbGaS+fCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAmcn4JfnwgS6kyHQ7VxKB4+dkB//2mO6C98V0n22Bq11nGyndDZZq4NPB1+1TjNwb7ssl1Q0QfHLTFanZvZ6x1w+l4HuNiY7VMe4jlRGLvlHHv82dszivibjWrO6tUQKInvHYdaM24GescftihH+5DbBVh1LPCEl39CZsu3QUbuqN+s6UhuD9+Ed71wzrvX8OOU6M476TDOeupY/W1aXwlkzObNCPp2u4nWGN0h6rgPdTkP1xYB5Zhxo7fL+a5Px7ByXM//vVjXcsaadOuczPtd/pEC391oaULu4dK10ibP5kM9Zxrzm6THsNfNbM8C1JHWg2xtQXteiWsTM65AGR6hYufOg9dbseO1OXc+BUz6+q7TtEcL57nx6r8dtbWpK1gzpJetrM+y6wf+vcdbhE4FIAn65oYnUfuoRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE4ifgl+fCBLqTMdAdfAnNMN2R32q6Rna6JNdMYs0o1lJy1BybCa7TP/+volQz4yEHF2/m87kMdGtG+tyHY7NGg9uo39t8v1qGLfrbzvJmoUYKIqtHzOAf7PKaIbz4kcp2Oj5/+v26UdbsPmRXedEE1wtkzhjn6t5gtDcDXgO62sW7lh6VL5V+1xUL2Y6GHS807XXOY1yB7sn1ysjdxfKEbEMrNIs33cDpdl5wF+UpJdAd3HB9KaGeyRR2ehbQFx70xYdoSlzXf6RAt/ea+V/J/PLeHaUi7uqNhX9Lu2mr7Xxv9/pJHej2tl27h9eXVBKraGZ9h2lr5N3lm+0m77g8t0ytf03I5nX+I1+usPWvmnHMC2TJIDrWuha9hvVapiAQrYBfbmiiPR6WQwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhbwC/PhQl0J2Gg+8DRE6LBvF827ZVnTdfe3ixf5/Jpb4JWOk60lpE3l7Bj7+q0M95yoawZ5a+WNbQqpDw9c728OGejrT+XY3RrA7zBYdug03+2myzpS9782Q0CbzXZpU7WtTdoqcG4DhUKu6uWHztXFm3bb79roDtcl9g6c7oJRP9ugq0a8Muf+b9s7oafLZVPVm+z63vHBbcVQX/0PGm2q1O8Gd1/7PtXipj2a/F2A+0sq59zTTfVlU131U6JK9AdaRu6rnf86buK5ZYp9f4LYJ6rQLdmD/+6ea/pQn+3NDRjyZfMldk5TPfTO/azdyzss7n+vcFi77WlLxM4mcnaJbhmTF9qekMILtr1f7G3fnHHX1/0yLVSNm9Wu1hSB7o1y11fnNBSLEeMrG1ezU4H/5mwaqs8e3osbX0Zo4Hx1bLCZHB/t3GXFMmWKexLEfvN9XrR6etVhzDY075W8KblkOk9ItuQH20Gd2nTRXkB89vQ35sW728wZEUqEAgj4JcbmjBNpwoBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgAQJ+eS5MoDsJA90PTF0mH5pglpZw40drJnCdDxea7sx322WWNqksV+eOHeO47NhfZcm2A3Z83+1tr5PsJqDlLT//s0dqm3WdbozPdaC7nAki/vRABcmSIZ23mbaNzvEFZ37HFeges3SzNP06NiNVg4WzH6wk2jW5t2hA8Jp3fnUN5pmscu3aXYs3o1VfMPjy3rJS55Ic3tXt9NaDR0XH13bGVNag4G9m/GKneLOstW7c7aXkoavyO7NtJnM1M/64BnWdElegW5cZdUtJaVr6Ymdx+7l8x0HRYLZTgjO/z1Wge6wZk/vRr2LPQ4mcmc0LDZVDxhjXly30pQstg0w3850qxnYnfjbXf6RAt+7joS+Wy3srtuik6PlabILYadOksd+dP82/Ximjlm6yX4ODzYkR6NZxsJ//eYNsP3xUNFtcr3+n6O+64LBZ7tjaz1e/TJ4zy3iLXndFRv73AojTm8Nf+/+VS0bEvlihgXwdzuDiLIG9EXhfrPCOPe7dvk57/Z15NQtlt79T5zufCEQj4JcbmmiOhWUQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEzizgl+fCvgp0n5k98ZZInz69rF2z8qw26B0jWDfUpHQBua1obtEs7e/MeLsfm4xjp8tnzXLWTEunPGMyPfvM3mC/6vLaNfn1l+SUnf8ek+lm3TdMV+BOkFsXOteBbm2Djv37ZOUiUip3ZpvJPn75Frf7cJ0fnJkdV6Bbl3/sm1Xy1m//6KTNhtdgYZWLs8lhk9WrGa+jTSBzr+nKWYvuc1mTKnZa/2hGb9l35rrdv2udZn3fcGlOuSLnhfKXydSev2W/jFux2c0212Vm3F9eahUODIhrl88frY59YUGX0eBicdPFvHbbreMm63nQoKRzPs4U6NZtaKBbs7Y1QKuZvRPXbHPbEfxCgC5/rgLd3sxgbYfa/K9kPimfL6vNZNcxo3Wsbqd4x+g+m+s/rkC3ZnVXHDdX9OUALRrsfsb0mKCZ3bvM72Oo+W18btqlRV+8WNG0SsC46IkR6Pb2GKDd4m9qHdjrgl4bes6c7uz1mnn06ottF+LatnHmt+GMCV/BWM5vfK1tr/65dtw8Ow66TutvqqX57dc11+2GvYftGN16vTjX2tjbrpLGpQrooiFFXzDRl2G85QPTlfr9idiVunfbTKdeAb/c0KTeM8CRIYAAAggggAACCCCAAAIIIIAAAggggAACySvgl+fCvgh0l69QSXbv3pOsZzBHjuyycMF/3VEndOePf7tKRi6JDdZG2oZmHH/bsJxotqVTNGNUu8P+e/8RpyrkU8fzdsahPpdjdGvQVjOoneBbSENNxYibSthgvXfemQLdmhl7uxnD/CtPINW7vjOtLwn8+lAl29WzU6efmjVbygQbdx4+5q0OO60B0R8alXczwr0LbTHbKerJvvXO02kdg/nw8RPy2brY4GqkQLfuo4kJdjpd1QdvR79r0HRe40pSMCiL91wFurVN2iuBZgefqQype4W0K/9f9/O6fEKv/7gC3bpdPadXjZ7jBou1Lrjoywea5e90We7MT4xAdxnTk4Dzkoru52DH2pIhXVpnF/bT24V5wAzPl6LZYmyGtb7M4pSVpqeC8u/OdYPkTn3wpwasNdAdvF/vcrnf+Mm9/iO107s80wiEE/DLDU24tlOHAAIIIIAAAggggAACCCCAAAIIIIAAAgggEH8BvzwX9kWge9ToMTJo0GA5dOhw/M9EAtaIiYmRLl06SbOmTRKwdugqbyz8W3qb7Gwng9NZQgNPmiE73nSH7R1f2pmvY/HWn/ybzRp26pzPzqZ76PZmTGtn/OiEBLpv8wSRj3SuEzZgdqkZn/pPk/0cnLUaHKTWTOnbJi62yzpt1E8Nxr9+w5U2C9pbr9PB2/CO0e0se9RkZvc03WJrlq6THevMUz/NZH/eZHrnMcHucEUzu19b8Jc89/PvIevr8npc1Qpmk9dMkDY4uOzdnr540PjLFWas6thxjp15LcsWlCF1r5QGU36LKtC9v0Ntm6n/wi8bQl4MeOTqArbr75yZArto131FE+iOGfyDPUbtYnxls/+y25227v73uOR8/Uf7Vff1zq1XObPkTIFlzVzvPmOdO266u6KZ0P2NvrWkVDXZ9uFKQq7/M7VH96Pjp3ecvkYmr90eslvNitdzGq5NiRHonmQy8BuZ4L++3PG0ySbvXeOykDZohV4vnX5Y6wbFnYX0pYe7Ls8jI28uIZkvCOzuX5dZt/uwdPtxbdhj02v2uepFQ14ccbbt/ezx4zrpP/cPW/Vwqfzy7m2lvLOZRiAqAb/c0ER1MCyEAAIIIIAAAggggAACCCCAAAIIIIAAAgggcEYBvzwX9kWgW7VPnDghJ0+ePCN8YiyQNm1aSZcuNPh0NtvW7OSNpuvhjXv/lQPHTsg1prtl7ZY4mqLB3sVmvG4NOBfJlsl21ZwxKHs0mu0k5jKRgtQ6xvC8zftMl9Hp5UrTRbh2J50Y5ZgJKK7bfUj+Op3hrhmwl2ePkfg4bD54xAYQ1VHbVc50GR0uyBhXezWwqVn0ul/dhgbbE1J0OzrW8hbTpkJZM0nJXJklqwl+pvSiWfLaLbe2+0oT4C6R60JJFzQ+drhjOJvrP9z2vHX7jh6XNbsOyT7zYkgGcz60W/l8mcO/+OBd72yntVt3cxpDxqUPt119UWKdcdMx34tlv9AOXxBuueA6fdll3Z5D9revPRfoWODaAwQFgeQU8MsNTXKasC8EEEAAAQQQQAABBBBAAAEEEEAAAQQQQCA1C/jlubBvAt2p+WLx47FFCnT78VhoMwIIIIBAZAG/3NBEPgLmIIAAAggggAACCCCAAAIIIIAAAggggAACCMRHwC/PhQl0x+essqwrQKDbpWACAQQQSNUCfrmhSdUngYNDAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSSUcAvz4UJdCfjRZGadkWgOzWdTY4FAQQQiCzglxuayEfAHAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH4CPjluTCB7vicVZZ1BQh0uxRMIIAAAqlawC83NKn6JHBwCCCAAAIIIIAAAggggAACCCCAAAIIIIBAMgr45bkwge5kvChS067mb9knrb9bbQ/pqapF5O5ieVLT4XEsCCCAAAKnBfxyQ8MJQwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEgcAb88FybQnTjnm60ggAACCCCQKgX8ckOTKvE5KAQQQAABBBBAAAEEEEAAAQQQQAABBBBA4BwI+OW5MIHuc3BxsEsEEEAAAQT8IuCXGxq/eNJOBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgpQv45bkwge6UfiXRPgQQQAABBM6hgF9uaM4hEbtGAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRSlYBfngsT6E5Vlx0HgwACCCCAQOIK+OWGJnGPmq0hgAACCCCAAAIIIIAAAggggAACCCCAAALnr4BfngsT6D5/r1GOHAEEEEAAgTMK+OWG5owHwgIIIIAAAggggAACCCCAAAIIIIAAAggggAACUQn45bkwge6oTicLIYAAAgggcH4K+OWG5vw8Oxw1AggggAACCCCAAAIIIIAAAggggAACCCCQ+AJ+eS5MoDvxzz1bRAABBBBAINUI+OWGJtWAcyAIIIAAAggggAACCCCAAAIIIIAAAggggMA5FvDLc2EC3ef4QmH3CCCAAAIIpGQBv9zQpGRD2oYAAggggAACCCCAAAIIIIAAAggggAACCPhJwC/PhQl0++mqoq0IIIAAAggks4BfbmiSmYXdIYAAAggggAACCCCAAAIIIIAAAggggAACqVbAL8+FCXSn2kuQA0MAAQQQQODsBfxyQ3P2R8oWEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBFTAL8+FCXRzvSKAAAIIIIBARAG/3NBEPABmIIAAAggggAACCCCAAAIIIIAAAggggAACCMRLwC/PhQl0x+u0sjACCCCAAALnl4BfbmjOr7PC0SKAAAIIIIAAAggggAACCCCAAAIIIIAAAkkn4JfnwgS6k+4aYMsIIIAAAgj4XsAvNzS+h+YAEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBFCLgl+fCBLpTyAVDMxBAAAEEEEiJAn65oUmJdrQJAQQQQAABBBBAAAEEEEAAAQQQQAABBBDwo4BfngsT6Pbj1UWbEUAAAQQQSCYBv9zQJBMHu0EAAQQQQAABBBBAAAEEEEAAAQQQQAABBFK9gF+eCxPoTvWXIgeIAAIIIIBAwgX8ckOT8CNkTQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAGvgF+eCxPo9p41phFAAAEEEEAgQMAvNzQBjeYLAggggAACCCCAAAIIIIAAAggggAACCCCAQIIF/PJc2DeB7hMnTsjJkycTfELis2LatGklXbp08VmFZRFAAAEEEEiVAn65oUmV+BwUAggggAACCCCAAAIIIIAAAggggAACCCBwDgT88lzYF4HuUaPHyKBBg+XQocPJcipjYmKkS5dO0qxpk2TZHztBAAEEEEAgpQr45YYmpfrRLgQQQAABBBBAAAEEEEAAAQQQQAABBBBAwG8Cfnku7ItAd/kKlWT37j3Jeg3kyJFdFi6Yl6z7ZGcIIIAAAgikNAG/3NCkNDfagwACCCCAAAIIIIAAAggggAACCCCAAAII+FXAL8+FfRHoLn5FSTl+/HiyXgvp06eXtWtWJus+2RkCCCCAAAIpTcAvNzQpzY32IIAAAggggAACCCCAAAIIIIAAAggggAACfhXwy3NhXwW6g4PPCxYulAYNGtlrpEaN6jLu3Xfc62XosOEycOAg+71r187SpnUrd97DjR+VWbN+tt8/+WSCVChf3p3nBNWD9+UuwAQCCCCAAALnkYBfbmjOo1PCoSKAAAIIIIAAAggggAACCCCAAAIIIIAAAkkq4JfnwgS6kyHQvWLlKjl1SuSirFmkcOFCSXrhJXTjfmhjQo+N9RBAAAEEEi7glxuahB8hayKAAAIIIIAAAggggAACCCCAAAIIIIAAAgh4BfzyXJhAdzIEuh9tHptNXuLK4vJEt87e6yTFTPuhjSkGi4YggAAC55GAX25ozqNTwqEigAACCCCAAAIIIIAAAggggAACCCCAAAJJKuCX58IEugl02x8Cge4k/f8BG0cAAQR8K+CXGxrfAtNwBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAghQn45bkwgW4C3fanQ6A7hf0fhOYggAACKUTALzc0KYSLZiCAAAIIIIAAAggggAACCCCAAAIIIIAAAr4X8MtzYQLdSRTo3rJlq0z5/Et7Ic+eM9d+XnDBBVKxQjk7nT59emn26MNhL/Q1a9fJzFm/yGazjYMHD0nBggXk0ksukTq1akqWLJnDruNU7t27TxYv+U3W/b5BNmz8Q9KnSyf58uaVayuVl/LlykqaNGmcReVs2uhuJMzEyZMnZe68BbJw8RLZtm27pDNtKFq0iBS7vKiULVNaMmXKFGat2Krjx4/LgoWLZf2GDbJ+/QY5cvSo5MmdS0peeaXUqV1T1NBbJnw8Sfbs3WuPSz11X5HK2HHvy79Hjki6tGmledNHIi1GPQIIIICAR8AvNzSeJjOJAAIIIIAAAggggAACCCCAAAIIIIAAAgggcBYCfnkuTKA7iQLdGmx+9fXhES8hDbaOGjk0YP6pU6fklVdfl2XLVwbUO180yPu/Rg1MwPc6pyrgc/mKVWafw+TYsWMB9c6XYpdfJk890dUNdiekjc62In3u27dPnn6uj+zbvz/sIhdlzSrdu3aUQgUvDpm/Y8dOGTDoNdlqguPhSqaMGaV/v96i23DKwMFDXK/2bVtK+bLXOLMCPn/fsFF6vfiyrSt4cQF5sdezAfP5ggACCCAQXsAvNzThW08tAggggAACCCCAAAIIIIAAAggggAACCCCAQHwF/PJcmEB3EgW6NbD63gcf2etmvcmu1qKB6ksKF7LT2bNnk3atH7fTzp9R74yzmdzOdw3s5s+fz2ZeayayU1qYbOTq1ao4X+3nylWr5eWBr7p1GkgvXvxy0czxlStXywmTZa3l3np3y52332KnE9JGu2KEP0dN9vUzz/cJCFTnyZ3bZqH//c8mNwCvbXuyRxeT4X2Zu6VDhw5Lu45d3XbqjEKFCkrunDlltclwP3z4sF32iuLFpKdZ1ykLFi6S14eNtF/LlS0jHdq2cmYFfI4d/4H8MOMnW/fwg/dL3Tq1AubzBQEEEEAgvIBfbmjCt55aBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgfgK+OW5MIHuJAp0ey+YaMa//uqb70S74daiGcvdOreXwqeD4lr3zXfT5IMJn+ikDZgPGtBXsmbJYr/rn+FvjpJf58233zWQW9t0c64BZS2aXd3zmRfkwIGD9nvf3s/JxQXy22nnTzRtdJYN96nZ6AMGDZEVK1fZ2ZdeUli6dmovWbPGtvGICdSPHPWO7ZZcF7i6VEk739nWzJ9ny6gx79qvla+tKE0aP+h2ca7dmQ96bai77UfNvNrX1bDLagC/ZZuONoiuxzv8jcGSIUMGZ7P2U9vWqm0n2225Vgx/fbDExETuPj1gZb4ggAAC57mAX25ozvPTxOEjgAACCCCAAAIIBAkcPHhQ3hn7rowf/74UMM9AHnrwf1K/fr2gpeL/9ZNPJsm48e/Jnj27pVGj++SRxg9L5sxxDzMX/72wBgIIIIAAAggggAAC51bAL8+FCXSngEC3ZjO3bt/ZXrExMTHy4gvPSM6cOUKu4ClTv5BPp3xu62+75Sa5r8F//0Br0aq9DfbqGN5vvDowZN3Va9bK1C++krLXlJGqVa6VzBdeGLDM2Qa6vd2g58ubR/r2ed4NtDs70qz0Tl2fdLOzX32ln2TPls3OHvTaG/Lb0uV22lvvrLv/wAEZNuItufKKK2z7dR9OGfPue/LjT7Ps15YtmkqVypWcWfZz1eo10m/AYDtdpnQp6dyhbcB8viCAAAIIRBbwyw1N5CNgDgIIIIAAAgggEFlAX4yeP3+BfP7Fl7Jx40b7n97/5MqVU/LlyyclS5aUm266QSpfe63tMS3ylpiT0gRGjBgpL/cfENCsTyd9ImUjDHkWsGCEL7Nnz5H/PfhwwNyeT/aQFi2aB9T59cuECR/JokWLz9j83Kb3viJFikiRopdK8WLFJNvpZztnXDERFnjiiZ4hW2nfvq1cfHHoEHkhC1KBAAIIIIAAAgggELWAX54LE+hOAYHun2b+LKPHjrcXV6OG9eXWm28Me6FpZvPjrTvY7r2DM6JbtevsBpBfeLanaEZ1fMrZBrrfGD5S5i9YZHfZqX0buabM1WF3v3nLFtm0abMZJzytaNBZu1bXMnT4WzJvwUI7rQF8DeRHWzZs/ENe6NPPLn5VyRLSvUuHgFU1k/yX2b/auo7tW0vZMqUD5vMFAQQQQCCygF9uaCIfAXMQQAABBBBAAIHwAl9/86307z9QNmyIHW4s/FKxtbly5ZIX+/SSm2+O/t+qcW2PeQkT0BcTgkuaNGmCq+z3WrWvlz///CtgnmZ19+79QkBdfLbZrXsP0Yxub7nEPH/5ccZ0b5Vvp7t26yETJwYeXzQH07PnE/LoI41tD4TRLH82yxS9rHjI6l9+MdW8lFIipJ4KBBBAAAEEEEAAgYQL+OW5MIHuFBDo9o7N3fKxppIvb96IV17/V16zAe2cOXKIdl/ulGFvvi1z5y1wvkqF8mXlhuvrmDdrL3ODye7MMBNnG+ju3K2n7Nq922Zxjxo5NMwe4q7yBvt1ySKXXmID/qWvLiUXXhgT98pmrrN/XdDbNbm3a3MdI/3Noa9K2tNdup9xoyyAAAIIICB+uaHhVCGAAAIIIIAAAtEK6EvkGuB+6+1R0a7iLlev3j0yoH8/SZcunVvHRPIJ3HTzrbJ27bqAHb47dozUrBk7vJl3hmb+TvjoY2+VOe/9pGGDewPq4rPNyZOnSKfOXQPW12ti0CuBmeMBC/joS0ID3XqIRYsWlXHvjpGCBQsm6RET6E5SXjaOAAIIIIAAAgi4An55LkygOwUEugcOHiLLlq90L55oJ8a8NcxkRse+ubx37z554unn3axu7zY0aFytamWpUa1qxKDx2Qa6na7Tc+XMKa/0f9G7+6im9Q3qvi+/ImvXrQ9ZPo/pEuvaSuWllhmXO2+e/7os9y742edfyaTJn9mqJo88JLVqVrfTS35bJoOHxAbe69apJTp+OQUBBBBAIHoBv9zQRH9ELIkAAggggAAC57tA+w6dZOrU2GHBEmJx//2NpO+Lvd1/jydkG6yTMIFwQemhbwyR2267NWSDv/++Qdq0bS+rVq2y82688QZ54/XXJEOGDAHLxmebR48eldZt2sm0abEZ3Febl/Nfe3WwXHZZ0YBt+vXL2QS69ZirVa0q48a9k6QJBgS6/Xp10W4EEEAAAQQQ8JuAX54LE+hOAYHu3n37y3rzD7D4lpHDAv+BpmN9fzxpssz6ebYdrzt4ezp+d48uHaVw4ULBs+RsA93O+oUKFZQ+zz8dsv1oKjTY/fW338s3306TPXv3hl3l8eZN7BjdwTP37NkrHbs+YasvN//AfKZndzv9+rA3ZcHC2PGlept2FTbtoyCAAAIIRC/glxua6I+IJRFAAAEEEEDgfBbQbqe1++m4SvHixeyQWwcPHoy42PDhQ+UWujGP6JNUM+ITlHbaoAFv7Skuf/78TlXAZ0K2uXnzZpNo8G+qCXA7IOEC3ZkzZ5YKFcrbRU6ePCn//LMpzu7+tWt47SI+qQqB7qSSZbsIIIAAAggggECggF+eCxPoTgGBbm8wtmePLpLF/CMimlKgQP6wb5Brd90bNmyUlavWyNJly2VNULdegwe8JDlyZA/YhROoLnFlcXmiW+eAedF8ccYIj4mJMV2HD4pmlTiX2bR5i2n/almxcpUsXvybHZfcWaFNqxZS6fQ/spw6/ezz0gBZt/53W/X64AGSKVNGadmmo103oZnm3u0zjQACCJyPAn65oTkfzw3HjAACCCCAAALxE9iyZYtUrVYz7Eoa3H6530tSqtRVbsbvmrVrbRfnTvaud0Xtpvm7b7+KugtzzQTW7s4Ts8vzEydOiL4wnj59em/TQqZ1mWPHjrnHFbJAPCv0WIKzouO5Cbv4kSNH7Hacnuqi2UZCgtJn2m5SbDPSPvVcqF/GjBkjLZKger0WtJzt9RUu0P3ggw9In969Atq1b98+GTnybRk6bHhAvX4pV66sTJoY2GV8yEJnUXG2ge6EXHfhmhvt7y/cutQhgAACCCCAAAJ+EPDLc2EC3Skg0P3+hI/l2+9iu73q0bWjlCxxZaJe49u2b5chQ9+Uv//+x25Xu+/Wbry95WwD3U8920v+2bTZbnLI4P5yUdas3s2f1bRmqr/34Ufy8y9z7HbKliktHdu3Dtmmzn9r9Fhb/7/7G0r2bNlExy7X0vDeenL7rTfZaf4ggAACCEQv4JcbmuiPiCURQAABBBBA4HwVePPNt6Tfy/1DDl+7vR7Q/+WwQ31pBmu79h3lyy+/Clnvww/ek8qVrw2p14pt27bLpEmfmn/rfydr1qwVJzv8kksKS/HixeXee+vLDXWvlwsuuCDs+s8885wsXrLEnZc2bToZMfwNyZUrl3z44QQZ/9777ljVmql8zz13SfNmTe18XenQoUPy8cefyIfmeYPTdbfuu1zZstKpUwe59NJL3W07Ex9/MlHefXec89V+3njDDdK+fVu7jVGjx8iCBYvcbN4yZcrIA/ffJ3fffZfoS+9nKlu3brNt+u77abJ+/foAkyqVK4uOdX3ttZVCur3WFw66dOlmN7/MvMwfXDTjuGjRIrZaz2Wrlo/b6WBDrXysRXO58847TEJAwrYZzqhqlSrSs2dsD3N2x54/Ggz95ZfZdqzwFStWunZOm2tdd500bHhv2POhm1lpXv7v3iNw2zfUrSsdOrSzz3jGvPOOuTa/Fn2JQ4t2pV6hQgVp17a1ey3YGVH+iTbQ7WxOxyvXccuDy/p1q93zGNd5CF5Prz819hbneJ26+Aa6E3rdOftzPrebZ2vvv/+hzPn1V9m48Q/XXH+T2nV91apVbCZ7nghD7jnb4RMBBBBAAAEEEPCLgF+eCxPoTsZA96XmH5UvPNsz5BpetXqN9Bsw2NaXKV1KOndoG7KMVhw/ftx27X2FedO82OWXuf9o0Axu7YpLs58rVawgFxcI7Y7LO1Z1xQrlpG2rxwL24QS6I7UxYOEwX3R8bB0nW8sN19eWh/7XyE4H//lgwieibdHSrXN78w+vnHZ6kwmSLzP/6MudO5eUL3uNrfP++ffff6Vl2062KlLWuL4V3cosox7ahXr2bBe5Y58ndvDd2zamEUAAgdQs4JcbmtR8Djg2BBBAAAEEEEgcgevr3uQGGp0tasDx51k/SjbzonSkstcMrVW2XMWQ2d27d3WDqs5MzdgdPeYd6dOnr1MV8VMD1G+/9abNIg9e6JFHm8pPP80MqJ4yeaJo1+vjxr8XUO980e1NmviRyepNL7q+E+B25ns/Xx/yqtxxx+3eKhkxYqS83H9AQJ2OR16zRnU71nXADM8XzW5/d+wY++9wT7U7qSZvjxotffv2c+siTVxfp7YMGPCy5MwZ+6xAl/vtt9/k7nvujbRKQP19DRvIyy+/ZOvCGb7wwnPS+OGHErzNcEY3my7sR5iu7IOLJhu0at1GwgXng5dt0byZ9OjRLSQje8HChdKgQeDzFTV66aW+ctvtd8rOnTuDN2W/63U9fNgbUrNmjbDzI1XGN9D9zTffSstWbUI29+OM6aIvVmiJ6zwErzhgwCsybPiIgGrvOdUZ0Qa6z/a6cxqhL7sMHDhIho9406mK87Nbty7SulXLOJdhJgIIIIAAAggg4AcBvzwXJtCdDIHuFq3a227C0qVNKyOHDwn5h4vefHfo3EP27d9vr+2mjzwk19WsHnKdj3hrtHlzdJ6t93YxPstkMr99OpO5gPmHbd/ez4Z0aT71i69k4qef2XUfaNRAbr6xbsD2z9RGXVjHwf70s89lv2nn3XfeLhoUd4pmjXd/8lnnq3Tt1F6uLlXS/a4Ty5avlIGDh9g6DVYPG/KKbeeuXbulc/fYFwDU6JX+fSV79sCHDL9v2Ci9XnzZrqvb1e2HK8NHjpJf584PmKUvBmiX8BQEEEAAgfgL+OWGJv5HxhoIIIAAAgggcD4JaAbvzTffFnLIXTp3krYm+/VMRcf1njs39t/jzrKaPdyje2ymsVP3xBM9bfau8z2aTw0SBwckwwUHmzZ9VEaPfifOTd5++21y2GRzT/9hRpzLaSB0+rTvJG/ePO5y4YK4FU128PwFC9xlIk1oVuu0778J+8LAkz2ftlnokdYNrteA/ddffe5uy4+Bbn3JoEHD+92s9eBjDPe9hnmhYMzotwO6og8X6K5Wtart/vxM50XP8cyffjBD1+UIt7uwdfENdP/440/yaJNmIdv66supUqJECVsf7lp2XjgIXjExA91ne905bXv+hd4yduy7zteoPp95+inR3ysFAQQQQAABBBDws4Bfngv7KtCdnBeEjnG1ds3KRNmldwzughcXkCqVK0lMpkymm7I67va3bN0qzzz/og2Ia6UGc2tWryZ58uSWDaZLpIWLFsvyFavc5b2BZG8msy6gQfAqpvs0DURrgHjxkqV2rG5n5XBjdEfTxqHD35J5CxbazWi34K++Evg2ts7TZZyimd2lTbdZJ06ekAULF8ucOXPdsbbvvP1WubfeXc6i8kKffvY4tULH065Tu6ZcVbKEbN+xw7Z/rglea6a2lhZNH5Hq1arY6eA/q1avNdnxgWOEt3qsmVS+NvTt++B1+Y4AAgggECrglxua0JZTgwACCCCAAAII/CcwY8aP0qRp8/8qTk/NmDHN/Nv5kpD6hFRoF87alXN8iwYkZ/zwvenhLLe7arjgoDszkSa0q/OnnnrS3Vq4QLc7M4oJzf5+qW+fgCUnT/nMdJUe/xfPvVnSfgt0Hz58WG6/4+6Q3gMCYCJ86dSxg+0q3pkdLtDtzIvm8+GHHpRevZ6PZlG7THwD3a8MGixvvDEsZPsLF8x1A+zhruWkDnQnxnWnB7Vx40apc/2NIcenFfq71eIMS2C/eP7MnzcnQd3HezbBJAIIIIAAAgggcE4F/PJc2BeB7vIVKsnu3XuS9YTmyJFdFi4IfFs7oQ3QLsX7v/JayOqj3nwjILt7vel+vHff0PHCgle8645bpb4Zf8tbNItZs5nPVB564L6AALuzfDRtfPr5Pu4435p5/eaw1wLeNNZtTf3ia5M5Hjo+k7Mf/axQvqxo8FlfJnDKps1b5Llefd1Av1Mf/KkBaw10e9cNXqZtx65y4MBBWx2pncHr8B0BBBBAILyAX25owreeWgQQQAABBBBAIFbgIzNedY8e/wV1HZc1q1dEHCfbWSaaTw1uVrq2atigV98Xe0v16tVt72hj3hkrEydOCtnkAw/cL7qcU8IFB3WeBtd0jG3taW38+Pfj7Lq6Xds2cuDgAdHueRl5owAAQABJREFUpdeuXeds2v2sUqWyfPD+ePd7XIHuumY88Ub3NZQMGTLItGnTI3afPvfXX8wL+7FZ4pFMdBzpXi88b8eTPnDggBlH/CPp33+g2w5n4rPPPjUvz18tOpSZdgOupVnzFvLnn385i9hPDdbXrlXLTmfNmlXy5ctrp8MZOgHWhG4znJE3KK87jjQWvGZsd+zY3gw3V8C80P+bdOvWI+z1MvOnGW438HEFujVjukGD+vac6Bjyc+b8ao/b+0cz7TXgGm2JNtCtPRN+++13Ybst12t02dLF7i7jOg/uQqcnEiOjO7GuO23Su+PGy3PPvRDcTBk/bqwdk1tnLFu2TJo2eyzkt9ivX1/7mwlZmQoEEEAAAQQQQMAnAn55LuyLQPeo0WNkkHlL9NChw8ly+rVb7S5dOkmzpk0SbX/r1v8uw0a8Lbt273a32eu5p+SSwoXc7zqxxvzjU8ex1izu4KLZ4PfWvzvsGNa6rGZ8f2TG6/oj6B99Ok+7NG/W5GE7trd+D1fO1MYFCxfZY9DM6nDBdmeb30/7QSZP/cINNjv1F5l/cN54w/Vy5+23OFUBn1u3bZcJH0802etLAur1i2aQ333X7VKnVs2QecEVH33yqXz59be2ulrVyvJYs0eDF+E7AggggECUAn65oYnycFgMAQQQQAABBM5TAc061exTb9Eusmf/MtNbleBpDTS2ads+ZP3hZuzmW8wYzk7R8X7bte8ounxwWbH8N9HnEVrCBQe1ftbMGVKwYEGdlEgBPZ33/nvj3ECcfr/zrntCxooODoKGC+Lquvfcc7cMNONmp0uXTr/aouMoa1AyuPR76UVp1Og+W/3V199I69ZtAxZR8+++/UqyZMkSUK9jeL/4YuzY2s4MfSbz9NOxw5w5dTfdfGtI0H7oG0NEu5EPLuEMnUC3d9n4bDOcUXCgO9xY8GXKlJEJH74nmUzvfk5ZvnyF3HHn3c5X91O7w2/Z8jH7PVKg+7rratrx3S+44AK7nF5X7Tt0ki+++NLdjjOxfNkSufDCC52vcX6GC3QXN8PBXX99bI+EJ0+clG3btslvS5dFzFhv07qVdO3a2d1PtOdBV0iMQHdiXneDX31Nhgx5wz0WndCxx3UMcm+ZPv0HeevtUaIvWmTLdpFkuyibHY6gVq3rvIsxjQACCCCAAAII+ErAL8+FfRHo1jN/4sQJ0Rv35ChpTbay9x9wiblPDdZrd9x646vB20hl//4DstX84+HgwUPmHyQxtgvzuJb3bmffvn2ybfsO2bt3r+g/Igvkzyd6TNGWuNqo3aSfNG/uZsqYMc7N6du9epw7d+42lmmlkPmHuB5HNEXfrNag985du0SD49oFu/OPt2jWZxkEEEAAgcQT8MsNTeIdMVtCAAEEEEAAgdQoEK6L5aJFi5pxqmNfkj7bY+7YsbNM+WxqwGZ0+zpudZo0aQLqly1bbgPPAZXmi47PXLt2bGZyuODggw8+IH169wpY7elnnpX33vsgoE4D2L/O+TnguYaO7d27z4sBy+mXDb+vdevCBXF15k8//iCFg17SP3LkiFSoWDkkI1mD4oMHxWZnhzPRrHXNXg8u+hyg5FWlA6o1wPrtN4EvBMQnKB3OMKkD3evWrZcbbwp9uX/Y0Nfl1ltD6x97vJV89933AcetGe9TP5ts6yIFuj+fOkVKlboqYD0dF/zW2+4MqNMv33zzpVxRvHhIfbiKcIHucMtFqtNs7mnff+tm1ety0Z4HXTYxAt2Jed1p1vrjLVtr0wLK7bffJg89+D+pUKE8z6sCZPiCAAIIIIAAAqlJwC/PhX0T6E5NFwfHggACCCCAgF8E/HJD4xdP2okAAggggAAC50YgUhfEv69fExKITkgL777nXtGxpL2lQ4d20rFDaJa3LlOxUpWQro41iK3BbC3hgoOa3Rzc89zHn0yU7t2fsOs4f+rVu0cGvTLA+Wo/ly5dKnfdXT+gTr+cKdCtmchTJk8MWU8rej71jHzwwYcB86pVrWoC7+/aunAmGgjNmze2a/GAFc2XDRs2BFQFd4GtM1N6oHvmzFnS+JHQ3gG92freg5w06VPp0rW7t8p2T+90/R0p0L161XLbZbl3Rc3wv6pUGW+Vnf7yi6lSsmSJkPpwFWcb6J408WMpV65swKbDXcvhXjjQlRIj0J2Y150mklxTtkLA8QR/0d+IHnO1qlVEu6ePNns+eDt8RwABBBBAAAEEUpqAX54LE+hOaVcO7UEAAQQQQCAFCfjlhiYFkdEUBBBAAAEEEEiBAuG6M9Zmzps7W3Lnzn3WLQ4XuI5rjN6GDe+X+QsWBOy3ffu20qljB1sXbXDwEzN8WbfuPQK2c1/DBvLyy4HdgEfKIj9ToFu7BNeuwcOVocOGy8CBgwJmebPkw5kELBzFl+AAcUoPdIc7H+EC9s6h/zJ7tnm5obHz1f10jjtSoNt73tyVzETRy0Izt5Mj0K3jvfd/uV9I5r+2LdprWZdNjEB3Yl93ffr0FR1SMdqiL6P06NGNTO9owVgOAQQQQAABBFKsgF+eCxPoTrGXEA1DAAEEEEDg3Av45Ybm3EvRAgQQQAABBBBIyQKRAoaj3h7pjj8cV/t1PGUdXizt6W7ItTvynDlzSOnSsd1tX126bEg33ppVrdnV4YoGNzXI6S06LrOOz6wl2uBguMBqYga6g8ef9rb3zTffkn4v9/dWBYxfHM4kYOEovixaOF+yZ/9v2LeUHujWDHfNdPeW4LHQvfPmz58vDe+LzeL31i9ZvEAuuugiiXTdJmegWwP12kW3Fh3Obs6cX71NtdMPP/Sg9Or1fEi9VkR7LeuyiRHoTorrbvKUz6RTpy7axKhKxQoVZMSIoaLnnoIAAggggAACCPhVwC/PhQl0+/UKo90IIIAAAggkg4BfbmiSgYJdIIAAAggggICPBXQMaM30PHjwYMBR6HjIUyZPkrRp0wbUe7+cPHlSqteoJVu2bPFW27GmdcxpLTo2so6R7C3du3eVVi0f91a507VqXy9//vmX+10nnnn6KWna9FFbF21wMKkD3Rqw+/jjwO7JbQPNn+df6C1jx8Z2U+7UVapUUT6aEDtmeLig9L331pfLLivqLB7nZ0ymGGnc+KGAscbDbVMzzjXzPLhEaxifbYYbx9z7MsD330+TFo+1DG6KrFu7KuA4nAW+/PIradM2tHt7J5CdEgLdwWPDh3PV49GxucOd23DLP/XUk9K8WVOHwf0MN+Z88IsbZ8paD3c+z/a60wbq7//7adNlxowfbbA/+P8l7kGcntBhC3T4AgoCCCCAAAIIIOBXAb88FybQ7dcrjHYjgAACCCCQDAJ+uaFJBgp2gQACCCCAAAI+Fwg3prQe0uBBA+Wee+6OeHSTJ0+RTp27hswfPPgVuefuu2x9s2YtZPoPMwKW8Y5X7Z2xefNmqVb9Om+VnfYGbMMFB8ONa5zUgW5t2MIFcyVHjhwB7T1x4oRcX/fGkGC9t6vzJk2b26Cgd8VXBvaX+vXreaviNR0uiOl1824sWsP4bPNMgW4dp13HiA4uH3/0gVSsWDG4Wp555jkZ/977AfWXXFJYfpwx3dalxEB3pG7wvQF/7wGFuw6Cg+fO8uG69I9voDvc/s72unPa53yeOnVKNm3aJEuW/CYTJ04K+e3rcvoSzdTPJjur8IkAAggggAACCPhOwC/PhQl0++7SosEIIIAAAggkn4BfbmiST4Q9IYAAAggggIBfBebOnSeN7v9f2OZ36dxJWrV6PCDrVjO5NeO2XfuOYddZuGCeCQBnt/M0s1kznIPLV19OlRIlSgRU67jWOr51cPl1zi+SN28eWx1tkDY5At3escOdNn/11dfSuk1otmrPJ3tIixbN7WIjR74tL/V72VnFfpYpU0YmTfwowFlnbN++XV55ZbBkzpJZLsp6ke22W22DX0AIF5QO9wKAbjNaw/hs80yB7mPHjkm58pVCeg7wvgCgbdOye/duKV/h2tgvnr+PPtpYnns2tvvzlBjo1qZ27NhZpnw21dPq2EnN/tdeALwlXJa2zp/9yyzJnz+fu+g0ky3dvEVoDwjxDXQn1nWnv399CeHAgQNy8MBBOWB6g9As7kcfaWyD2G7DzcSLL74kb48a7a2y05Ey+UMWpAIBBBBAAAEEEEiBAn55LkygOwVePDQJAQQQQACBlCLglxualOJFOxBAAAEEEEAgZQtECtBpqzWT9tprK8kVxYvLLhOEnDHjp5DuyJ2j69ypo7Rr18b5Klu3bpMqVau7350JHaN3/Lh3bLBbs6AnTPhYnno6cAxnXfa662rK2Hf+C5RFG6RNjkC3tk+P9777GkqmTBll+vQfpHOX2LHEdZ63eLuv/ueff6RGzdre2Xb6gQful6dN19UXXnih/b53715p3vxxmb9gQcCyGhSfMnliQF39exvKokWLA+o0c3bQKwMliwmSZ8+eXWJiYuz8aA3js80zBbp1x88930vefXdcQBv1i47B3qF9O2OYyXaDrV2ca3Z0cJk08WMpV66srU6pge4//vhDate5Ibjptt0TP/lIdAx7p4we/Y707vOi89X91N9Gt66dJU+ePDJ79pywgWJdOL6B7sS87sINSVC8eDGbqZ0xY0b3WMJlkefPn98E82e6yzCBAAIIIIAAAgj4TcAvz4UJdPvtyqK9CCCAAAIIJKOAX25okpGEXSGAAAIIIICAjwU0qHrLrXeEjLcdn0PSAOyHH4x3A6rOuhrM06BeQoo3uKnrRxukTa5AdzTHFK6r9iee6CkTPvo4ZPXMmTPLNcZx3/59YYO9ukK4LskjZQc7Oxg+fKjccvNN9mu0hvHZZjSB7r///kdqXlfbaVK8PuvWvV7eGjnCDRSn1EC3HlSkgP4Icw60G3OnLF68ROrVb+B8jfdnfAPduoPEuu7eeutt6ftSYK8EzgHceOMNotfxzJmzZOfOnU61+/nwQw9Kr17Pu9+ZQAABBBBAAAEE/Cbgl+fCBLr9dmXRXgQQQAABBJJRwC83NMlIwq4QQAABBBBAwOcCa9asEc2m/fPPv+J9JJr1rRmruXPnDln38OHDZnzm+rJ27bqQeXFVdOrYQbR7cG+JNkibkgLdP0z/TooUKeI9DNvV82233xlv6xo1qss7Y0aFdHE+ecpn0qlTl4B9eL+0avm4dO8eO556tIbx2WY0gW5tT6Rx3b1tDZ7WoOmMH74PuLZScqB727btUrlKteDDsD0jaGZ/+vTp3XntO3SSqVM/d79Hmrj33vp2zGvv/IQEurWL8cS47nbv3iP3NrhPNmzY4G3SGac1W/2Lzz+TfPnynnFZFkAAAQQQQAABBFKqgF+eCxPoTqlXEO1CAAEEEEAgBQj45YYmBVDRBAQQQAABBBDwkYBmdnfp2l10XOBoS4MG9eXZZ56WrFmzRlxFA2MdO3WWn36Krsvip5/uKU2bPOpm8DobjjZIm9SBbs3M1S6YdQzyuMr4cWOlevXQoKeuowHRtu3ay7x58+PahDvv+jq1ZejQ120X327l6YlTp05JGzM2+FdffxM8y36vUqWyfPD+eDsdrWF8thltoFsboOO7t2nb3rblTH90HHfN5C5UqGDAoik50K0NHfzqazJkyBsBbdYvfXr3kgcffMCt37Jlq/zvwYfjDBg/+UQPe/zBZgkJdOuOE+u602EJ7mt0f9Qva+j1279/P9FgNwUBBBBAAAEEEPCzgF+eCxPo9vNVRtsRQAABBBBIYgG/3NAkMQObRwABBBBAAIFUKrB06VL54MOP5IMPPgx7hJple889d8m99eu74yaHXdBTqWNxT/38C3lv/Psh4047izUy410//PBDUqrUVU5VwOdjj7eS7777PqCuX7++out5S7hAd7gukzXL/Kabb/Wuaqc3/L7WrYsriPvxJxPl5ZcHhHTRXK/ePdKmdSu5/PLL3O2Em1CTTz+dYsYo/yiiiWZxt3z8MalWrWpI4N+7Tc2cH//e+/Laa6/bjHHvvKJFi8r0ad/aqmgNdeFotzlmzFjp1buPd5fm+rhbBg8aGFDnfNGxrMeb6+CDDyeEtFWX0fGeH2ncWOrXvyekK3ydHy7QrdfksqWB45TrslqKXlY8dsLz1ztuuqc67GTXbj1CMqqbNW0i+kJGuLJv3z6pVv26kGPTIO+8ubMDzqMaDx78mrxvfmuace0UfZGiVavHRa9bHf+9eYvHnVn2M/h6js8xJtZ1d/ToUfniiy9l+Ig3w/bYoOdEx1W/9ZZb5IEHGgUcd8DB8AUBBBBAAAEEEPCRgF+eCxPo9tFFRVMRQAABBBBIbgG/3NAktwv7QwABBBBAAIHUJXDy5EkbxN2ydavsMVnZ2bNnkzx58pr/cod0nx2fI9+9e7cZD9xsc88eu1q+fPmkQIH8YYOa8dluUiwbV6Db2Z+ORazdOF+ULZsUKlhILrwwxpkV9eeuXbtEs2QdE/VQl5iY+G1LM7F37Nhhx1tPkyat7SY6T548Ubcj3IJJsU3dz7Fjx2TT5s2yzRz30WNH5SLTK0C+fPklb96za2+4Y/BD3fbt282522l/XxoUT5MmTZI3O7GuuwMHDtjrd8uWLSKm2cUuL0YX5Ul+9tgBAggggAACCJwLAb88FybQfS6uDvaJAAIIIICATwT8ckPjE06aiQACCCCAAAIIpFiBaALdKbbxNAwBBBBAAAEEEEAAAQQSVcAvz4UJdCfqaWdjCCCAAAIIpC4Bv9zQpC51jgYBBBBAAAEEEEh+AQLdyW/OHhFAAAEEEEAAAQQQSKkCfnkuTKA7pV5BtAsBBBBAAIEUIOCXG5oUQEUTEEAAAQQQQAABXwsQ6Pb16aPxCCCAAAIIIIAAAggkqoBfngsT6E7U087GEEAAAQQQSF0CfrmhSV3qHA0CCCCAAAIIIJD8AgS6k9+cPSKAAAIIIIAAAgggkFIF/PJcmEB3Sr2CaBcCCCCAAAIpQMAvNzQpgIomIIAAAggggAACvhYg0O3r00fjEUAAAQQQQAABBBBIVAG/PBcm0J2op52NIYAAAgggkLoE/HJDk7rUORoEEEAAAQQQQCD5BRYvXiI/zJgRsOOSJUvKLTffFFDHFwQQQAABBBBAAAEEEEj9An55LkygO/VfixwhAggggAACCRbwyw1Ngg+QFRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQCBPzyXJhAd8Bp4wsCCCCAAAIIeAX8ckPjbTPTCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgkX8MtzYQLdCT/HrIkAAggggECqF/DLDU2qPxEcIAIIIIAAAggggAACCCCAAAIIIIAAAgggkEwCfnkuTKA7mS4IdoMAAggggIAfBfxyQ+NHW9qMAAIIIIAAAggggAACCCCAAAIIIIAAAgikRAG/PBf2TaD7xIkTcvLkyWQ512nTppV06dIly77YCQIIIIAAAilZwC83NCnZkLYhgAACCCCAAAIIIIAAAggggAACCCCAAAJ+EvDLc2FfBLpHjR4jgwYNlkOHDifLNRATEyNdunSSZk2bJMv+2AkCCCCAAAIpVcAvNzQp1Y92IYAAAggggAACCCCAAAIIIIAAAggggAACfhPwy3NhXwS6y1eoJLt370nWayBHjuyycMG8ZN0nO0MAAQQQQCClCfjlhialudEeBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDArwJ+eS7si0B38StKyvHjx5P1WkifPr2sXbMyWfeZHDs7deqUrFm7TjSQnzdPnuTYJftAAAEEEPCxgF9uaHxMTNMRQAABBBBAAAEEEEAAAQQQQAABBBBAAIEUJeCX58K+CnQHB58XLFwoDRo0sie+Ro3qMu7dd9yLYOiw4TJw4CD7vWvXztKmdSt33sONH5VZs3623z/5ZIJUKF/enecE1YP35S7g84lXhwyTxb8ttUfR6vFmUrlSRZ8fEc1HAAEEEEhKAb/c0CSlAdtGAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTOJwG/PBcm0J0Mge4VK1eJSaSWi7JmkcKFC53T30Gzx9rIiZMnbRsqlC8r7Vo/fk7bw84RQAABBFK2gF9uaFK2Iq1DAAEEEEAAAQQQQAABBBBAAAEEEEAAAQT8I+CX58IEupMh0P1o89hs8hJXFpcnunU+p1fxBxM+kW++mybp0qaVzh3bSamrSpzT9rBzBBBAAIGULeCXG5qUrUjrEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB/wj45bkwge7zLNCtP6H9Bw5IhgsukIwZM/rnF0VLEUAAAQTOiYBfbmjOCQ47RQABBBBAAAEEEEAAAQQQQAABBBBAAAEEUqGAX54LE+g+DwPdqfD3xiEhgAACCCSRgF9uaJLo8NksAggggAACCCCAAAIIIIAAAggggAACCCBw3gn45bkwge4kCnRv2bJVpnz+pb3wZ8+Zaz8vMFnUFSuUs9Pp06eXZo8+7P4wvp/2g6zfsFFy5sguDe+tJ+vW/y7zFyySxUuWytGjR6Vrp3Zy8cUF3OV1YtXqNfLr3PmyZetW2b17r2TJktmOAV6nVk25JMJY4MtXrJJZv8y222nUsL5kz5bNTp8yg4i/+96H8u+//8rVV5WU6tWqyB9//ClLl6+QZea/g4cOy6Vmm+XKlpEK5WOPwa6YwD87duyU76fPkD//+ttmmOfOlVOKF7tcSpa4UooWuTTOreoxrF6zVn7fsEF27twtuXLlsMd984113eNxNjDz59miY6RrufO2W0IMneX00zkHOn3PXXdIvrx5dJKCAAIInNcCfrmhOa9PEgePAAIIIIAAAggggAACCCCAAAIIIIAAAggkooBfngsT6E6iQPfiJb/Jq68Pj3hJ6RjZo0YOdec/9Wwv+WfTZhPoziH1690lb48e687TieeefsINAGtge8TI0bLRBKIjFQ1Ut2j6SMjsDz+aKF9/+72t79vrWTfwe/LkSWn6WBtbX7N6VVs/4eNJIetrxVUlS0in9q1FA/cJKVO/+Fomfjol4qq33HSDaBA+TZo0ActowP/Nt8fIgoWLA+q9X7p36WDb59Tpseoxa6lbp5Y8/OD9zqyAz+PHj8vjrTvICeOgZcQbgyVTpkwBy/AFAQQQOB8F/HJDcz6eG44ZAQQQQAABBBBAAAEEEEAAAQQQQAABBBBICgG/PBcm0J1Ege7fTXb2ex98ZK+t9b9vsJ8aGHYyrbNnzybtWj/uXntOoFsD4E6wVWfmyplTjh07Jk/37CZ58+Sx0z16Pie7du9219V1Lr/8Mjl0+LD8/fc/bv2dt98i99a72/2uE9EEujOZsbv/PXLErqeB9xwmy3zT5i1y2GzfKbffepPNPHe+R/s5Z+48G6R3lndMDphxw7du2+5US+mrS0mXjm3d7zrxfO+XAoL72rYiRS6Rf/7Z5K6rbX9lQF/JfOGFdl0dj7xdx252WucNNwHs4AC6zly4aIkMGTrCLle2TGnpaAL5FAQQQAABMT1n7LIMuUzPGxQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB1C/gl+fCBLqTKNDtvcQfbd7Kfi1xZXF5oltn7yx32gl0OxX17r5D7jBdbadLl86psp+j3hknM2f9YqdjYmJsZnUxE+ROa4LdWrRL75f6D3LnD389dtpWmD/RBLp1WQ0Kt2/b0s2O1q7Np0z9UiZ/9rmzKRkx9FW7nFtxhom169bLi/0Guks1a9JYNHvcKWvWrpOXBwx2A/19nn9aChUqaGfv2rVbOnfvaae1S/Ee3TrZ7Hdn3alffGWyxD+zXyuULxvwEkH/V15zuy9Xfz0PweXVIcNk8W9LbXXnDm2lTOlSwYvwHQEEEDgvBfxyQ3NenhwOGgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQSAIBvzwXJtCdAgPdd95+q8nEvivsZblh4x8yfcZPsnTpcunaub0UKnhxyHJvmW7Pf/5ljq1//dUBkjVLFneZaAPdnTu0McHeq931nIk+Lw2w44frd2936s78uD579HzWzbxu06qFVKpQPmTxb76bJh9M+MTWaxBcg+FadPzs8acz5JubLtlrmK7Zg4t2a66Z3NdWqiBXFC/mzp63YKEMHf6W/R6uS/cjJnu9dbvONsAeV9a3u0EmEEAAgfNIwC83NOfRKeFQEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBJBXwy3NhAt0pMNA99LWBkjlz5gRfoJMmfyafff6VXV+7/9ZuwJ0STaA7S5bM8sar/2VeO+vq57ffT5f3P/zYVrVs0VSqVK7knR1xevuOHdLtiWfs/AL588tLfZ4Lu6xmji8y45ufMmNla1fthQsXssv9OPNnGTN2vJ3WMcJ1LO5oy4kTJ6Rl206223ftKv1Nk4nuZMDrNmbPmWvH/tbpG+rWkYceuE8nKQgggAACRsAvNzScLAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHEEfDLc2EC3Sks0K2B2LeGD4nXVagX29Zt22T79h2y3owNroFbHddbS7vWj0mF8uXc7UUT6Nau0J9+MnZca3fF0xOLTRD61deH228aENbAcDTl13nzZfibo+yiDerfbbtlj2Y9Zxkdv1szwp2SPVs2ufWWG21WeM6cOZzqiJ/eLt+Ds9VfHjhYVq5aY9ft88IzYbPkI26YGQgggEAqF/DLDU0qPw0cHgIIIIAAAggggAACCCCAAAIIIIAAAgggkGwCfnkuTKDbh4Huf01X27Nn/yqa5fzXX3+7Y1qHu7oTEujW8al1nOpw5TfTZfqg196ws+IT6P762+/t+OC6YnCbwu0nXN3kz74IGCPcWUYz0MuWKS21rqshxYtd7lQHfP5uXgDo9eLLtk67TNeu07UcOnRYWrePHTddx/5+uW8vW88fBBBAAIFYAb/c0HC+EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBJHwC/PhQl0+yzQvWvXbnm+90uyb//+sFdqzhw55Oixo3LgwEE7PzioHE1Gd1IEur1B6q6d2svVpUqGbf+ZKjWj/NMpn8sff/4VdlHNMH/w/oaSJk2akPkduzwhe/bulXRp08qbw16T9OnTy4yfZsk7775nl73/vnvllptuCFmPCgQQQOB8FvDLDc35fI44dgQQQAABBBBAAAEEEEAAAQQQQAABBBBAIDEF/PJcmEC3zwLdTz3bS/7ZtNleqxqwrVu3tpS48gq5pFAh0S68dezpn0ym9+ixseNZp5RAtzeg/MjD/5M6tWqe1e9NA9arTHfjK1atlnnzF8rhw4fd7Wlmd5PGD7rfnYkpU7+wQXL93qZlC6lUsbz07ttf1v++wS7y+qsDJGuWLM7ifCKAAAIIGAG/3NBwshBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQSR8Avz4UJdPso0K1dlrds09FeoTExMaab7RfkoqxZQ65Yb/Z0Sgl0L1u+UgYOjh17vGaNatLs0YdD2p3QihMnT9rg/thx79tNZMqYUUYMfTVkc7t275bO3Xraes1ab970EWnfqbv9XrLEFdKja6eQdahAAAEEzncBv9zQnO/nieNHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQSS8Avz4UJdCdjoPvSSwrLC8/GBlqDLzQnU/uCCy6Qt4bHBoSDl/GOM12zelVp1qRx8CL2u3ZtvvGPP+10Sgl0Hz9+XB5v3cEdT3zI4P5hg/Tbtm+Xwa8Nk1OnTkm5smWkUcP69jg0g3u5CZYfOHhQbr6xbtjjfuKp52XL1q123sB+fSR37lwhy73Qp59s2PiHra9/z50yafJUO+1keIesQAUCCCBwngv45YbmPD9NHD4CCCCAAAIIIIAAAggggAACCCCAAAIIIJBoAn55LkygOxkC3S1atZdjx47ZsaFHmiB2unTpQi60aALdR48elcdMsFhLkUsvkeefeTJkO99+N13en/CxW5+cgW4NZn/59XeyYuVKqXt9balUobzbDp0YOeod+WX2r7buqpIlpGO7VpIhQwZ3GQ1uP9urr/z119+27pGHHpA6ta+z0207dnXHHdf1yl5Txl1PJw4eOiRdTLa2Zr3rywIjzRjc4cbpnvXLHHl79NiAdbUL+EjnJWBBviCAAALnoYBfbmjOw1PDISOAAAIIIIAAAggggAACCCCAAAIIIIAAAkki4Jfnwr4KdCfJmYqw0fTp08vaNSsjzI1f9evD3pQFCxfblQpeXECqVK4kMZkyyQ1167gbiibQrQt7M5KLF7vcbuuyokX+z96dwMlRl/kff/runnty3wnkhtzk4pKAgMihooICAnKJ14rX6rruKrvurrqurrrqeq2srvtfD0BFARVBUAnkgFwkJBzJ5L4zkzl7eqZn/s9Tkx56kpnMPd2/7k+9bLq7urq66l21r63Ud57nJ/t03O4Nm16Q1Wuea1+nvRjKoPspHRv8vhNjg9tv//uXPi/l5WX20pssCP/7e/9Z9h844L0fOWKEXHnFZTJ+/Dh55dXt8syqNe0ht4XP3/qPr0hE25DbZJXXD/3mEe+1Bdmvv/gisfbjLS2tsmHjJv3u6vYg/NzlS+XuO2/zlj35P40ahL//rz7aXllun5+uOv7k7/MeAQQQyDcBVy5o8u24sL8IIIAAAggggAACCCCAAAIIIIAAAggggMBgCbhyX9iJoHvROUuksrJqsI5Vp+u1gPb559Z0+llvZ255cav865e/dsrX/us732iv7u5p0L1128vypS9/tUNQe/KKx4we3d7CeyiD7p/87AH57e//0L45f/vJj8mM6dPa39uLmppa+dTf39seSnf48MQbC7I/9uEPyqyZM9o/tkrtf/r8l2TPnr3t8zp7MXbMGPnEx+7pELCfvNy3vvP9Dn8Q8JlPf1LsjwWYEEAAAQROFXDlgubULWcOAggggAACCCCAAAIIIIAAAggggAACCCCAQF8EXLkv7ETQ/V8/uE++8pV/l/r6hr4ci15/JxaLycc+9hG54/bOq4J7vUL9glUsf+vb35djlZXtX//Hz35aJk2c4L3vadBtC+/bf0DHsf6mHD5ypH1d9sIC4ltvvlH81or7+/d5nw1l0G3bYxXntbV1YtXmn/6bj3fYvtQbG4f7//3fz2W9VmKnT1bFfcYZU+Q9d75bRo0cmf6R99rC7gce/JU88cenTgn67ZhZpfy7bri+/Y8HTlnBiRkvbt0mX/y3r3rvykpL5atf/kJXizIfAQQQyHsBVy5o8v5AAYAAAggggAACCCCAAAIIIIAAAggggAACCAyQgCv3hZ0Iuu2YJJNJbVPdMkCH5/SrsaC4s3G0T/+tnn1qYb0FwqWlJWIha3+mhoa4bN9RIY2JRhmnlcyjR4/qdFzq/vxGb79r42xb0F1cXNTtV83i4KFDEo/HpaSkRMaNHdOj7bcW6IcOH5EjR456x2nK5IlSWFjY7e+xAAIIIIBA7wVcuaDp/Z7xDQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHOBFy5L+xM0N0ZMvMQQAABBBBAYHAFXLmgGVwF1o4AAggggAACCCCAAAIIIIAAAggggAACCOSPgCv3hQm68+ecZE8RQAABBBDotYArFzS93jG+gAACCCCAAAIIIIAAAggggAACCCCAAAIIINCpgCv3hQm6Oz18zEQAAQQQQAABE3DlgoajhQACCCCAAAIIIIAAAggggAACCCCAAAIIIDAwAq7cFyboHpjjzVoQQAABBBDISQFXLmhyEp+dQgABBBBAAAEEBlkg2SrS1NIqhxqSsrWqUV6sTOijUXbXNcuxxqQ0NLdIqy4TC/qlLOKXMQVBmVIUkjNLwjJVH+MLAzIqFpSCoG+Qt5TVI4AAAggggAACCCCAwFAKuHJfmKB7KM8KfgsBBBBAAAHHBFy5oHGMlc1FAAEEEEAAAQQyKmDhdn1zq1Q3tcjhhmbZr8H2ztomfehzTZMG381SnWiRRl2uVZPusN8nxeGADItq2K3B9lgNvMcVhvR1QIZHAlIS9mvY7fcCcQu9wwGfhPQ7+sSEAAIIIIAAAggggAACDgq4cl+YoNvBk4tNRgABBBBAYKgEXLmgGSoPfgcBBBBAAAEEEMgFgeMaYu+ua5JtVQl5/nDcC7ePJ5JSq+F3nYbfjVrqbWG4/s+bfBpYW3CtebZENL22Cu+oPpeEAl6l96hoQCYUhWVCYVCfQzJSA/BSXdiWYUIAAQQQQAABBBBAAAH3BFy5L0zQ7d65xRYjgAACCCAwZAKuXNAMGQg/hAACCCCAAAIIOCwQ1wDbKrUt5N6iLcpfONYo6480yl59r13KJWl9ynsxWfhdFPLLCA26x2uFdyrothbnw7TVuYXdFooXpMJxfa3/E/0fEwIIIIAAAggggAACCGSxgCv3hQm6s/gkYtMQQAABBBDItIArFzSZduL3EUAAAQQQQAABFwQOakvyTccSsvFoXDboY5e2Kq/UsbjjWsndoiF372JuDay1YDug5d5W5W2ty62C21qXe23OtaX5aA28LfyeaCF4UVArvYNSrMG4VYYzIYAAAggggAACCCCAQPYKuHJfmKA7e88htgwBBBBAAIGMC7hyQZNxKDYAAQQQQAABBBDIYgFrRV6jLclfqU7IX/Y3aBV3XF4+npBjGnIPxmRjelultwXbXtCt7cwn6mN0gY7zbWN662cxDcRT1d4RDckt+7bgnAkBBBBAAAEEEEAAAQQyL+DKfWGC7syfK2wBAggggAACWSvgygVN1gKyYQgggAACCCCAQBYIHIknZauOx71OA+5nDjbIqxpy12rwnUgNwj3A22ihdVBT69R43lblbaG2tTK3NudjC6zCO6Dhd9ir9h6u8yz41v8xIYAAAggggAACCCCAQBYIuHJfmKA7C04WNgEBBBBAAIFsFXDlgiZb/dguBBBAAAEEEEAgkwJayC1Wzf2qVXIfqJfndTzuLTou9yFtYZ6JyVqbl2lF9yit9J6orcwnaEtzq/QeFQt484s06S70qr3b2qCHNfj2a2BO/p2Jo8VvIoAAAggggAACCOSzgCv3hQm68/ksZd8RQAABBBDoRsCVC5pudoOPEUAAAQQQQACBvBSo17G3D2uovf5oozyyq1Y2VzbK8USLjsndkhEPzbklpMF1VKu7Y/rGqrwt2Lbwe7RWdY+z8bwtANfwe5yO710eDnjjeQfoaZ6R48WPIoAAAggggAACCOSvgCv3hQm68/ccZc8RQAABBBDoVsCVC5pud4QFEEAAAQQQQACBPBJobW0VLebWyu2kvKAV3KsPNciT++qloqbJm59NFD6fBt4aere1NH8t6B6voffIaNAbz9vG+y6wSm9dLnqixblVedt3mRBAAAEEEEAAAQQQQGDgBVy5L0zQPfDHnjUigAACCCCQMwKuXNDkDDg7ggACCCCAAAIIDIBAiwbdNvz2tuNN8lBFjazRoHt3XbNUNSYHYO0Dv4qABtY2nre1NrfxvAtOVHoP00pvq/Ieby3O9dkqvcdqpXdxyCdW5O0n6B74g8EaEUAAAQQQQAABBBBQAVfuCxN0c7oigAACCCCAQJcCrlzQdLkDfIAAAggggAACCOShgLUmr9IW5TYm9/3bq2Wjti6v13lNln47MFmdtoXYRRpoj9bxvC3onlQc0jG9g17wPVzbnBdaGG4t0DUYt0dEk+8gLc4dOLpsIgIIIIAAAggggIALAq7cFyboduFsYhsRQAABBBDIkIArFzQZ4uFnEUAAAQQQQACBrBSwcbm3VCZkzeG417J8e3VCkieqvLNygzvZKAu7Lbi2Km8by7utytun1dx+saB7gobfFnxPtPG89XlkLCBFuhwTAggggAACCCCAAAII9F/AlfvCBN39P9asAQEEEEAAgZwVcOWCJmcPADuGAAIIIIAAAgj0QsAKti3Q3l7d5AXcNjb3i5WNcjienS3Le7Fr3qIWY1v4XXaipbkXdFvgrWH3mIKAlOt8C8Tb2p9bO3S/hHR5zcqZEEAAAQQQQAABBBBAoBcCrtwXJujuxUFlUQQQQAABBPJNwJULmnw7LuwvAggggAACCCDQmYC1Jq9vbvVald+/vUZblzd443LHk260LO9sn9LnWV5tj9CJKu+YN6a332txbuH3KG1zbuH3ZG1zPknDb3tfGvZ7VeHp6+E1AggggAACCCCAAAIInF7AlfvCBN2nP458igACCCCAQF4LuHJBk9cHiZ1HAAEEEEAAAQROCBzXcbl31zZ5LcsfqqiRrVVN0uK1LM+NoLurA51qcT5Mw25rZT6pKOiN6T22ICjDdZ6F4F4orm3PCzQcj2jVt1WHM6R3V6LMRwABBBBAAAEEEMh3AVfuCxN05/uZyv4jgAACCCBwGgFXLmhOswt8hAACCCCAAAII5I3ALg25nz7QIKsPxWXdkbjsr28Wi7hbNezO5cnv83mhdViTa2tbHtNHoYbZJeGAN563tTW3Cu9JRWENwjX81jG+rcW5LsaEAAIIIIAAAggggAACnQi4cl+YoLuTg8csBBBAAAEEEGgTcOWChuOFAAIIIIAAAgjks4B1Jo83t8iLVQn59c5abVkel70aetc0teQzi47R7dOw2++1MLeg26v21rbmozXotirv4pAF41rlrZXeVvFt43mTfef1KcPOI4AAAggggAACCJwQcOW+MEE3pywCCCCAAAIIdCngygVNlzvABwgggAACCCCAQB4I1GmgfaghKc8fjcuvdtTIC8capUHT72Ydszufp4BWemuOrWN0a5Ct5dsWaluld3nEL2MLQ9543pM0+LYAfLy2ObfxvK2dOWF3Pp817DsCCCCAAAIIIICACbhyX5igm/MVAQQQQAABBLoUcOWCpssd4AMEEEAAAQQQQCDHBSzKtpB7k4bbqw7Wy5P76qWipinH97rvu2chdmEooFXeARmn4bYF3W3V3kGv8rtEq7uLNfAu1GA8ogE5wXffrfkmAggggAACCCCAgLsCrtwXJuh29xxjyxFAAAEEEBh0AVcuaAYdgh9AAAEEEEAAAQSyUCBVr71NW5Y/vKtWx+ZukO3VCalqzO+W5d0dqqCm19bW3NqVW5V3sYbb1sp8nFZ5Ty0JyZn6sOcR0aC3nC7GhAACCCCAAAIIIIBAXgm4cl+YoDuvTkt2FgEEEEAAgd4JuHJB07u9YmkEEEAAAQQQQCA3BBq1PXltc6us1zG5799e7T3buNw2n6nnAtbi3ILvkVrlPbUkrEF3WKaVhmRsQUCGaQBeGrbxvP3aAt3nVXj7dXkmBBBAAAEEEEAAAQRyWcCV+8IE3bl8FrJvCCCAAAII9FPAlQuafu4mX0cAAQQQQAABBJwUOKaV2y8fT8jaw3F5bE+tvKKvm7WYO9lK0N2bA2qxtYXXFnYXhXwaagekRAPuMRp8TyvV0Furu2eURWS0vrcqcKsIZ0IAAQQQQAABBBBAIJcFXLkvTNCdy2ch+4YAAggggEA/BVy5oOnnbvJ1BBBAAAEEEEDAKQGLsVv0Pzt1LO6/HKiXNRp0W1X3gfpmp/YjWzfWQm/LsodHAzKlOOy1MZ+pgfdkHc97dCwo5RG/BuJ+LxjP1n1guxBAAAEEEEAAAQQQ6I+AK/eFCbr7c5T5LgIIIIAAAjku4MoFTY4fBnYPAQQQQAABBBDoIGCdya09+aajcXlgR408ryH3kXhS6rRtOVP/BXwadFvNdlirtwv0YW3LS7XCe3JRSOYOi8hZ+rAqbwvCmRBAAAEEEEAAAQQQyEUBV+4LE3Tn4tnHPiGAAAIIIDBAAq5c0AzQ7rIaBBBAAAEEEEDACYFaDbQPNjR7ldy/1KD7hWONtCwfxCPXVuHt81qXn1UelrM16D67PKLV3m1hd2HQLzZs9+kbmlsdfqu0ttofI7R6y+o7ffa1/ddnofnp1zCIu8iqEUAAAQQQQAABBBDoIODKfWGC7g6HjTcIIIAAAgggkC7gygVN+jbzGgEEEEAAAQQQyHWBfXVtIffqQw3y7MEG2avvW3RcbotSmQZeIFXhrR3LT4zdHfRC7jkaeJ83JuZVeoe013nXQ3frkfEC7qQG3dpevjWpG9mix8uOmEbd/pAG5RF9TYX4wB891ogAAggggAACCCDQFwFX7gsTdPfl6PIdBBBAAAEE8kTAlQuaPDkc7CYCCCCAAAII5LmAtSxP6ODc2yob5be76+Q5bVleUZ2QqgQty4fy1CjQCu4R2rbcKrtXjCuQOeUhGRNrlaJgk4bYDdKSjOtzo4baCX3YPHskpKXFnps13m4Lutvqt7Wi2xfUoDvqBd4Wdvv0vUhI/P6wztMA3KfPGoS/9to+p/p7KI85v4UAAggggAACCOSbgCv3hQm68+3MZH8RQAABBBDohYArFzS92CUWRQABBBBAAAEEnBVo0KS7sjEp6zTgfnB7jWzSluXWxrxJw2+moRMIaJ/yiI7dPaYgILPKInLOCL+cP7JJJsaOS0vTHkkm9ktL8xFJNh/VQu5qDb5rtIi79kTw3da6vFUr8L2o2nqei7Y+96q6w/oyqgF3kfgDZeIPDpNAcLgEQiPFF9Dn8Eh9r6/187aJsHvojjq/hAACCCCAAAII5JeAK/eFCbrz67xkbxFAAAEEEOiVgCsXNL3aKRZGAAEEEEAAAQQcFTgST8pLxxOyStuV/35Pvbyq1dwWmDINpYB6exXaDVIYiMvISFzmlDbIpWPiMqv4mBS17pVgiwXdh/VRqSG3ht8adEtL/Ymgu7PjpYG1z8b5DmvAHfOCbAu6A8FyDbtH6MPCbnsepc+jdZlyDb4LdfkCXdYeMe97tD4fyvOA30IAAQQQQAABBHJbwJX7wgTduX0esncIIIAAAgj0S8CVC5p+7SRfRgABBBBAAAEEHBF4RUPux/bWe0H3tqqEHInreM9MQyhgIbU+Wo5r1r1HAs07JdJSIRMiB2RhebXMK6mT2SUNMjLcqG3KrX25tiqXJq3q1nblNja3jsvd9XQi7LbW5f621uU+a13utSzXSm999luoHSj2qroD4fESDE+SYGSKVnpP8AJwn7+w69XzCQIIIIAAAggggAACvRBw5b4wQXcvDiqLIoAAAgggkG8CrlzQ5NtxYX8RQAABBBBAIL8ErDV5XXOLbDqakAd31Mjzhxu8FubWypxpCAROVHC3ttR6Ibc0H9QMe6cOu12hz9ul1H9AJhXUytzShFwwyidnFllrc62vHvDO4gFv3G6/VnpbuB2MTNKAW8NuDbztvVf9rUG4L6CtzzX0tqC8bSzvAd+QIUDnJxBAAAEEEEAAAQQyKeDKfWGC7kyeJfw2AggggAACWS7gygVNljOyeQgggAACCCCAQL8EjidapKImIc8dbpRHd9fKtspGSWj4Tc7dL9aef1nH2bYKbgu1WxtfFGnerWNuHxNJ6nwNv0O+uBQEmmVmSau8fqxP5pSJjIj4pMAKswd0ssD6xHjeWt3tVXh7Vd5lEtSW5oHIRA2/p0soNk3D7zO8luc+nybu+h0mBBBAAAEEEEAAAQR6I+DKfWGC7t4cVZZFAAEEEEAgzwRcuaDJs8PC7iKAAAIIIIBAngh4jbJ1DO7dtc2y+nBcVh9qkDX62FtHy/LBPQVUvjWpjwZ9Oq7B9j4NuV/VCu6XNOjequ8PaCvyBv2843GYUCCyfKRPFgzzyaxS0fG7fdp6vK2mejC31+eP6hjdJVrdPVZD7ukSjGrYrYF3IDxO5w/TR6lX5W1jgA/+1gzmnrJuBBBAAAEEEEAAgaEScOW+MEH3UJ0R/A4CCCCAAAIOCrhyQeMgLZuMAAIIIIAAAgh0K9CiIXezZq6bjzXKL3bUyhptWX6gvllqm0431nO3q2WB7gQ0wG5t1TG2m/ZqsL1RA+5tGnRXaMB9SCu4qzXgtvG3NQi38brTpuKQyDgNu+eX+7zK7mnFIkFNuv2D3Dncq9r2hbRVeVQf2rY8UKah93Bv/O5Q9CwNv2fp62k6tvcIzbmtunuQNyjNhJcIIIAAAggggAACbgq4cl+YoNvN84utRgABBBBAYEgEXLmgGRIMfgQBBBBAAAEEEBhigXodl/toPClrtZr7ge01skkD70btV57UAJxpEAS8Ku6EhtlVGmrv14D7Zc2017dVc+u43NJSd9ofDWqGHNNO4TO1mvtybWE+t8wnw7WqOzbgLcxPuxmaYwe1rXlUw+5xGnLPbntELeye2NbO3F+ieXdMq801mWdCAAEEEEAAAQQQQKATAVfuCxN0d3LwmIUAAggggAACbQKuXNBwvBBAAAEEEEAAgVwUsOrtDUcbvUruv+yv13G6m8WqvIm5B+lot1ir8qNayf2ytDSs8lqVS/PhE1Xcjae0Kj95K6xNeUAf42IiC7V9+QKt7J6rj9H6fmgn3Qgdm9vnS7U0H66h9ygJWUvzgnM0+J6p7yd47c6Hdrv4NQQQQAABBBBAAAFXBFy5L0zQ7coZxXYigAACCCCQAQFXLmgyQMNPIoAAAggggAACgyZgFdtazC2vVCfk8b313rjcL1Ul5FijtctmGnCB1iYNsTXk1tbk0rRd25W/oEH3Wq3q3qUtzO2z3rmXaKH0xEKfzCsTuXC0T6YW+ySild4Wgmdk0sptv79AK7onSbjQgu45GnrP0LB7vD7KNRS3JN6CcWtrzoQAAggggAACCCCAgIgr94UJujlbEUAAAQQQQKBLAVcuaLrcAT5AAAEEEEAAAQQcFLD25DU6Dvd6reZ+cEeNrNOxue29zWcaBAEdd7u1aY9WcL+oefdafX5FJHlEQ+56DbltPPTeuYdSLcxL2sbqnqOB98ioTwqGuoV5isoLsANe2O0PlksgPN4LusMF8ySiwXcgPFHDbh3fm1bmKTGeEUAAAQQQQACBvBdw5b4wQXfen6oAIIAAAggg0LWAKxc0Xe8BnyCAAAIIIIAAAu4JVCVaZHt1k6w+1CCP7KqVbVWNXtTK0NwDfCytWlvH3W5t3qtV3Jv1sUnH5N6oIfcBDbl7H3CfvHUTCkSWjdAW5trGfLaO221ht7U3z1Rhd2r7/MFSCWq4HYrN0grvJRp6z9Sxu8dIIDhc25kX6QYydnfKimcEEEAAAQQQQCBfBVy5L0zQna9nKPuNAAIIIIBADwRcuaDpwa6wCAIIIIAAAggg4IzAjpomeXJfvQbdcdl0LC6HGpo1eHVm893Z0GSVVnJXeCF3S3y1SOJV7VJ+XAu447oP/Qcv1rzYxuuer+N0v36sT6YViwT9PtH/ZXayVuaBAn2Uif/E2N1W2R2Kni3B6FSdr+3MmRBAAAEEEEAAAQTyWsCV+8IE3Xl9mrLzCCCAAAIInF7AlQua0+8FnyKAAAIIIIAAAm4INLe0SoO2J998rFF+VVErz2nL8kPxpNRp23KmARRIjcndtFsz7fUadG9sq+a2MboHcApqoB3TsblnajX35Rp0z9HAe3gkgy3MT943n43LHdLq7vFa2b1Qg24duzt2lo7lPVnD7mHi8xfqw/qtM3b3yXS8RwABBBBAAAEEcl3AlfvCBN25fiayfwgggAACCPRDwJULmn7sIl9FAAEEEEAAAQSyRsDG4d5X1yxrNeB+eGetbKlKSLy5RRiae4APkTcmt7Urf0HH5H5ax+R+Oa2Se+B+ywq3A/qfcdrCfIGG3PO1hblVd4/WKu/smHTjdPxuvz92orp7jIQiZ0q4YI6Ei5ZpAH6Gflykj3B2bC5bgQACCCCAAAIIIDBkAq7cFyboHrJTgh9CAAEEEEDAPQFXLmjck2WLEUAAAQQQQACBjgLWKHu/htzrjjbKqoP18pcD9bK7trnjQrzrn0CrerbqeOfN+9oquL1q7nUizTYmd/9blXe1cSXawnxCgU/makfwi0b75Mxin0S00tsqvrNp8gcKdZzuURKMzdQK72US0cA7ENbq7uAIDbtjXvV3Nm0v24IAAggggAACCCAweC5sZLoAAEAASURBVAKu3Bcm6B68c4A1I4AAAggg4LyAKxc0zkOzAwgggAACCCCQ1wIWsWrXctmqFdwPVdTIqkMNskdD7uOJZF67DPjOt9RpyH1IK7i3SGv9U/q8VSRZKa0tDQP+U+krDGnnb2thPqOkbazuOWUiI6M+KbSu4Fk0+XxBDbQj4guUSiA0WsLaxjxSdL4G33O0unuCVn2XZNHWsikIIIAAAggggAACgyngyn1hgu7BPAtYNwIIIIAAAo4LuHJB4zgzm48AAggggAACeS4Q197kVRpqrzsSl/u318hGreq2cbmbLP12eWpp0ZbgTdLalGh72OsWDe8HsXq6cy5z1IeF2okdWs29WcfmXq2V3HsGtZL75G0Zry3Mlwz3yVkadE8u9EmZdgT3ZaCq268/6tfgPaRBeyTsk7BWnIdCOu/EUNw+HbtbNPS2sbojRct13O4FbWN3e2F3mReG6wIn7x7vEUAAAQQQQAABBHJIwJX7wgTdOXTSsSsIIIAAAggMtIArFzQDvd+sDwEEEEAAAQQQGEqBw/GkvKjV3Gu0kvvJffXy6vGEjsvd6lV5D+V2DPRvtTY1SrKuWpI1lZI8fkRa9HVLU1wD56Fuyd6iObeG7F4190siTbs1gD+iZfT1ustD98cEVsFtldwTNfCersXRwyM+bwzvoQ67g9ozPaq/XaIt1EfouOHDyvxSau3UdV7b5NMA3q+BdoG2LR+ugbeO2124SMfuXqDtzBeIX6u9U8udeMETAggggAACCCCAQI4JuHJfmKA7x048dgcBBBBAAIGBFHDlgmYg95l1IYAAAggggAACQyVgBdsWaG+vbpKn9td7QfeWyoQcahjqIHhg97i1WUPlxgYNtw9L4kCF+KqPSLQ5LuGWhARamsXfqsHzkE4ngu7kMQ25d2rIXaW/bm3hhy7ktt21gumg/qdcK7knnajoturqoQ66/b4WCQSaJRJqlFikXoPupIwfE5CRWm1equ3VLQRP3yYbozsUmy2RwoUaeC+VUGSaN263P1Ckhd1a/U11tx1eJgQQQAABBBBAIKcEXLkvTNCdU6cdO4MAAggggMDACrhyQTOwe83aEEAAAQQQQACBoRGw1uQNza2y4VhcHtCW5c9r6/KqxhadN9RB8MDur1VuJw/vlsaKzVK/+RkpjB+XKZMmyugRw6S4IKYBq/bKHtLJAm19tCa0irtOn5va3g/pNrT9mF+Lpm3M7gKt7g6nWoUP8XYktNK+Xo/R0SMHZM+eHRIJVsvsGSGZMysgc2cHZMworTTXDW0Pu31hb3zuYHi8hKIzJKRV3dFiHbs7coYuE9GcO8sGGx9iT34OAQQQQAABBBDIRQFX7gsTdOfi2cc+IYAAAgggMEACrlzQDNDushoEEEAAAQQQQGBIBap1HO49tU2y5nBcHqqolS2VjV678pYhH8N6gHb7xJjczYf3SONLz0lw71Ypr94rE4qjMnP2bBk3bpy2yC7ScaG1pJkpYwKNjXGpqamWvXv3yosvbpXjVbukMHJIJo5t0KDbL9POCMjEcdrOvMSvgbfm2Cc6mvv8hRIMj9GwW6u7iy/UKu+5+n68VneX60JhXY7AO2MHlR9GAAEEEEAAAQQGWMCV+8IE3QN84FkdAggggAACuSTgygVNLpmzLwgggAACCCCQPwK7NeReebBBVh+Ke9Xc++qavWbarY4G3akxuRu3b5Tapx+SyYEGuXLFBbJ4/lyZOHGSlJeXSSgYFL//RClz/hzqrNrTFv2DhGZtL19ZWSm7du2WDevXyB//+LDs3/uS/iGCyLyzgvKmK8Jy9oygN2530LqT26RBtlVwB0I6bnd4goRj8zTwfp3X1tzG8rYxvZkQQAABBBBAAAEEckPAlfvCBN25cb6xFwgggAACCAyKgCsXNIOy86wUAQQQQAABBBAYJIGkdtFu1P+8qBXcv9lVK88dbpC9GnJXJxxvWV5fI01H90vy1XWSXPuoLBhVLHfcfpucd955MmzYMInFYoMkymr7ItDQ0CDHjh2TlStXyn333SerVq2S2tpamTo5KW+7KiLLF4dk8vi2yu709ft8IQ21Y1rZPdWr7A4XLNQ25tM1/B7bFnbr50wIIIAAAggggAACbgu4cl+YoNvt84ytRwABBBBAYFAFXLmgGVQEVo4AAggggAACCAywQL2Oy324oVkD7rj8qqJGXtDAu17H5XZ8aG5JHj8qjbu3SfTgyzLq6Kuy7Myx8s7rr5P58+ZJWNuVBwKp0uABBmV1fRJIJpOSSCRkw4YN8pOf/ET+/Oc/y44dOyTgOy6zpwc06A7KFReHZdqUgFZyn+hfrr/k81lFfkDH7S4Wf2ikVnSfrWN2X+RVeAcjk8QXKO3T9vAlBBBAAAEEEEAAgewRcOW+MEF39pwzbAkCCCCAAAJZJ+DKBU3WwbFBCCCAAAIIIIDAaQQONyRls4bbq7Rt+RP76mV7deI0S7vzUfOxAxJ/ZYOUVe2WmcE6OW/WFHnzNVfLzBkzvJ2Ix+NSVVUl9syUOYFoNCplZWVizzZt27ZNHnroIXnyySdl3bp1UqnHsbTEJ8sWBeXWd0Rl4ZygjtXt04D75G3WGRp6B8OTJFq0XMKFizX0nqfV3ZN1dpE+GIv9ZDHeI4AAAggggAACrgi4cl+YoNuVM4rtRAABBBBAIAMCrlzQZICGn0QAAQQQQAABBPos8MrxhPx2d52sOtQgL+vro/Fkn9eVTV9sPrJPGratkRG1+2VhqV/OP2uqXHbZpTL1zDO9zTxw4IAXpNozU+YExowZIwsXLhR7tmn79u3y2GOPyVNPPSVPP/207N27SyJhnyxZGJQ7b4rK0gUhCYd8WpHf2Tb7tLK7QB/DNeSepZXdF4rXyjw6Q/zBYZ19gXkIIIAAAggggAACDgi4cl+YoNuBk4lNRAABBBBAIFMCrlzQZMqH30UAAQQQQAABBHorUNvUotXcCfnZq9WyVluXVzYmpcH1nuUnEJoO75GGF1fJ6PqDsnRkVM4/e5pcfPHFXqBqldxbt271qob379/foaK4t4Ys3zeBxsZGr6LeAu4VK1bIrFmzvONgf3hgIbdVdNtj584KL9RePD8od9wY8yq7C2M+CWnY3dVkrc0DobESKlgkkaLFEtHq7kD4DA27y7QSPNLV15iPAAIIIIAAAgggkKUCrtwXJujO0hOIzUIAAQQQQCAbBFy5oMkGK7YBAQQQQAABBBDoicCr1U1e2/KfvlItm441SiLZIsnWnnwz+5fpKui2Ftk2DvSzzz7rBakNDQ2yYMECGTt2bPbvVA5toQXa69ev91qWX3TRRbJs2TKvsttayacH3bt2VUgoKLJwblBufrtWdC8Mea3Mo5Gug25j8vmjWtldLqHoNAkXLdOw+xyvlXkgNDqHFNkVBBBAAAEEEEAgPwRcuS9M0J0f5yN7iQACCCCAQJ8EXLmg6dPO8SUEEEAAAQQQQCADAn/YUy8vHW+Uh3fVyktVuTE2d4qxq6DbPrcg1YLuVNBqFcVTpkxJfZXnIRCoqKjw/tDAgu358+d7QbdV3NuUHnTvtqA7JLJAx+a+8dq2oLu8zCex6OmD7tQuBEIjNew+S8fsturuc/V1Wxtznz+mi/RsHal18YwAAggggAACCCCQGQFX7gsTdGfm/OBXEUAAAQQQcELAlQsaJzDZSAQQQAABBBBAQAW+uvGY7K1vljU6PvfeuuacMuku6LbW5ZWVlV4rc4LuoT/0FnRboG2V3aWlpTJz5kyvtbxtSVdB901vaxuju7zUp5XgPQupff6IVnaXSDAyxRuvO6yV3ZHCpdrKfMKJne7ZeoZeiF9EAAEEEEAAAQQQSAm4cl+YoDt1xHhGAAEEEEAAgVMEXLmgOWXDmYEAAggggAACCGSpwEdWHpKjOi73S1WNcjSezNKt7NtmdRd0W9Da0tLiVXJbJTEV3X1z7uu3UkH3ycfB1tdZ0L1QK7rf1YvW5Sdvlz9obczP1Pbl8yVWskKCsdkSCI7UFueFuihh98levEcAAQQQQAABBLJJwJX7wgTd2XTWsC0IIIAAAghkmYArFzRZxsbmIIAAAggggAACXQrc/MR+qU4kZU9dkz63dLmcix8QdGf3UetL0H3zdSfG6C72SaSbMbpP3nufLyy+QKEEtZI7FJujrcwXS7T4An0/xQb01sUJu0824z0CCCCAAAIIIJAtAq7cFybozpYzhu1AAAEEEEAgCwVcuaDJQjo2CQEEEEAAAQQQ6FTg6kf3SH1zi1fNbc+5NBF0Z/fR7GnQvevEGN2LtKL7luvbgu4SC7rDfQumfYFiDbfHS9gqu0svl1DBXK3sHuWF4ITd2X3OsHUIIIAAAgggkL8CrtwXztqgOwWYv6cQe44AAggggIDI8OHDMsqQ+v/Hmd6OjCLw4wgggAACCCCAwAAKvO5XuyTR0io1TUlJJFsHcM2ZXxVBd+aPwem2oFdBd1Bk0dyg3PqOtqC7uKgfQbcvJOKPatg9USIFC7Sye6lErLI7MvnE5vYtQD/dvvIZAggggAACCCCAQP8EXLkvTNDdv+PMtxFAAAEEEBhUgUwHzK5c0AzqQWDlCCCAAAIIIIDAAAosebBCmjXojms1d3Nu5dxC0D2AJ8ogrKovQfe73xmVZQtDUlTok3AfK7pTu+IPlHhhd7hwoVZ2v0Hbmc8Vf3C4djGPpRbhGQEEEEAAAQQQQCBLBFy5L5y1QXeWHEc2AwEEEEAAgbwWcOWCJq8PEjuPAAIIIIAAAk4JnHP/Dg24W7WqW7zA26mN72ZjCbq7Acrwx70JuoMBkXPmBeW2G2IadAel0ILuUP8qr31a2W2hdjAyRSJFy9oqu4uWSiA0PsMy/DwCCCCAAAIIIIDAyQKu3Bcm6D75yPEeAQQQQAABBNoFXLmgad9gXiCAAAIIIIAAAlkucN4vKrzW5fVazt2kld25NBF0Z/fR7EvQfceNMVm6SIPumE9C/Qy6Uzr+4DANu8+UaNFiiZS8XkLRs7Syu0x8vkhqEZ4RQAABBBBAAAEEMizgyn1hgu4Mnyj8PAIIIIAAAtks4MoFTTYbsm0IIIAAAggggEC6wBUP75IGHZu7sjEpDTnWu5ygO/1IZ9/r3gTdAa3oXqwV3XfepBXdGnTHLOgO9q+iu13EFxZ/oEjD7qkSLblAwgVL9DFfK7tHty/CCwQQQAABBBBAAIHMCrhyX5igO7PnCb+OAAIIIIBAVgu4ckGT1YhsHAIIIIAAAgggkCbwzj/slZqmFtlf3yw11r88hyaC7uw+mD0OundWiBd0LwjKXamgO+qT4EAF3SeYAqGROk73WRpyL5Zo8UX6eqb4AsVa2R3Obki2DgEEEEAAAQQQyAMBV+4LE3TnwcnILiKAAAIIINBXAVcuaPq6f3wPAQQQQAABBBAYaoH3/+WgV8396vGE9zzUvz+Yv0fQPZi6/V93T4PunSeC7iUadL/n5rYxuqORgQ+6ff6IVnaXaOvymVrZfbGO2b1YX8/SNubD+7+zrAEBBBBAAAEEEECgXwKu3Bcm6O7XYebLCCCAAAII5LaAKxc0uX0U2DsEEEAAAQQQyCWBz687KgcakrLuSFz21zXl0q4JQXd2H86+BN1332Kty0MSCWvQre3MB2MKhsdqVfdCiRQtlXDRed743T5/AZXdg4HNOhFAAAEEEEAAgR4KuHJfmKC7hweUxRBAAAEEEMhHAVcuaPLx2LDPCCCAAAIIIOCmwC8ramV7dZP8fk+tWFV3Lk0E3dl9NHsTdPv9IksXBuV9t7YF3eGQz2tnPhh76PNHJRAs19blcyRWermEChZp2D1Zq73LBuPnWCcCCCCAAAIIIIBADwRcuS9M0N2Dg8kiCCCAAAII5KuAKxc0+Xp82G8EEEAAAQQQcE/ghWONsqUyIQ9srxZ73dzSKrkyUjdBd3afj30Jut//7ragO2RBt4bfgzX5fLr+8ESt6j5PH+d61d2B8CT9uaBWdg9SKflg7QzrRQABBBBAAAEEckDAlfvCBN05cLKxCwgggAACCAyWgCsXNIO1/6wXAQQQQAABBBAYaIHjiRbZpAH3/718XJ7T9uU1+j6hYXcuTATd2X0UexN0a+6sLcuD8gELus8JSSjoE6vyHszJ5y+UYHiMtjE/RwrK36Jjdi8QX6BUg+7IYP4s60YAAQQQQAABBBDoRMCV+8IE3Z0cPGYhgAACCCCAQJuAKxc0HC8EEEAAAQQQQMAlgW1VCflVRY2sPhSXnTVNUpVIurT5XW4rQXeXNFnxQa+Dbm1d/sHbY7Jcg+5AYPCDbhG/htpBbWE+UwrKrtLxupdrC/MZEgiNUD9N3r1HVlCyEQgggAACCCCAQM4LuHJfmKA7509FdhABBBBAAIG+C7hyQdP3PeSbCCCAAAIIIIDA0Avsq2uWdUcbZdXBBvnLgXrZXds09BsxCL9I0D0IqAO4yp4G3bZcqqL7Q3e8FnTbvMGd2sJsC7ZD0enavny5REuv0uB7lheAWxDOhAACCCCAAAIIIDA0Aq7cFyboHprzgV9BAAEEEEDASQFXLmicxGWjEUAAAQQQQCBvBaq1XfleDbvXHm6QR3bV6ZjdjdKYbJVkq9stzAm6s/uU7n3QHZJ77rSgO6hty31e+D0Ue+jzR8UfKNEW5gukYNhbtYX5YvEHR+o2FA7Fz/MbCCCAAAIIIIAAAirgyn1hgm5OVwQQQAABBBDoUsCVC5oud4APEEAAAQQQQACBLBRobhGp1/+8oGN1/1JbmD+vY3Ufbkh687Jwc3u8SQTdPabKyIK9CbptA61l+Ufe01bRPfjV3K+R+HwB7VIe0vG6J0uk+AKvsjtStFRbmI9/bSFeIYAAAggggAACCAyqgCv3hQm6B/U0YOUIIIAAAgi4LeDKBY3bymw9AggggAACCOSrwPbqhDyxr94bq3uzVnVb2N3qcFU3QXfbmRyPx6WqqkpaWlqkqKhIotGoBINWFZ3Z1tt9Crrvjsm5GnhnYvIHh2kL82le0B0ru1Jfz9aq8oiG4MFMbA6/iQACCCCAAAII5JWAK/eFCbrz6rRkZxFAAAEEEOidgCsXNL3bK5ZGAAEEEEAAAQSyQ6CyMSmvati9+lBcW5jXysvHm8Sal7sadhN0tx27AwcOyPr16yWRSMj06dNlzJgxXuAdDoczeuK5FnT7/BFtYV7U1sK8/G3awnyJVnWPEh8tzDN6HvHjCCCAAAIIIJAfAq7cFybozo/zkb1EAAEEEECgTwKuXND0aef4EgIIIIAAAgggkGEBG5e7OpGU9Ucb5YEdNbJeW5jX6PjdiRY3x+rO96C7vr5exzI8Klu3bpU//elPkkwmZdmyZTJr1iwZN26cFBcXZ/SM623Qfe7i11qXZ2rDfdozPRidKtGSyyVafL6EYvM07B6jm+PL1CbxuwgggAACCCCAQF4IuHJfmKA7L05HdhIBBBBAAIG+CbhyQdO3veNbCCCAAAIIIIBAZgWS2qbcxut+Wau6H9tTJ2u1svsVfX0snszshvXx1/M56LYq/D179sgzzzwjzz77rKxevVpCoZBccsklsnz5cpk3b56MHj26j7ID87XeBN02Jre1LP/wiTG6B2YL+raWgLYwD0bP1Bbm50tB+bUadp+lK7Kgm7C7b6J8CwEEEEAAAQQQ6F7AlfvCBN3dH0uWQAABBBBAIG8FXLmgydsDxI4jgAACCCCAQE4IHGholnVazb36UIOsPNAgu2qbJalV3a7Vdedr0J2q5N60aZM89thjsnHjRjl48KDXsvyyyy7zgu6zzjpLRo4cmdHztadB986dFToWtshyC7rvinnPmdxwr4W5X1uYFy6WwhHvkog++/wl+tDxupkQQAABBBBAAAEEBkXAlfvCBN2DcvhZKQIIIIAAArkh4MoFTW5osxcIIIAAAgggkK8C9VrWfUSruNcejsuD22tk07G4ti8XrfZ2K+rOx6A7vZLbqrlXrlwpdXV1MnXqVFm0aJFX0W2ty0tKSiQSyWww25ug2+9vC7rvsaB7USjD/6fp11A7JKHoLImVXa2V3cu1wnuGBILDM7xd/DwCCCCAAAIIIJC7Aq7cFybozt1zkD1DAAEEEECg3wKuXND0e0dZAQIIIIAAAgggkEEBy7Ob9D+bKxu9oNsC7wP1zVJnfc0dmvIt6O6qktsqty+66CKvknvBggVeZXc2HMbeBt02Rvc9d8ZkWcaD7ja9QHi8VnMvlUjxeTpe90USjEzRD2hhng3nFtuAAAIIIIAAArkn4Mp9YYLu3Dv32CMEEEAAAQQGTMCVC5oB22FWhAACCCCAAAIIZEDA6rZ1iGdtWd4kqw42aAvzuFZ3N8g+DbtdmvIp6D5dJffChQtlxYoVYpXcZWVlEo1Gs+Iw9iboDgTaxuj+UBYF3T5tXx4Mj5OwVnQXDb9BQgXztcV6UG21/JwJAQQQQAABBBBAYEAFXLkvTNA9oIedlSGAAAIIIJBbAq5c0OSWOnuDAAIIIIAAAvkqUNXYIhU1TbJGQ+7f7a6Vl443SSLZIklHOpjnS9DtWiV36v+eehp079pVIamg+6/uyJ6KbtFQ2+eLaOvyc6Rw+C36vEz8gXJta54df0iQcuYZAQQQQAABBBDIBQFX7gsTdOfC2cY+IIAAAgggMEgCrlzQDNLus1oEEEAAAQQQQGBIBRKaaNc1t8pGHaPbxupedyQuVTpYd4MjLczzIeh2sZI7dRL3JugOaqH0ueeExILupQszPUZ3ag+0TbkvIOHYibG6C8+VkL72M1Z3CohnBBBAAAEEEEBgwARcuS9M0D1gh5wVIYAAAgggkHsCrlzQ5J48e4QAAggggAAC+SzwyvGE/HZ3nbYwb9Cq7oQcjSed4HAl6LawOplMSm1trRw+fFjbxrfKsGHDpLCwUMLhsFYza9/uTiZXK7lTu9KboDukQffyrAu62/YkGJ6g1dzLdazu8/X5dTpW9+TULvKMAAIIIIAAAgggMEACrtwXJugeoAPOahBAAAEEEMhFAVcuaHLRnn1CAAEEEEAAgfwVOKLB9raqhDyrQfdje+pke3WTF8Zmu4grQXdzc7M0NjbKK6+8Ik899ZQ0NTXJ0qVLZerUqVJeXi6xWOwUapcruVM706ugW4u4raL7g7dnU0V32574g2U6VvckDbovkMJh79Cq7rNTu8gzAggggAACCCCAwAAJuHJfmKB7gA44q0EAAQQQQCAXBVy5oMlFe/YJAQQQQAABBPJXwFqVH9Pxup/X1uW/2FEjm441Sl1TizS1ZPdg3a4E3YlEQurq6uT555+Xn/70p3Ls2DFZsmSJzJs3T2bPni2jR4/uUNnteiV36v+Sehp079YxukMWdC8OyQduy76g2+eP6NjcJV5Vd9GI2yRcuFhbmod1/G4tQ2dCAAEEEEAAAQQQGBABV+4LE3QPyOFmJQgggAACCOSmgCsXNLmpz14hgAACCCCAQL4KJDXQTuhjq1Z1P6otzJ87HJddtU1S1ZjdLcxdCbqtmru6ulpWr14tP/rRj2Tbtm1SXFzsBd1XX321zJ8/v72yOxcquVP/d9TjoHt3hYRDPg26g17QvWRBtozR3bYnPh2nWzTUDhcslKIR75Jw0bkSCI4Un78gtas8I4AAAggggAACCPRTwJX7wgTd/TzQfB0BBBBAAIFcFnDlgiaXjwH7hgACCCCAAAL5K7C3rllWaftyG6t79aG47NWw22q6s7Wu25WgO1XR/cILL8ivf/1rWbt2rezZs0dGjBghl19+uSxbtsyr7Lbwu6amRrZs2SKPPfaYbNy4UQ4ePCgjR46Uiy66SJYvXy4LFiyQMWPGOHGS9ibojoTbgu73vzsm2RZ0p7BDsdnauvytWtF9noSi08UfHJb6iGcEEEAAAQQQQACBfgq4cl+YoLufB5qvI4AAAgggkMsCrlzQ5PIxYN8QQAABBBBAIH8FarRd+YH6pBd0/6qiRjZrC/NmrfRuyVISV4LulpYWsXG6jxw54o3TvWrVKnn00Udl7969Mnz4cC+8vuaaa2TYsGGydetWWb9+vaxcudJrd27jeC9cuFBWrFghs2bNkrKyMolGo1l6RDpuVk+D7j1a0R2J+GT5OUHJ5qA7GJki0eKLdKzu8zXsXqrjdk/ouMO8QwABBBBAAAEEEOizgCv3hQm6+3yI+SICCCCAAAK5L+DKBU3uHwn2EAEEEEAAAQTyUcBC7bg+Nh1tlAe318hzRxrkaDwp9c3ZWdPtStCdOpessru2tlassvs3v/lNe2W3hd2XXnqp187cgu6dO3c6Xcmd2t8eB917KiR6Iuh+363ZW9EdCOlY6rF5XtAdK71CgtFpqV3lGQEEEEAAAQQQQKCfAq7cFybo7ueB5usIIIAAAgjksoArFzS5fAzYNwQQQAABBBDIX4GWVq3e1ky7oqZJ/nygXtZo+/KNWtV9sL45K1FcC7q7quzetWuXlJSUiI3Pffz4cSkoKBCXK7lTJ0tvgu5YtK2i24LuxfOza4zu1P74A6VaxT3RC7oLh98godic1Ec8I4AAAggggAACCPRTwJX7wgTd/TzQfB0BBBBAAIFcFnDlgiaXjwH7hgACCCCAAAIIHG1MyktVCVlzOC6P762TV/R1NrYwdy3oTp1ZJ1d2Wyvz7du3e63KLeSePn26k2Nyp/Yv9dzToHuvVnTHYjpGt7Yuv/uW7A26fb6IjstdJpGi86V41F3avnyR7mogtbs8I4AAAggggAACCPRDwJX7wgTd/TjIfBUBBBBAAIFcF3DlgibXjwP7hwACCCCAAAL5LdCoZd3ViRZZry3M799eLes18K7V8bsTVu6dRZOrQffJld3PPvusPPzww94Y3tOmTXN2TO6TT40eB917K6QgFXTfHJNzsrSi2+ezUDuo43SfJ0WjPyjRoqUivtjJu817BBBAAAEEEEAAgT4IuHJfmKC7DweXryCAAAIIIJAvAq5c0OTL8WA/EUAAAQQQQCA/BSzO1i7asu14Qh7eWSurtIV5RU1CqrTSO5smV4PulGGqsnvz5s3yyCOPyLFjx2TGjBly9tlny4IFC2TMmDGpRZ187k3QXVjQVtH9Hgu652Vn6/LUQYhowF2iQXe4cIlWeI9MzeYZAQQQQAABBBBAoB8CrtwXJujux0HmqwgggAACCOS6gCsXNLl+HNg/BBBAAAEEEEDAwm4bm3vTsYQG3Q3y1L56L+zOJhnXg+5UZXdlZaXYON0WfA8bNkzKy8ulrKxMotFoNnH3elt6GnTv04puL+heHJS73pX9QXe4YKEUjbxdItq6PBid2WsXvoAAAggggAACCCBwqoAr94UJuk89dsxBAAEEEEAAgRMCrlzQcMAQQAABBBBAAIF8EKhrbvXC7ueOxOWhihrZXNkoDTrPxuvOhsn1oDtlmEwmvZDb3ofDYQkEcmPc594E3cWFPlm+OCR33RSTRfOCKZqsfA7F5kjR8BslXDBPwkXLs3Ib2SgEEEAAAQQQQMA1AVfuCxN0u3Zmsb0IIIAAAggMoYArFzRDSMJPIYAAAggggAACGRNIap4d1/9sPhaXX2sL8+c18N6vVd41On53Nky5EnS3ap94q+62ye/3i8/nywbefm9DT4Pu/fsqJBV032lB99xsD7pnS0H5WyQUnS2xsiv77cQKEEAAAQQQQAABBERcuS9M0M3ZigACCCCAAAJdCrhyQdPlDvABAggggAACCCCQgwK7apvlzwfqZbW2MF9vYXdds2RDTXeuBN05eMp4u9SboLukWCu6zwnJHTc6EHRHZ2jAfZUG3TOkYNh1uXr42C8EEEAAAQQQQGBIBVy5L0zQPaSnBT+GAAIIIICAWwKuXNC4pcrWIoAAAggggAAC/ROoSiTFwu41h6yyu0a2VSUkqe3LMx12E3T377gO9rd7HHTvr5DSVNB9Q0wWZnlFdzA6TWKlb5RQZJoUjrhpsBlZPwIIIIAAAgggkBcCrtwXJujOi9ORnUQAAQQQQKBvAq5c0PRt7/gWAggggAACCCDgpkBC25fXNbfIxmMJeWB7tazTqu6qxqTX1jyTe0TQnUn97n+7p0H3gf0adJe0VXTfbkH3nOxuXR6MTNWg+w1a0T1Vg+5bu4dgCQQQQAABBBBAAIFuBVy5L0zQ3e2hZAEEEEAAAQTyV8CVC5r8PULsOQIIIIAAAgjko4AWb0uz/md7TZP8cV9bC/OtlY1yJJ7MKAdBd0b5u/3x3gTdZRZ0Lw7Jbe/M/qDbAu5oyeUngu53d+vAAggggAACCCCAAALdC7hyX5igu/tjyRIIIIAAAgjkrYArFzR5e4DYcQQQQAABBBDIa4FDDc3yglZ1rzncIE9p4L2jJiEWgtsjExNBdybUe/6bvQm6y0tPBN3viMmCLK/oJuju+TnAkggggAACCCCAQE8FXLkvTNDd0yPKcggggAACCOShgCsXNHl4aNhlBBBAAAEEEEDAa1V+TFuWP3c4Lg9ur9FW5nGp15bm+r+MTATdGWHv8Y/2NOg+eKBCUkH3uy3oPjv7W5cXlF0hQWtdPvyWHnuwIAIIIIAAAggggEDXAq7cFybo7voY8gkCCCCAAAJ5L+DKBU3eHygAEEAAAQQQQCAvBaxyW4frlherGuVXFTWy5lCD7K1rlupEZpJugu7sPg17HnTvlGHlNkZ3UG693oGgOzpdCsquklBshhSUX5/dB4GtQwABBBBAAAEEHBFw5b4wQbcjJxSbiQACCCCAQCYEXLmgyYQNv4kAAggggAACCGRaoFVDbutSvq++yavqXqVB9zMHG2RPbXNGNo2gOyPsPf7RHgfdB3fKcA26zz0nJLdcF5X5WV7RHYrNlsJhb9MxumdJtPSKHnuwIAIIIIAAAggggEDXAq7cFybo7voY8gkCCCCAAAJ5L+DKBU3eHygAEEAAAQQQQCCvBaqbWmSPVnI/p2N1P7yzVl6sTEijlnonLQkfwomgewix+/BTPQ26D2nQPWKYVXSH5GYLus/K7tblodhcKRpxo1Z0z5FI0bl9kOErCCCAAAIIIIAAAicLuHJfmKD75CPHewQQQAABBBBoF3DlgqZ9g3mBAAIIIIAAAgjkoUCz5tk2Nvemo3H5ZUWtPK9jdh+JJ715Q8lB0D2U2r3/rd4E3SOHnwi63x6VeVkedIcLFkjRyNslXLhQq7pn9x6GbyCAAAIIIIAAAgicIuDKfWGC7lMOHTMQQAABBBBAICXgygVNant5RgABBBBAAAEE8llgR3WTPL63TqyF+ZbKRjkcb5HWIazqJujO7rOvp0H34UM7xYJua11+09uyOej2ifj8EilcIsWj7tag+xwJhMZl90Fg6xBAAAEEEEAAAUcEXLkvTNDtyAnFZiKAAAIIIJAJAVcuaDJhw28igAACCCCAAALZJnBMq7hf1bB7tbYwf3RXnbx8POGN4T1UYTdBd7adER23pzdB9+iRbRXdN701KnNnZ2nrcl9AfL6Q1668ZPQHJVy0RN8XdNxp3iGAAAIIIIAAAgj0ScCV+8IE3X06vHwJAQQQQACB/BBw5YImP44Ge4kAAggggAACCJxeIJ5skepEi6w/2igP7KiRDUcapSaRlETL0IzVTdB9+uOT6U97GnQfObxTLOi2iu4brs3eoNvnj4g/WKZB9/lSPPI9XkW3lnhnmpnfRwABBBBAAAEEckLAlfvCBN05cbqxEwgggAACCAyOgCsXNIOz96wVAQQQQAABBBBwSyCpbcqbNNR++XiTPLanTtbqWN2vaFV3ZWNySHaEoHtImPv8I70JuseOaqvotqB7zqzsrOi2kDsYniSR4gukoPx6CRecrTYE3X0+QfgiAggggAACCCCQJuDKfWGC7rSDxksEEEAAAQQQ6CjgygVNx63mHQIIIIAAAgggkN8C++ub5fkjcVmjQffK/fWyu65ZkhqAD3ZdN0F3dp93PQ26j2pF99jRWtG9OCTveHP2Bt2B8BgNtxfoGN3nSazsDRKMTM3uA8DWIYAAAggggAACDgm4cl+YoNuhk4pNRQABBBBAYKgFXLmgGWoXfg8BBBBAAAEEEMhmgbqmFjms43VbRfcvtIX5C8cavfblzYPcwpygO5vPCpEeB91Hdsq40f4TQXdEzp6ZnRXdwcgZEi1Z4bUujxQukUB4fHYfALYOAQQQQAABBBBwSMCV+8IE3Q6dVGwqAggggAACQy3gygXNULvwewgggAACCCCAQDYLWJ5t43Jv1oD7VztrZe2hBrEq71oNwAdzIugeTN3+r7unQfcxDbrHj/XLch2j+/o3ZWPQ7ROfzy/B6GwpHHatjs19noSi03S87vL+I7EGBBBAAAEEEEAAAU/AlfvCBN2csAgggAACCCDQpYArFzRd7gAfIIAAAggggAACeShgLcp1uG7Zq+H2c1rVvfpggzyrYffu2qZB1SDoHlTefq+8x0H30Z0y8UTQfd01ETkryyq6fb6ADsUd1ID7HCkacbtWdC8Xf6BMfP5ov41YAQIIIIAAAggggECbgCv3hQm6OWMRQAABBBBAoEsBVy5outwBPkAAAQQQQAABBPJYoForuPfUNss6Ha/7sT218mJlwqvqtmrvwZi6C7q3bt0qlZWVMmbMGFmxYoVMmTJlMDaDdXYhkAq69+/fL6WlpTJr1iy5+OKLvaWfeuopefLJJ71HpQXd47V1uVZ0v+1qDbpnZFfrcr8/ptXbw7yAu2jkbTpO9yIv+Bbxd7HnzEYAAQQQQAABBBDorYAr94WdCrpbWlpkw4aN8sQfn5S1a9fKvn37xKCj0aiMHz9epk49Uy59/SXyutddKEVFRb09ZiyPAAIIIIAAAicJuHJBc9Jm8xYBBBBAAAEEEEBABZo1z65vbpFXjifkj/vqZZ1Wd7+sr481JgfFp7ug+9lnn5X169dLJBLxgu7JkycPynaw0s4Fdu7cKRZoNzQ0yPz582X58uWdB93HdsqkVNB9VURmZ1nQHQiN0Fbls7yxuQvKr9YW5jN1h30nHp3vO3MRQAABBBBAAAEEeifgyn1hZ4LuzZu3yL33/qOsfe65bo9EYWGh3PvZz8hb3/oW8fv5a85uwVgAAQQQQACBLgRcuaDpYvOZjQACCCCAAAIIIKACBxuaZePRRlmnjzXawnxnTZPUaQCeSA5sZXdXQbcVKGzYsEEs6Laq4Xg87gWto0eP5vgMocDBgwe942DH46KLLpJly5bJwoULvePRoaJbg+7JE9oqut9qQff0bKro9kkoMkUixRdJtPgCbV++RALh8UOoyE8hgAACCCCAAAL5IeDKfWEngu4nnvij3HHne3p95txyy83yD/d+ptff4wsIIIAAAggg0CbgygUNxwsBBBBAAAEEEECga4EGDbWrEi1azd0kTx+olw0aeG+vTkjlAFd2dxV0W6vyqqoqsdblFqgeOHBAysrKvA59XW81nwy0gP2BgR0HOx4WdFvrcjsOdjzSg+4qDbqnTAzI8sVBufaN2RR0+8Tn80soNkcKh7/Tq+gOhieIL1Ay0FSsDwEEEEAAAQQQyHsBV+4LZ33QbZXcV1/z5j6fUP/wD5+VW25+V5+/zxcRQAABBBDIZwFXLmjy+Rix7wgggAACCCCAQHcCqbrt/XXNGnLHZdOxhGyubJTdtU1e2B3Xyu6kjtudWq679XX1eVdBd2osbgtUrbLbnpkyJ2BBt7Uut2ebUmN3p8borqrcKWdMCugY3UF5iwbds6ZlR0W3zxfRsbnLJKJV3IXDb5Jw0WLt5KhDF/pCmcPklxFAAAEEEEAAgRwVcOW+cNYH3Xfedbc8/vgTnZ4mV111pYwePUrWrVvvPTpbyNqYr1n9jMRisc4+Zh4CCCCAAAIInEbAlQua0+wCHyGAAAIIIIAAAgiogIXYjRpoWxX3Xg28t1Y1ypbKhGw4Epe99c3eZ80advdn6i7oTlUU2zNT5gSsdXl6Rf3JQfdxDbrPnKwV3Rp0v/mK7Am6/cEREo6dpW3Lz5NY6RU6TreOze0LKCTDFmbubOKXEUAAAQQQQCBXBVy5L5zVQffx48dlwcLFp5wjc+acLf9933/J8OHD2z+zZb/3vf+Sb37rP9vnpV7c94Pvy4oVF6XeZuS5sbFRIpFIt7/d0+W6XZEu0NN1JRIJCYVC2v7J15PVdrpMT3+r0y93MrO+vkHCkbAEA/YPFiYEEEAAgUwJuHJBkykffhcBBBBAAAEEEHBRoLqpRfZo2P2Sht3WxrxCx+yujCelVluc1+tnjS0iFno36cOy7xaNyVt7kIE3HdotDZtXSom2vp4ZbZLF0ybp/ZgVkqrodtEqH7Y5FXTbGOrr16+XxvhBmapB97nauvxNb4jIzKmZrei2duUiAQlGztSA+/XasvxcCRcsFH+orSI9H44R+4gAAggggAACCAy1gCv3hbM66O6qbflP/u9/ZdmypZ0e02ve9BZ54YXNHT6797N/L7feeos378UXt8onPvk3HT6PRqLy85//pMM8e9PQ0CDXv+OGU+Z/77vfbm/v9PP7H5Af/eh/OizzkQ/fI5dccrFXif6d735P1qxZ631u1eU2/wPvf6/MnKl/dapTS0uL/P6xP8j//OjHsmHjRqmrq/MC/Llz58idd9wu559/nrdc+n8624dLX/96ueeev/LGu/rGN/9TnnzyKW9d9r3ly5fJXXfdKZdcvKJ9NRv1t37wg/+Wvzy9Uo4ePerNX3zOOXLNm66Wm268QQLdBMxm/OMf/69s3rJFduyo8H7L9m/hwgUyc8YMOU+3O/332n9YX3Rm9q6bbpS3ve2tYl733ffD9m36wuf/Wa644g3yrptvTV+F9/r973uvvPGNV5wy32Z87p/+WVavXtPhs/nz58k/fe4fO8zjDQIIIIDA6QVcuaA5/V7wKQIIIIAAAggggEC6QLOG1vUaatfouN3HtML7YH1SdtU1ea3Md2nofVhDb/uszoJvfVjgbTl3d2F30wG9P7D+KWndsUGiR3bKtFFlXtA9efLk9J/ndZYJ7Ny50xuj28ZQtzG8C6KNMu2MtjG6r7ksG4JuLc4IxDTcXtQ2NnfhMm1hPlx8/oIsk2RzEEAAAQQQQACB3BFw5b5wVgfdFlS+4503nnJW/OiH98mFF15wynyb8dzzz8urr27v8Nn8eXPbg2X7/O1vf0eHz+3Nju0vnzLPQuc5cxecMv+pJ5+QSZMmevO//e3vyhf/9Usdlvn0pz8l48aOlQ988EMd5qe/+fGPfyhLlyyRT/7N38ovfvHL9I86vL7j9tvk7/7ubzvM62wfLFT+zGf+TlZcfGmHZdPffPzjH9WQ/X3yPxpQf+Yz96Z/1OG1Bd4//OEPpKDg1H8wVFdXyz0f/qgXpHf4Uidvrr32LWJ/ZFBSUtLh087MLLQuLCqUL33pyx2WTY2x/ua3vE0snE+frrzyjfLNb3w9fZb3uqmpSWbMPOuU+R/84AfkYx/98CnzmYEAAggg0LWAKxc0Xe8BnyCAAAIIIIAAAgh0JZAKr49rqL1Hg+49Omb3bq30PtygQbdWdtfZQ4PuhFV2nwi709fV3Nws1iXO7p8cP14l1btelZrNqyS+c4vIkT0yujjmjQU9evTo9K/xOssE9u7dK6tWrZI9e/Z4Wza83CfTz7TW5SG5WoPuGfo6k5M/UCqB8ESJFp8rBeVvl1DBXO1KaFXmtCzP5HHhtxFAAAEEEEAgtwVcuS+c1UH3oUOHZdnyUyuax4wZI9/65n941cO9PY06C4ltHQMZdL/j+uvkpz/7+Wk3zdqu33bbrfJv//aV0y5nH57cer2zfbCq7aqq415F9+lW+IUv/Iv8jYbr3U3ve+/d8olPfLzDYjU1NVpZ/e5TAucOC530xo7VT3/y/9r/MMA+7izottbyVoV+8pQKun/0Pz+Wz372Hzp8bBXk69etlWCwYwstq6DvrBL/0Ud+LbNmzeqwDt4ggAACCJxewJULmtPvBZ8igAACCCCAAAIIdCVgYXeTjt3dYI8TFdxa5C3NWr5tldw6y3ttrcyTJ5V019fVy5HDh6SiYoe8oH+cvvOlrXJ09w5J1h2X4nBAigtiUlpa2qOh3LraPuYPvoB1+rNq7iNHjng/ZkH3zKlW0R2Sq14f8ULvwd+Krn8hEJ4s0ZIVGnSfL+HCxRIMT9CFbfi9vg/B1/Wv8QkCCCCAAAIIIICACbhyXzirg26DXLxkeXsba3ufPp1xxhly0003yIUXnC/Tpk0Tv7/7v+TsLCS2dQ5k0J2+jQPx2sJZC2lTU1f7kPp8oJ7Xrnm2wzjoX/3a1+VrX/uPXq/eKru/8uXXqt47C7q7Wmkq6D548JAsP/f8UxazlvNWgZ4+ffkrX5VvfOOb6bNk+vRp8vvfPdphHm8QQAABBLoXcOWCpvs9YQkEEEAAAQQQQACB3gpYCG6TZuDemN0J7aAWj8eltrbW+2P7o0cOy/79+2WfVgLv3lUhlceOSbMu06qBuP1Rug3XZssnk5qc6+TzvRZMpl539ex9oYvvdPdZap3dLWefpy/b1etsXC59m8zbrO3Z7o3ZcHSph71P3y/7Xmqy71hVvh3DTZs2yb59+6SxsVHKS1tl5jQdo/ucoLzRgm5tY56JyecLa8vyYm1ZPk9iZdfo2NyLJRCaIP5Ax86Bmdg2fhMBBBBAAAEEEMh1AVfuC2d90P3ob38n73//B7s9X6y695prrpZr3/JmWbz4nC5D765C4sEIuq0qeuTIkfKzn99/2krrO7Q9+YgRI+SZZ5+VP/3pz53u60vbtkgoFPI+62of7MOrrrpSzj/vXHn8iT96Y4R3ujKd+frXX+KNc/7SSy/J/fc/2OliVom9dOkS7zMbr3zJ0nPbx/1OfcFC7PfefZf3hwa2rs997l9k5TPPpD5uf371lW3tx+R0Qbcdxze+8Q0ybtw4OXjgoFysLdnf8IbLvfXcfMu75S9/ebp9nfbCxiX/8D0f6jDvjVdec4r3Jz/x1/Le976nw3K8QQABBBDoXsCVC5ru94QlEEAAAQQQQAABBPoikGpvbt+tqa2RAwcOyCuvvCobNmyQHTt2yFGtArb7FZMnT9JubvqY2DbUm7XBtmWtWtjuKVjYmv5IBbCpZ/ss/bX9Xvry6a9P91lPlxuIdfT0twZ6uZO33f6QwAJqC7rD4bBEo1GJxWLes7230LuzydrO2x8tbNmyRX772996YbdVdRfG4jJrurUu16D74og3Xndn3x/seTYOdyg6UwPuZRItvUxfz9JxuWN6XrTdHxvs32f9CCCAAAIIIIBAPgu4cl8464NuO4lsDGwLR3s6WbtsC19vvfWWU77SVUg80EH3f/7nN+WKEwGtbcRtt9/ZaWvuj3z4HvnQh14L8j//hS/Kd7/7/VO2+/E//F7OPPMMb35X+/Dud98in/3M37d/9zvf+Z584Yv/2v4+9WLJksXys5/+X+qtPKGh+B13nhoCf/GLn5frr3u7t5z9Q+e///tHclT/Otv+kWrvY9GYfP/73/H+8ZRa2dNPr9T25rem3rY/P7PyLzJmTNuYXF0F3bZd//mtb3SoIm9fgb6wQP6vP/HJ9FkyZ87Z8uuHXhvj/ICG4+eed+r47enjqndYAW8QQAABBE4r4MoFzWl3gg8RQAABBBBAAAEE+iRg1b4WUrdVcFfJ4cOHxcZz3r17t7Yrr9Cq7iqvkri8vFymTp3q/dG6vbbvvPrqq96y9p2+BN0WDttkz+kBeHpo3J/XqXX3dR19/f7pvtfVZ93Nt6DbKudTlfRWRGDHwe6PTZ482Wsfb+uwz21ZOx7Hjx/37u3YHyO8+OKLsnLlStm8ebN3zKLhWjlrRlvQ/YYVEZk6pfOg3NY5OFNAA+2whCJTJVJ8oT6Wa1X3Iq3mHjs4P8daEUAAAQQQQAABBE4RcOW+sBNBt+lalfCnPvVp2bVr9ynYXc2wtub33vtZCab95WpXIfFABt2TJk0UC1bTp1//+jfyoXs+kj7Le23ttK2tdmra9MIL8qY3XZt62/78q18+IPPmzfPed7UPz6z8s/ePmNSXrOXU+RdclHrb/vwv//w5ueGGd7a/t7/gXXTO0lOqtT/96U/JnXfc3r5cdy+atD3Z//3kp6eMpW3fSw/quwq6Vz79Jxk7tut/tNg/whYsXHzKZqxetdKrnLcPOgvDFy5cIA8+cPox009ZKTMQQAABBDwBVy5oOFwIIIAAAggggAACAy9QU1PjtbN++eWXvQruVLgdiUTaqretglsfVjVsobcF4Baa2rO9t+/X1dWJ3S+wyULl1JR63ZPn9DA6tZ7076W/Tn2eek7/LP11T7bjdMt39/3U75+8XGfvU7+T+iz13NP5qRbkFmJbmG1Bt91fWbRokXY/vEZmzJjhrdL+cMEqv+2PFTbqmOoWbG/btk0OHjwodm+osrJSrBI/Gq6RObO0dbmO0X3phWE5c/LQBt1WtR0IjtDxuBdJQfm1GnIvFH9wmFfNnbLhGQEEEEAAAQQQQGBwBVy5L+xM0G2Hyy7cn3/+efnNw4/Kb37zcJdjd6cf2ssuu9SrEk61aeoqJB7IoNvagn//e99J3wzvL50vvuSyDvOGDx8uNg72ydMZZ04/eZb0JOje/upLHf7RaCvpbIzz9NA59UN33nX3Ka3Ouwq67R9Nu/QfrVs2b5GNOobTjh0VsnPnrlPahafWbc/pv9lZ0H1yZXb6d9Nfv/d9H5Df/e736bPk3//9y/KWN7/Jm/fBv7pHHn74kQ6f/+M/3is3v+umDvN4gwACCCDQMwFXLmh6tjcshQACCCCAAAIIIHA6AQukrdrXAmoLqq0a24LPXbt26b/7d3pVwHZ/xe5nTJs2zavgLi0t9aqJt2/fLi/oH++vXr3aW97WZcGqPdv9nN5OqZC3q2dbX+qzk1+f/P50y9myNqUv0zan83mpz05+7uz7Jy/Tk/d2z6Wr6eTPUr9p8+1h46IXFBR4RRAWbp977rly5ZVXen+MUF1d7QXZhw4d8v4QwSru7dimWstb1fwx7eL3kg5LFwlWyjnzg9q6PCSvW65t6ScMVdBt44kHJRAerW3KZ0u06FxtWX65BKN2n8z+SOK1P5Toyoj5CCCAAAIIIIAAAgMj4Mp9YaeC7pMPjf0ja/WatV5Ae3L4mb6shc4WPts0FEG3jSn9bW1dnj7Ztq64+NL0Wd4/DAcy6O4srO8s6O6sjfd77n6fPPbYHzpsX2dB95///Bf5u7//TK8q622l3QXdnZl12JgTbx555FH5wAc7jslt44R/5ctf8v4BbRXf9tfi6VN6xXf6fF4jgAACCHQv4MoFTfd7whIIIIAAAggggAAC3QlYGGrV2BZ22hjcFnBb6G3jPVsLbKvenqhjcNsY0NZ1zYJSG9/ZvmMhqQ1zZkGqtTq34NUC7lQI291vd/V5Kszt7PPTfZa+fE+XS/+Ove7r905ez0C9N8v0ybYvva27/QGCBdwLFiyQ888/X2bPnu2F3vbHC1Zpb3+IYJXcdpyttfm4ceO8P1iw9VjwvUmLGVavWiUB2ScXaMB9rgbd58wPybjR/vSfHbTXPl9YfIFCCcXOkoKyqyRSuFSCkck6r0x/k5B70OBZMQIIIIAAAggg0ImAK/eFnQ66090rK6vkm9/8lvzXD+5Ln+29fvObrpGvfvUr3muCbvHaqlt79fSpu6Db/jFlreN/+rO+tQAfqKC7vr5Bzp7T1sI9tf3WkmvD+udk3fr1ct11r7Vkt89f97oL5Yf//YPUojwjgAACCPRSwJULml7uFosjgAACCCCAAAIIqIBVW9sfi6fG4LYW1qkKbmtTbvOtLfmoUaM6VHDX19drd7cdXuvrVRqMWiBuLbGt/XVfK7g5ID0XsGDaHqFQyGtTblX1FnLbHyOcffbZXsBtIbfdL7E/QLCh7azi3oowrG25fW/69OnecnPnzvWO8+OPP+6N071+3Tq9Y/jwAABAAElEQVQpKTwk11wekeWLgzJV25YPKxvsoFtDbF9A25UP00ruqdqyfLHESq6QYGy2167c5wv1HIclEUAAAQQQQAABBAZEwJX7wlkddH/1a1+XCm2LnT7dffd79EJ8VvqsDq87a21tY1tb62+bugq6O2v7feDAQTn3vAs6rN/epFdEf/vb35Uv/uuXOizTWXWy6xXdT/zxSbnjjrs67GfqzRlnnCEXXniBN9b43Dlz5C3Xvi31UfvzQAXdtsKPfuyv5Re/+GX7uu2FHd/Hn/ijfP3r3+gw3yq9reKbCQEEEECgbwKuXND0be/4FgIIIIAAAgggkL8C9gftVpVt9ytsnGar4LaQ26p/LSBNVXBbFbeNyW2ht31u4zrbd6zldXoFt40PPRAV3Pl7RHq+51bFbS3kLeAeP368F1gvW7ZMZs6c6VVw22c23raF2zYEoLWgtz9YGDlypPcHC3ZsbQxv+77Nt0ruH/7wh17Qbcd19rSE3HVTVIPukBQV+iQcGuRqam1X7vdHNeSeJbGyK3RM7iXarnyqjss9XFECGuoPdtDec3uWRAABBBBAAAEE8kXAlfvCWR1033b7nfLkk091OGfuueev5MP3dGxdnb7AZz5zr/zPj/83fZYXwP7+d49689atWy9vfdt1HT63Nyuf/pN3kZ/+wcpnnpGbbrolfZb3Oh+D7r/+xCfl/vsf7GAxZswYbyzys88+q31+PB6X2WfNbX+fejGQQbedE3ZupE8f//hHddz2R04ZJ9wqvUtKStIX5TUCCCCAQC8EXLmg6cUusSgCCCCAAAIIIJCXAhZspyq4U2NwWwW3tR234Nqqsi3MtpDb/r1vLbAtDC0uLhar4LYKb2t9bRXc9toCcSq4h+ZUSlVwW4Bt43DbfY7Ro0fLhAkTxIoPLOC2Io8RI0Z4lfX2BwhWcW8PO1b2BwgWiNsxXbRokXdcbV5tbY3s339AnntutTz00IOye+cWrd5ukcXz/XLdNRE5a4aFzIMZctu6NbQPleu43PoHFYXn6Jjcb5RwbI5WchfpIzw0wPwKAggggAACCCCAwCkCrtwXzuqg26pz//2rXzsF16p37QL+5Gn79h3ypjdfe8oYzentqw8cOKBV2hee/FV5z3vulE/9zSfb59s//t6ubbBt7KKTp3wMuq9/xw2yRsdDT58+97l/kHfddGP6LFn73HOntA+3BQYy6LZjs3DRkg7H2Vp02V8dp09XXvlG+eY3vp4+i9cIIIAAAr0UcOWCppe7xeIIIIAAAggggEBeCVjIbQ+r8rXgc+vWrV4F9/79+73w28Jsq/K18bftYWNyW7htba6t2te+Y1XB9rAxuC0op4J76E4hq+C2gNuOix0raztuY3DP0a56drwKCgrECg+s4n69Dutmz/beKranTZsmU6ZM8UJxq+i2kNzG6LZz4IUXNnnnwc6dL0nN8b0ydmStrDjPL/PPDsjk8X4pLRnsSuqAVnKHdUzumRItuVRbli/Vqu4ZWsk9SkPuoAIP9u8P3THklxBAAAEEEEAAAdcEXLkvnNVB9+naZd900w1yySWXSERbLL2qrZj+9Kc/y+OPP9HpefLP//Q5ufHGtrGb7S9Wp06b2ely11/3drnssku9C/5vf+e78vLLr3S6XD4G3be++3bPOB3k1ltvkXs/+/fts2yc9He884ZO3f4/e2cBZ1XRhvF3l+4O6Q4JQcD6UBEUE1skRQExkBRU7C4UMZCwBVFBLAQUA1BREAWkpaVLQrDR/d5nljnMvXvu7t3du3F2n+F398yZnv+ZE5znvDOxFLpR4V133SMT3pjo1e3nGTvmeWnf/iy/KIaRAAmQAAlESSAoDzRRdofJSIAESIAESIAESCDXEICwDYtrrMENYRMit7XghvU2rLhhlQ3hE2IpRFNYcENQhZCNNJimfN68ecYyGOVAPD18+LCxEM41ILOgo9aCGwI3/EWLFjUW3LC0hyU3LLNbtmxpxGukwfHF8cIHCevWrTMfKeC4wtq7VatWRuiGGA7DgT17dumxXy8/rVoi69ctlc2blsk/f+1SS+44aVg3Xs74Xx6pVS1eBWisA55RnUe/dLryvCXUmruyCtzHS6ESZ6sldyNdqrukitwFM6pilksCJEACJEACJEACJBAlgaC8F87WQjcewLt06W6shKPk7ptsoU7BVKpUKS/Ob0p0LzIKT24UurEOOdYjD3ft2rWVE09oJXv1P8zjx78RYmXtpo210D1//nfSqXNXt4ok/pUrlkrBgvzPURIwDCABEiCBVBAIygNNKrrEpCRAAiRAAiRAAiSQ4wlYC+69e/ca4XPlypVmxjrMcoc4WPpaC25Mf126dGnz/2dYcM+ZM0dWrFhhLLetFbe14IYVty07x0PMwg5aC+58+fIJfjhWJ598shx33HFSu3Ztc7yQBtb1mIkQU5RjunKspQ5xG78aasUNYRzHFh8n4NivWrVSvvvuG9m2dY3aSu+TUsUPSI3KB6RC2b+lZPE4I3aXLxsnRQpnpMgtKnLnVzG7sLHkLlS8rQrdLSRvgToqfJfVuHyaIE8W0mfVJEACJEACJEACJEACIBCU98LZWugGyF27dst553dIMi014qJxr7z8orRpc3pI0tWrV8vZ55wfEua3c4r+J2K9/mcB/xlwXW4UuiOtV+5ySc4fa6Eb/7k+8aT/RRwXV3a8Qh599OHkmsQ4EiABEiCBKAgE5YEmiq4wCQmQAAmQAAmQAAnkaAIQoP/66y+zzra14Mb7DLsGN7aIhyEARFAsCYcpsCtVqmTEa4jcWH978uTJsmjRIlMO0qPccOcXhjSpDU8uT0pxiI/kol1XOtp06a0nUn433LYFDGF9jWnGsUwbhGpMPw7L7Fq1ahkLfByXbdu2Gav8NWvWGGt95MexbNGihbe+ep488freZI+m3aLW22tl7dolsuanefL3n5ulYrkEqVMjQZo2jJMqlUSKFIpTUT3DTLgTu6oCNoTs+LxldIryGpK/8PFSsFhbFbwbqPBdXH8FXCT0kwAJkAAJkAAJkAAJZCGBoLwXzvZCN44h1g266657U2XZja9WMa322We39x0GL7z4kjz88KO+cQhE/mkffSCXXHq5Tv+0OSRdbhS6ASCSVbcL54brrzNreWOtbtfFWuhG2Y8+9riMHfuCW43nnzD+NV2v6hRvnx4SIAESIIG0EQjKA03aesdcJEACJEACJEACJJAzCEAcxVJtsOpdu3atsciGpS8ssmH5C7HUWnBDDIWIWrJkSTMlNtZ9hsj91VdfeT9MgQ0rYIin+XXJuDx58oSI2KjPitp2C5LJhVvSNn1KW1teeD67H761QjHCI/lTinPj3TIQ7uf80viFuXkjxdtwcMEU8s2bNzcfIxx77LFGwIb4feDAAfnpp5/MMYYVN9LiuOLDBYjgOLY41jhmmGZ+48b1Mn/eXF1ibqWK4TukQN7dUv2YX6TKMX9I5YpxUq5MnK7DLVJYJ8PLkydOx4rb0tj7MSV5fN5SOkX5sbomt1pyF24uefNXUwNunYVRpzKPoyV37KGzRBIgARIgARIgARJII4GgvBcOhNBtj8G8efNl9Jix8sMPCyNOkV23bh3p3buXXHLxRWZ6J5vXb/vDwoVy//0PmWme3Pjzzz9PBg8aqP9JqCnnntfBCO1u/Lxv5+qaSOVN0CuvvCb3P/CgGy0Xa91PjXgiJAxrX7U548yQsGrVqgpE83DXuEmzJP374P0p5j84SIt2X375lSHZihQpIsuWLg4Jw07LViclsXp2228z9B8wSKZO/cjumu0ddwyT3r16emGwop4+fYY88eSIJOI/vgS/4Pxz5dpre8sVV3RK8lGCK3RHy8yrOIJn6dKlcuFFlyaJBYvFi3+QvPofcToSIAESIIH0EQjKA036esncJEACJEACJEACJBAsAhA48bMW3BBAsQY3LLjx/gHrNeMHsRqiNkRQO+011njGms+ug7X33Llz9f/Si1UUXSOY8hwO63VjOmxsUR9cSlsI7vZn22kyOnnDy7Flhof75XPDIA5DhLcisY0L37fhGbl1+xBeT3Jxblo7ZTkE62bNmhmLe4jeCMdHCzhO+JABx/nQoUPG4huCOMRwTGleuHAh2b9/v6bdJVu2wIp7maxcPlf27lmn70gOSuUKf0uzxiK1q0PkjpfCasWdGc5MVZ6niOTJW14tuWuZNbkLFDtd/XVV4C6ox0+nK6cjARIgARIgARIgARLIVgSC8l44UEK3e4Txn7ht27bLzp07zdpSePAvV66s+Q+Omy4aP9YC36BfK+fXdY9gyc11nZOnhv+w7t69RzZv2Sx54vPolFh1BeJyZjv8p65J0+ZJqu3V8xq5887bk4QzgARIgARIIPUEgvJAk/qeMQcJkAAJkAAJkAAJBJcA/l+Oj9EhfkKYxprasOD+5ZdfjDUvpryGpW+1atWMdXDZsmWNBTf+7453HhCuXff7778ba3CIpPi/9t9//22iIbDi5ycc+4m3CEPbILBDhEcb3bx+eVBRpHDbRr94hEHkdoV4m87dun63LoS7cdH4U8qfmjLC02IfltjFihUz77lgcZ9olb3RiNtYZx37+FABxxXCNtZXx4cM+HABx3Tnzh2ycOECWb58qa7Nvlb++XOLHFNur1Sp+JtUr/yfVCyfIKVKxElRfYVTID8+ErCEM3YLK+68+TFVeRMpUOwUFbgbSp58x6gldzGtGB8qZLApecZ2j6WTAAmQAAmQAAmQQI4kEJT3woEVunPkqGGnUkVg+PAn5fnRY5LkeXfKZJ3iq1mScAaQAAmQAAmknkBQHmhS3zPmIAESIAESIAESIIHgEIAIih+EzoMHD5oprGHBvX37ds+CG9a+EJlhCIA1nTHzGiy5IYRi2uuUnBVeXWE6pTw2HoI2hG2sDb5r1y7Tvj/++MMI3jZNLLZoI+qyPCDo169f31g2o3wbbvti922c3YaHR7Nv89ptNHmQxqa3WzfMloEtjp2NA0t8dIDjCwtuWGfjowZ8qNCoUSPza9y4sa63XlLwkcL+ffuNyL1x42pZtXKBbNm8Qg7s2yQliv4qTRrESf3a8VKzWryUKhkX8uGBaVyG/EE9+oFEfCGJz1Na8haoputwN1ah+zi15m4hefJX0Xh8bEGBO0Pws1ASIAESIAESIAESiAGBoLwXptAdg4PNIjKPwKWXXeFVtmhR0qnaIXBD6KYjARIgARKIDYGgPNDEprcshQRIgARIgARIgASyJwGIoJiNDiIy1miGdS+W88IU41hjG2I21mmGhS9mqsM+LIIhjMLiGZbPGekgtkKIXbVqlXz99ddqTbzOCLBosxVvY1G/aymOcjFtd8+ePc3Wlh9eX/g+0oWHpXY/LWUklwf1Q8CHRT7Y4fjCQh+zGZYoUcL7eKFq1arGohvrcMOK+9dfD2jaFbqU3VL58cdFsn/vRilRbJ9UKHNQalT5Q44p/6+ULa3rcBeLkyL6rUN+teLODGfW2lYhO2/+qipstzICd76Cx0qeAlV1+vLSRgDXOcu1KZnTnszoM+sgARIgARIgARIggZxGICjvhSl057SRl4P787OuL9amTbtke/j6a6/Iqae2TjYNI0mABEiABKInEJQHmuh7xJQkQAIkQAIkQAIkkP0JQPiEuG0tuGHdC1HbteDeunWr6UjlypXNWs5YgxtiN6ycIX5npEP78IPVtl0bfKMuCQfrY4jdEGztlOdIl1ZnBf7ffvvN9B/CL6ZWR9kQgE877TTp27evnHDCCWmtIkvzYYp49A39ArNt27YZoRvrrMNCH9ORY4pyrMGNNbvxEQN4Jk41v0s2b9ogq1cvkw3rlsjmn5dInrhdUrdmvNRTC+5j68ZJhXJxki9fnPLKpG7qWtvx8YV1SvISOjV5OZ2ivL4UKHqSsebOm7+axOctmUkNYTUkQAIkQAIkQAIkQALpJRCU98IUutN7pJk/0wi8Pn6C3HPPfRHrGzRwgPTvf1PEeEaQAAmQAAmknkBQHmhS3zPmIAESIAESIAESIIHsSwDWvbCG3rlzp7HuXb58uVmHG4I3LHmxTjPW4LYWvhC3rQU31nnOaAtuK0BDbF+4cKGxLoelOcLr1q3rWZcXL148XZAxhTemal+/fr0sWLDACOkQ1lEuhP2TTz5Z2rVrZ6ZoT1dFWZAZgjWO54YNG8yx/eGHH2THjh3m2GFNdUw/j2NcqVIlz0IfwjjEcFhxfzf/G53SfLXIf3ulZNH9UqPyAalc8S8Vt+OltE5RXlyXvy5YAFOIq910JhlOx0PgVivufIUaSAGdojxvwXrGqhvTl8dBAI/PnwWkWSUJkAAJkAAJkAAJkEBaCATlvTCF7rQcXebJEgLX9Owts2fPSVI3/kMPkbtr185J4hhAAiRAAiSQPgJBeaBJXy+ZmwRIgARIgARIgASyBwFYSFvrbQi6EDWtdS/8sGSuVq2a1KtXz6zBDQtuCNwFCxbMlA5YgRttg8UxLLhXrFhh1pCGVTKmTD/llFOkSZMmRqRF27Dmt/1F00gIwLBkRx2YDh39hrU4hPQ9e/YYi2YI/RC6YekMi2dM5R0Eh6nXrZW+tdBH3yB244d4WG3j+LZo0cIwxPrqhw//I3th8b19q2xYv065L5HVq76VP3/bJOXL/iu1qiXIccfGSdXKIkWLxEl+teLODGemKJe8asFdVNfiLqkid2W14q4r+Qs10inLm+t+tUSBWy296UiABEiABEiABEiABIJFICjvhSl0B2tc5erW4j+33/+w0HzR/sfvf+gX7OXN+mNYk6tw4Yydli1Xg2fnSYAEcjWBoDzQ5OqDxM6TAAmQAAmQAAnkGAKYmtxaSGOdZlgzQyyGsAtRG9OUw28tuCGC5suXL8MtuC1ga2GN9cGnTp1qLK0xTfoxxxwjDRs2NMIshFoIz8WKFfPWB4dAb6cyt2X5bSFyQ0wHh0WLFpl1qiGkQxxG38EAFuPwow5YdsPCHVbsQXCYdh1W26tXr5bvv//eCPiYhhx9gAU3+met9EuVKmWOK6Y2h7g9Xy24V69eoWL/NskXv1OqHfOLVD3mD7XijlOxW9fhVuN5vBrJmyfzpiqPiytgpiPPV6CW5CtyvFpyN5S8BWrqOtwV9afTlMOKOw7rw2fW3OlBGAVsIwmQAAmQAAmQAAkEg0BQ3gtT6A7GeGIrSYAESIAESCBLCATlgSZL4LBSEiABEiABEiABEogRAVjywpob61tPnz5dMFU5BM4iRYqYabnr169vLLhhzQ1xt0CBAjGqOXXFYB3pNWvWyFdffSVTpkwxltxVqlSRBg0aSKtWrczH6FhX2grbdguLbit0W+tuvy2mbMf03LDkRj2wGoffTlUOS3FYcZcvX94rL3U9yNzUtj84lrDUx1T0mzZtMgL3unXrzHrjOMaYohz8IHbbvu3bt1d27dolW7du0TW4V8jKFd/Inl1rVDI+IBXL/SnNGovUqRFvpiovUjizLLjz6jzo+dV6u7D+iuuvjFptH6NW3PXUgvt4Fbnr6NrcFdSKu0jmgmZtJEACJEACJEACJEACMScQlPfCFLpjfuhZIAmQAAmQAAnkHAJBeaDJOcTZExIgARIgARIggdxIAJbbsGKeN2+evP3228aC+ayzzjLTc0P4hAV3iRIldDazRAtuiMZZ4TB9+IcffihffvmlLF682EwvDsttWFejnRBtYZFtLbPht/toL8RtrB+OH/pg/XaLdclh9Yx99BnWzRD5IQTDUhxhVuhHWdnd4eMFTFGOacnBC+I9jjM+BqhZs6b3g0U8LLhxfOF27IBl//eybNkSzbNa/v5js1Qss1cqV/hNqlf5VyqWT5BSJUSKFY3Tjx7i1Io7c0hgne08+cqpoF1NxW1Yb9dJtOBWsTtP3lIqcBfVY6wfYcSpIE5HAiRAAiRAAiRAAiQQaAJBeS9MoTvQw4yNJwESIAESIIGMJRCUB5qMpcDSSYAESIAESIAESCBjCWA96vXr18ucOXPk9ddfN0Jx9+7djZWvK+z6WUGjZW643bdbG2f37TY8HPtunJ8fa3LD4vzHH380ltaYUhxTp2PqcPwgXsOK2QrcdgvhGz/UYUVtK3S7WwjdEP3RZ1huN23a1KxVDUt2TJGOurKjs/2DNTqmIscPVtwQuTFVOSzTIXbDIt6uw401uNFHiPgQuA8e/NWk37lzh1p8r5FVK7+XLZtW6NrcG6R44QPSuH6c1K8dL7XUirt0SbvueUbRwFjAdPM6JTymH9dfvFpp58kHC+7Kkjd/LclfuKH6a6nQXUXji2VUQ1guCZAACZAACZAACZBAFhEIynthCt1ZNEBYLQmQAAmQAAkEgUBQHmiCwJJtJAESIAESIAESIIFIBKzQPXv2bBk/frwRRE8++WSpUaOGmabcisiwBHaFYojE9odw64egbP3RhNu0KW0h3kK4/fXXX41gi/6gfLTLTlluBXS7RZnW7/YfYeHOTuFup/OGFbe1dLb9CM+THfYh6KPtOI52anII2xC4wQt9hXU6RO1atWpJpUqVPOt0CPgHDuwXrMm+dOkSXZv8B9m7Z70UK7xfypc5KDWq/C6VKhyWsqXipGTxOLWaF8mfD0J3RvYcY0k/XMhbWsXsqipmV9efitrqz5OvssTnK2umLscU5fEqgtOCOyOPBcsmARIgARIgARIggawhEJT3whS6s2Z8sFYSIAESIAESCASBoDzQBAImG0kCJEACJEACJEACEQhAIIUwCqEbFt1Ym7lZs2ZGELVCMgRjK/a6grQVkV1BOa1+Nx/Ktft2a8VpbG1boq3fdt1OZW6toF2rb4jFsOrGGuSYphyWzrZ85E/Jj3ibzm7D80QKd/OG+90y0G7bB7sG919//WWmm8cHADh2+GE97gMHDgjiMLU71hdv1KiRmYod4j2s4WHFvWfPbtmy+Wedony5rF+7RH7esFjiEnZIrepxUk8tuBvVi9OpyuOMuK3fFMTUxcVpgZhmXNfdxpTj8fGF1Dq7oP5UwM5bwqy3DXE7b4Ea+qupYZU0rKyx8I5pQ1gYCZAACZAACZAACZBAtiMQlPfCFLqz3dBhg0iABEiABEgg+xAIygNN9iHGlpAACZAACZAACZBA6gns379ffv75ZzN1+WuvvWYsptu1a2esfyEoW+HVisN2awVXV3xNj98tzy3HDU+rH/nCf+405/DbH4R1iN2Yqhz+aH5WeLeivM0TTbhNG2mLMvCDQxshyOOHKcoxPTkEbVi7Iz/WUoeQXbZsWbOueOXKlc365QhDHNY0h/i9bds2Wbliucz/7hvZvGm1HP77FylRZJ9Ur7xfqlT8UyqUi5cyasVdXGcFL6jrcGvROg5SP7aSyxEXr+J2nhKJltv5yuu2olpt63rb+SrpVi2380LUVtE7j669naeI1l/wiCjONbiT48o4EiABEiABEiABEsgJBILyXphCd04YbewDCZAACZAACWQQgaA80GRQ91ksCZAACZAACZAACWQKgUOHDsn27duN0P3CCy8Y4bR9+/ZSt25dT+S14jYaZP3hWzfO9bvpIoXbNDY+XJTOzH20xVpRYwsB2W/fDY+F3y3DrRfhrtBtWcAqG8fujz/+EKzPDQt0TEuOacqxrnjNmjXNDwI3LNUhjOOjBkxnvnHjRlm/bo2sWbtK/vhtl4ra/0nNqv9K04aHpVqlw1Ks6L9qxX1Yj/VhPST/6kHXn/ynP6x3nriF/6hTJVwwpTm2UMWVmcBiGx9KYG1zFafj8xq/2VcrbgjY8XlKabCuvZ2vwhELblhtH2NE7/i8xU2ZKJeOBEiABEiABEiABEggdxEIynthCt25a1yytyRAAiRAAiSQKgJBeaBJVaeYmARIgARIgARIgASyGQFY+B48eNBMXT5ixAhj7du6dWupUUOnjHbW5baWxeFbK8SGhye3n1wexFkH0RkuXAjPjHAIyqjHbm0b7L7dhqdJa7ibzy3ThoMDBHDLFWunQ9y2P6wtDr+7hR/W37/88ousX79elixZImvXrjVTm+fPn1dq16ouNWuUlSqV8km50oeleJFf1YL7kOSNP6TQD6m+fVD++/c35fCX7v+tv3/VD/EbIjjEbxwfFaJVzIaAHWemIk/0m/WzjUV2MRW0i6nmjW1x/em05Cpux+k2Lh5iN6aIL6BCuE5brlbbidOX6zTmKIsitzKgIwESIAESIAESIIHcRyAo74UpdOe+sckekwAJkAAJkEDUBILyQBN1h5iQBEiABEiABEiABLIhAUyHDYvghQsXmjW6Ye0Lq2BMdw2hGz+Iz1acTm7rl86Ks24++N1ybT6EhVszYx8OaeC3v+T2EQfnlhUpX3i4yah/rLCNLZy7HyksUjjyW8EaWzDHNtof0sNZkRvTqkPUhrU2fsWLFzfTrSMNrLcxlTksuO305rDY37x5s7HkxnTnaGelSsfICa1aSIP61eSYigWlaOHDKmr/qm06pNuD2uDEbcK/v2v6PyXhPxW6jXW3ity6NUI3rLthva2itCd0C4Tu/Ea4TrTaPiJyx1uxu6RabJdOFL8hcBtBGy2nIwESIAESIAESIAESIIFEAkF5L0yhmyOWBEiABEiABEggIoGgPNBE7AAjSIAESIAESIAESCAABCB6QnDdtWuXrFixQjZs2GD8mBIbgiqEYD9x1oq14dvwtOHxdt9NZ8OwhbPCN+q34i5EcPixtT83Ljw+pbjkyrB57Rbtsf7UbN188IcL4egrwqIJR1orymOL9oMPfvDD4YMFCNlYcx0/fLSA9bh37txp2l+lShWpXr26WX8d05yXK1dW1+EuLAULKtc8sGCHpfY/WpIeB2zN/hFR+8jU5XFHpi/XVht7a2PTbaYq1/5piE72bsRvPYpGxDYCuE5bjunLE8VwncpchfBE62+sPZ74IYN66EiABEiABEiABEiABEjAEAjKe2EK3RywJEACJEACJEACEQkE5YEmYgcYQQIkQAIkQAIkQAIBIgCRFFOYYw1nTG+N9ZytgAoBGlNgYwuLYfghVLthiLPpEO6mselsuE1ny3PzolwIuVbstlsIzK4/fB9xfmE23I2zYe7W9SOtFZOxdfPCb/ft1s3r+t14hOOH8uzP3cdQ8UsTnhbprDiOLdjh2OGHdbsPHDhgPlTAhwu7d+82xxRxZcuWlcaNG0ujRo2kXr16Zt/WjzLpSIAESIAESIAESIAESCC7EAjKe2EK3dllxLAdJEACJEACJJANCQTlgSYbomOTSIAESIAESIAESCDVBKxwDbH00KFDRjiFEAoHQRXxdosw7NutG2792Ib/bBk23O77lYUw/CDkJrd146wfeVCHFdARbuPcrRtu63Hj/cL84t10kcoEKwjf+HjAb2vDbDz2wy3asQ9n68OHA7C837dvn5muHII20pQsWVJKly5txOzy5cvrNOWVBFtMR49pzosVK2amOoeITkcCJEACJEACJEACJEAC2Y1AUN4LU+jObiOH7SEBEiABEiCBbEQgKA802QgZm0ICJEACJEACJEACgScAgdqKyX4W4KkJgyAcnh77Njx8G22cXzqEod3Y2p9bPvoFYRkCtrXcDveH7yMdwmw+18LeCuqwsIclPraoAyJ25cqVBdOUV6tWzUxVXqNGDSN+04I78KcHO0ACJEACJEACJEACuYJAUN4LU+jOFcORnSQBEiABEiCBtBEIygNN2nrHXCRAAiRAAiRAAiRAApEIQLC1Pwi61m+30YYhfXja8H2bBm0Jj8N+eHh4Gpvfts3u++VDXojf2NqfFcjtfni83cfW1gHh24rg+fPnl0KFCnm/woULGz+29lekSBFj6U0LbnM4+YcESIAESIAESIAESCCbEwjKe2EK3dl8ILF5JEACJEACJJCVBILyQJOVjFg3CZAACZAACZAACZBAMAhAyLbrm7tb+FOzj97Cyhs/TFNetGhRKVOmjDctecGCBY0FeDCosJUkQAIkQAIkQAIkQAIkkJRAUN4LU+hOeuwYQgIkQAIkQAIkcIRAUB5oeMBIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARiQyAo74UpdMfmeLMUEiABEiABEsiRBILyQJMj4bNTJEACJEACJEACJEACJEACJEACJEACJEACJEACJJAFBILyXphCdxYMDlZJAiRAAiRAAkEhEJQHmqDwZDtJgARIgARIgARIgARIgARIgARIgARIgARIgARIILsTCMp7YQrd2X0ksX0kQAIkQAIkkIUEgvJAk4WIWDUJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJ5CgCQXkvTKE7Rw07doYESIAESIAEYksgKA80se01SyMBEiABEiABEiABEiABEiABEiABEiABEiABEiCB3EsgKO+FKXTn3jHKnpMACZAACZBAigSC8kCTYkeYgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIICoCQXkvTKE7qsPJRCRAAiRAAiSQOwkE5YEmdx4d9poESIAESIAESIAESIAESIAESIAESIAESIAESIAEYk8gKO+FKXTH/tgnW+I///yTbHxcXJzkzZs32TSMJAESIAESIIHMIhCUB5rM4sF6SIAESIAESIAESIAESIAESIAESIAESIAESIAESCCnEwjKe2EK3ZkwEg8fPixPPDlCJk16R/bt25dijQMG9JOBA/qnmI4JSIAESIAESCCjCQTlgSajObB8EiABEiABEiABEiABEiABEiABEiABEiABEiABEsgtBILyXphCdyaMyBFPjZRnnx2VqpoodqcKFxOTAAmQAAlkEIGgPNBkUPdZLAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAnkOgJBeS9MoTsThmbrU9vI1q1bU11TbhS79x84IMuWr5TVa9bKb7/9JqVKlZITWh4v9erWkYSEBFm6bIVs3rJVWp9ykpQoUTzVTJmBBEiABEggdQSC8kCTul4xNQmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQCQCQXkvTKE70hGMYXjdeg0F05dnhitfvpxcdumlMnjwwJiv9b1p8xZZ/OMSI0KvW7/R9Kl2rRrSoH59adyoodSpXStdXVz10xoZ/uRI+fe//0LKaXNaa7n6qq7ywdRp8t4HH5m4fPnyyfPPPCnYwkEc/2j6DOMvUbyE3HBdL+PnHxIgARIggfQRCMoDTfp6ydwkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAKWQFDeC+c4ofs/FUn37t0nZcuWscciy7eZKXTbzt50U1+5WcXuWLmPZ34mb02akmxxl11yoXQ4/9xk00SK3LdvvwwaOsw3+uaBN0mTxo3krvseks0qtlt329BBKrLXM7szP/1CJr492fgLFiggY0aNtMm4JQESIAESSAeBoDzQpKOLzEoCJEACJEACJEACJEACJEACJEACJEACJEACJEACJOAQCMp74RwndMNy+rY775VhQwdLmTKlnUOSdd6sELqrVKksX305OyadHjX6BVnww8Koyjqr3RnStXPHqNK6ieZ8NVdeeW2CF3RswwZy8omt5NeDh+Tss9oa6/Q5X34tr7z+hklTtGgReWbE4xIfH2/2KXR76OghARIggZgSCMoDTUw7zcJIgARIgARIgARIgARIgARIgARIgARIgARIgARIIBcTCMp74RwpdPe+vp+x6M4uYrcVuvPmzSsrVyzN0NOi4bFNzJTiqGvN6pXprmv+gu9l9NiXvHJK65rZ3bpeKY2PbWhEZkw3Pu7FV1SQPuileeapx6V4sWLefjSe58e+KN8t+MEkTc4ie8fOnbJr126BEI4+Wkeh25LglgRIgARiSyAoDzSx7TVLIwESIAESIAESIAESIAESIAESIAESIAESIAESIIHcSyAo74VzrNCNoYfpy7OD2O0K3bEQn5M7rWJd12NPPCUrV602VRYqVEiGP/KAwJradbt275Zbht3tBfXu2UNan3KStx+N54mnnjHrbCNt0yaNZPCAm6LJ5qWh0O2hoIcESIAEYkogKA80Me00CyMBEiABEiABEiABEiABEiABEiABEiABEiABEiCBXEwgKO+Fc7TQjfGXHcTuWIvPyZ1Xsa7rtjvuld0qZMO1b99Orrz8Ut/q3fWzT2zVUm64rpdvOjcwISFBXp/wpvz511+ydNlyOXToNxMNIR1rcsMVKVJEuh2ZCn3Dxp9l5mdfJAlHQLRC97+6hvv8776XxT8u0X7tkXz58kmtmjXUQry+1KtbRwoWLGjKj/QH678v0bau37BRfv55k0lWrlxZqVenjrQ5vbXkz58/UlaGkwAJkEAgCQTlgSaQcNloEiABEiABEiABEiABEiABEiABEiABEiABEiABEsiGBILyXjjHC90YG1ktdsdafE5uvGdmXW47br39btmpU4rDndr6FOl1dXc32teP9dQxzXxyLo+uwf3SuFEmSXJidnJxtvxf9u6Ve+5/2BPUbbjdoq6re3STU/93sg0K2UIgxzTtEMv9HETzoYP7G8HcL55hJEACJBBEAkF5oAkiW7aZBEiABEiABEiABEiABEiABEiABEiABEiABEiABLIjgaC8F84VQjcGSFaK3ZkpPmdmXfbEW7FylTz+5NN2V65Rsfj0U//n7UfyZKbQ/dvvv8sdd90v+w8ciNQcL7yntv+0sPbP/vJrefX1N7w0kTwQyx956F4pX65cpCQMJwESIIFAEQjKA02goLKxJEACJEACJEACJEACJEACJEACJEACJEACJEACJJCNCQTlvXCuEboxVrJK7M5M8Tmz6vpPrZp37/lFpwFfIB98OM2zci5erJg89cQjkidPnqhOT1hZw40e+5KsXbfe+OvUruVNfR4XFyelS5Uy4clZbScXB0H9vocek82bt5hy8KfD+edK82ZNpWyZ0rLyp9XyyqsTzBTqNsGI4Q979SLMnZodfezetZM0Orah5vlTvlvwg7w1aYrNatYnxzrldCRAAiSQEwgE5YEmJ7BmH0iABEiABEiABEiABEiABEiABEiABEiABEiABEggOxAIynthCt2ZMFoyS3xGVzK6LqyTfd+Dj/pSq1ihggwe2DdN1sxPPPWMLFu+0pQLAXrATTckqSM5MTu5uM9nzZHxb7zlldfvxj7S4vjm3j48sPS+eejtnmB/4QXnyaUXdzBpsHb4TQOHeOn98k+dNkOmf/ypVK1SWY5vfpyc0/5MLz09JEACJBBkAkF5oAkyY7adBEiABEiABEiABEiABEiABEiABEiABEiABEiABLITgaC8F841QndWWXNjUGa0+OwO/IyuK5LQXbRoEenaqaOccEJLwfTdqXUZKXTf+8AjsvHnTaZJzZo2kYH9b/Rt3jvvfiAfTf/YxFUoX04ee/h+4//tt9+k74CjQveJrVrK9X16CqzN6UiABEggpxMIygNNTj8O7B8JkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJZBaBoLwXzhVCd1aK3BhwGS0+u4M6o+vaum27DNf1uDF1+a8HD7pVG38VtWi+5eYBgum9U+MyUuju1aevZ6nd4vhm8r9TTvJt2nK1KIf1N1y+fPnkhdHPeOn6D7olpL8Q9tu2OV2aNmkkNWtUj3qqdq9AekiABEggIASC8kATEJxsJgmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAlkewJBeS+c44XurBa5MVIzWnx2z4bMrAv1btu+Q96ePEV+XLLMa0bjRg1lyKD+3n40nowSuv/++2/pc+OAaJqQJM1L40Z51uno31PPjEqSxgbUq1tHTjv1f2Z9bhvGLQmQAAnkBAJBeaDJCazZBxIgARIgARIgARIgARIgARIgARIgARIgARIgARLIDgSC8l44Rwvd2UHkxmDMTPE5M+tyT7SnnxstixYv8YIeffBeqVixgrefkiejhG5YncMaOy1uzKiRUrBAAS/rmrXr5OVXJ8j2HTu8sHBPo2MbSL++14fkC0/DfRIgARIIEoGgPNAEiSnbSgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQALZmUBQ3gvnWKE7u4jcGKSZKT5nZl3uCfizroF9j66Fbd2N1/WWE1q1sLspbjNK6E5ISJBrrj26JveZbdtIs+OaptieAgXyS906tX3T7dq9W5aohfdPq9fKyp9+kkOHfgtJd3zz46S/it10JEACJJATCATlgSYnsGYfSIAESIAESIAESIAESIAESIAESIAESIAESIAESCA7EAjKe+EcKXTfdue9MmzoYClTpnR2GAuBFboh6L41aYrH8IrLLpZjKlb09l3Pzl275dbb7/aCenTrLGe0Oc3bT8mTUUI36r2+70D586+/TBMuuegCuajD+Sk1J1Xxy3Rt7zEvvOQJ3nni4+XFsc9JXFxcqsphYhIgARLIjgSC8kCTHdmxTSRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQRAJBeS+c44Tu//77T/bt259tRG4M3sy0so5lXVh/+/a77vPOvzPbnSHdOnf09l3Pu+9/KB9+NMMLGjp4gGAa72hdRgrdY198Rb6d951pSqFChWTkE49IAWdKctvGr7+Zp1baa6RG9WpSp1ZNqa5buC1btsqKVT/J1m3bJV7F6x7du9gs3vazz2fJhDcneftPPv6QlCmdPT608BpFDwmQAAmkgUBQHmjS0DVmIQESIAESIAESIAESIAESIAESIAESIAESIAESIAES8CEQlPfCOU7o9jkWWR4US/E5pc7Esi5M+91v0FDPUhl1n9P+TGl9yklSpUpl05TDhw/LjE8+lSnvfeg1LV++fDLq6Sckf/78XlhKnowUuteuWy8PPjLca0LjRg3lBp1avUjhwl7Yqp/WyKPDR3j7dWrXkjuHDTX76N/bk9/14nr26Cannfo/bx+cRjw9SpYuW27CihYtIs+NfMKLp4cESIAEgkwgKA80QWbMtpMACZAACZAACZAACZAACZAACZAACZAACZAACZBAdiIQlPfCFLozYdS0PrWNbN26NRNqOlpF5cqV5euvZh8NSKPvuwU/yPNjX0ySu6BaRBcsWFAOHjwo/6oVvet69+xhxHA3LCV/RgrdqPvzWXNk/Btvec2AGH9sw/pSvFgxWb1mrWDqddfdfuvNUq9uHRN08NAh6TcwUfS2aTCFO/Lv3rNHVq5aLf/884+NEqwD3q3Lld4+PSRAAiQQZAJBeaAJMmO2nQRIgARIgARIgARIgARIgARIgARIgARIgARIgASyE4GgvBem0J0Jo2bEUyPl2WdHZUJNR6vo16+vDB408GhAOnwrddpuWCy7Yq5fcRCPB/W/UQXg6Kcst+VktNCNet59f6pOrz7dVhlxi+nZMU276xZ8v1BGjXnBDfL1N2xQT/rdeL0ULlzIN56BJEACJBA0AkF5oAkaV7aXBEiABEiABEiABEiABEiABEiABEiABEiABEiABLIrgaC8F6bQnQkjCNN7P/HkCJk06R1dP3xfhtZYqlQp6djxchly82DJmzdvzOrCuudvavvXrl0ve8P6gLWo69SpJZdfepGUK1s2TXWOfOZ5Wbxkqcl7QqsWcqNOLR7uZn76hUx8e7IJDp8ePLk4txxYdk+b/kmSPiANLLgvvfhCaVC/rpvF88Py+513PzAW4F7gEU9p5d6u7ely/rlnh0dxnwRIgAQCTSAoDzSBhszGkwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEA2IhCU98IUujN50KRkFZ3e5sCqOqMdhPtNm7fIfzplefVqVSUz6ox1n/bu3Sd7fvlF/v77HylWrKgR6KO1wv799z9M3r1790qJEiWkapXKMf2oINZ9ZXkkQAIkkB4CQXmgSU8fmZcESIAESIAESIAESIAESIAESIAESIAESIAESIAESOAogaC8F6bQffSY0UcCJEACJEACJBBGICgPNGHN5i4JkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEAaCQTlvTCF7jQeYGYjARIgARIggdxAICgPNLnhWLCPJEACJEACJEACJEACJEACJEACJEACJEACJEACJJAZBILyXphCd2aMBtZBAiRAAiRAAgElEJQHmoDiZbNJgARIgARIgARIgARIgARIgARIgARIgARIgARIINsRCMp7YQrd2W7osEEkQAIkQAIkkH0IBOWBJvsQY0tIgARIgARIgARIgARIgARIgARIgARIgARIgARIINgEgvJemEJ3sMcZW08CJEACJEACGUogKA80GQqBhZMACZAACZAACZAACZAACZAACZAACZAACZAACZBALiIQlPfCFLpz0aBkV0mABEiABEggtQSC8kCT2n4xPQmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgD+BoLwXptDtf/wYSgIkQAIkQAIkoASC8kDDg0UCJEACJEACJEACJEACJEACJEACJEACJEACJEACJBAbAkF5L0yhOzbHm6WQAAmQAAmQQI4kEJQHmhwJn50iARIgARKIOYGDBw/K+g0bpHGjRpInT56Yl88CSYAESIAESIAEci6Bf/75RxYuXCSNGh0rRYsWTVNHDxw4ID/9tFpatDiezyJpIshMJEACJEACmUUgKO+FKXRn1ohgPSRAAiRAAiQQQAJBeaAJIFo2mQRIIJsQWLNmrUyY+GaaW9OjezepVatmmvMzY/QEJr0zRZYvXyHFSxSXmwcOiD7jkZSbNm+WrlddY/YaNqgv40aPSnUZGZlhxsefyLz535kqht06VAoWLJiR1bFsEkgXAY7XdOFj5mxCIL33lWzSDTYjkwj8+++/0rbdWbJp02ZT43fzv5Fy5cqlqvb16zdIuzPbmzxNmzaVD96fkqr8uSnxmDHjZNny5VKhQnm56847clPX2VcSIAESyDYEgvJemEJ3thkybAgJkAAJkAAJZD8CQXmgyX7k2CISIIGgEJj/3QIZcuuwNDf36RHD5fjmzdOcnxlFfv/9d2NlDRYVK1aUsmXK+GIZMHioLFy0SEqVLCkfvveOb5rkAt//YKo8OfJpL8mUt9+U8uVT94Lay5wBnmefHy2TJie+8J7+4XtSrFixmNayYeNG+e233yQ+Pl6ObdgwpmWzsNxHIL3jleMxZ44ZiFJwxYoVl+rVqmZZJzPrvpJlHWTFGULgh4ULTbklS5SU2rVrJalj9Zo1cvbZ53nhzzz9lHTocIG3j3vsqp9+MvtVKlcxAq0XecQz4Y2Jctdd93jB38z9Uo455hhvn56jBLp2vUq++fZbKaPPhd8vmHc0gj4SIAESIIFMIxCU98IUujNtSLAiEiABEiABEggegaA80ASPLFtMAiSQXQis0qkjhz/5lG9z8ELTunp161pvyHbI4AHSsEGDkDDupI7AylU/SZ8b+ppMQwYPlIucl8ZuSekVunFPu1br2b17t5zR5nS5/5673OKz3J9e4TClDtwy7A75dt58KVy4sHwy7cOUkjOeBJIlkN7xyvGYLN7ARp56xpmm7W31GntfFl5jM+u+EtgDxYb7EqhZK/FZ7/zzz5Pnnj36YZxNnJCQIFf1uEa+/nquVNMPOT784D0pUaKEjZYlS5bIRRdfZvYfevAB6dKlkxdnPbt27dY0l8qOHTvkvPPOlVHPPWOjuA0jQKE7DAh3SYAESCALCATlvTCF7iwYHKySBEiABEiABIJCICgPNEHhyXaSAAkEi8CFl1wu+/bvN9NSvjsp7dObB6vXmd/azBQkMO3or7pON6zCs5tLr3CYUn8oLKZEiPGpIZDe8crxmBrawUlLoTs4x4otTUogJaHb5sAHc7AyxgwprotG6EZ6PIvs1+dLlEEXmQCF7shsGEMCJEACmUUgKO+FKXRn1ohgPSRAAiRAAiQQQAJBeaAJIFo2mQRIIAAEKHRnzkHKTKE7c3qUtlrSKxymVCuFxZQIMT41BNI7XjkeU0M7OGkpdAfnWLGlSQlEK3QnzZkYEq3QHSk/w0MJUOgO5cE9EiABEsgKAkF5L0yhOytGB+skARIgARIggYAQCMoDTUBwspkkQAIBI5AWoRtWPh/P/Ew2bNggu9RfUi2Hq1apLGe2ayu1ayVd7zG1SLD+6YqVq2T1mrWya9cuXWO6vNSsUV3OP/dcrevo9Jl+5e7es0c+/mSmtm2jaRvWw65Vs4ac1rq1VNE2puT+++8/M/X1gu9/kK3btpnkNWvWkPr16kmb006VPHnypFRESPz3PyyUb+fPlz2798gXs+eYuEbHNpRGjY41/lo1a2q/zvHyhE9dvnfvXvlxyVL5celS2bx5i5QvV07q1Klt8hQsWNDLZz2YcvTlV1+T3//4Q+rrVPTtz0qcYtfGY/vP4cPyvfYPfLHO5l9//aXrhleQJo0amWOYL18+N3mq/OD3zbx5gnXht27dJoe1LvT3tFNP1env60u0wuGKlSt1zcp5skXL2KPHFNOm4jie2batVK9eLaRNsBob9+LLcvjfwzJr9pdm2nYk6HhF4tSq8He+sqPvuuipqQflJOd+03XYwR6ucqVKcunFF0VMjuP6xltvm/hKum7pZZdcnCQtlhX4YtYc2bxlixw4cEDXQa2g51dNOfuss9RCrnSS9Ahwy23bpo1h75cQbH/QteDhbryuT9Tj2h1fzZo2lVNb/0+wDjXG6KJFi+WQrt2KNV9btjheTmjZ0q/qkLCsPl9jNV7dTqVnPGb0tRXTCc/87HPZomNq2/btgmsIxt/pem077rimEh8X53YliR/tm/7xTFm/fr3g2JUrW1aPdQs568y2UqBAAe/aY8eGLcAdl+efc45U0OvNd3qN+Hb+d7J69Ro56cQT5Po+vW1ys/1Dr2Gffv6FrNR7wbbtO6Ro0SLm/tLi+GZynI69aNxPq1ebc2jL1q1yUGe5wLWjds1aep07Q8srGk0RXprt2oZ33nvP7E+aPMVsMWvGWWe1M/58efMl6YPNnJZz2eb122b2fcVtQ6zvkW7Z1r902TKZNm26bNz4s7k/1aldW5o2bSInnHCC73rQNh+2WLf8ww+nyuLFP5p7ZsGCBaRqtWpmKY9TT22dxDrZ5n333fdkxYqV5nmjj47F9es3yNxvvpG5c7+RvHnzCvLieldJr+1wuLfhmjf/u+9kwYLvzXjCEi+dO1/pa8GMdbFnTP/Y5L355kHmmjt9xseyVO/tqLd06dJSt24duezSS6Vq1SomXXJ/cJ2Zrc8UX371tWzatMkkrVevrjRp3FjO1WeK8GcVPD+89trrJt1LL79itrC0vviiC40/X/58custQ40ffyyPosWKysAB/U04WMyaNVt27Nxpjg8CmzdvJsc3b27i69Wvp/fdy40f94qnRj4tv//2uzRu3EgujnA/RD/mzPlS5nz5lenH33/9LdX0PD3++OZygU6tXqhQIVNe+B/bPjwf4njt3LlLrynfmeOxbt16wZg57rjj5JJLLkrCIrwsv/3w4wXL9Fl6P/5i1izzbNO/X1/DOdp+rl27Tt46cs/v0qWz1NJ7uXVdo1yj2z0v8EyAPtbX56oLdSmc4sWL2+J8t/v27TNjefmKFbJs2XLJr895OC/atT1DWuu4jkvh2u9bKANJgARIIAcRCMp7YQrdOWjQsSskQAIkQAIkEGsCQXmgiXW/WR4JkAAJgEBqhe73P5gqT+rLy0ju4gs7yOCB/dP00uzvv/+WZ0eNlvf1JXUkN/q5p6WxCrJ+LqW29bz6KunauZPkz5/fL7sc+PVXuan/INn488++8VjDHOtrQ7CN1r2iL5ZffjXx5bJfnvB1tF2h+7lnnpJrr+9rXtyH5y2ngvdD99+TZO10iBCnt2tvkp97ztly+61HX1wjEOLUPfc9IEv1RaefQ7mvvjROihcr5hedbBhEhYAK8QAAQABJREFUNFiwrlMRzM/dPGiAEW2tSDT9w/ekWFg9WM9zxNPPmo8N/MpAWKeOV0jfG67zojFu2p19nrfv50Gf3I8w0lKPX7luGF7YX3L5lWYpAIR//NEHUqRIETeJ53/vgw9lxMjENUv79O4p3bt28eJwDB9+bLh8MvNTLyzcg+OK4xvulixdJn37DzTBkdIg8vEnR8jUj6abdF/MnCHRftzgjq8una40H7g89sQIU074n7P0w5dh2s5IZWf1+RqL8RreZ+ynZTwiX0o80nttfX7MOJny3vuoytc1UTFqxBOPS0EVrP3czE8/kwceftQvypxbjz78gFzRqauJx9i44bprvbTuuLxf17R+8+1JglkurAu/DuIDlEFDbvW99iEP1sbud9ONvh+vIP4/FdkefvTxiOcQBOr+mh8fZ0XrFuqHIbg+R3KFCxeWT6Z9GBKdnnM5pKCwncy+r9jqM+IeacvGFtfQobfcJu9FGKe4nmJN6TZ6/P0cxO1u3XvIb/rBjZ+D4PrGhNd9RcGuR8RGpLnrzjvkyk5Hr8luWZ98PE1FylpyY99+8qmeE+EObXzl5RelVavQD31GPT9anjhyrZw96zPTTwjkfm7QwAHST4XUSOLjvn37tX2dZY1+rObn0IeHHrxfPw5o6kV/8+23gj5Gcmj3sqWLvWjLA2L49wvmmfCn9d488unIa227a3HjWNap28Dku/zyS2X44495ZVsPxFdwjtQPtOmdyW9JA/2AINzZ9qGvjz7ysFzQwf/DMsSPfn5UVB86unW4x+uD96dIF2XnjqsHHrhPuul9O5p+otz39Z4/aNDNpoqJb4yXk08+yavO9sVl7UWq5z99Jhgy9NaI5wXy3X33nUbwdvNZP0T76667UX755RcbFLI9++z2yui5iOMtJDF3SIAESCCHEgjKe2EK3Tl0ALJbJEACJEACJBALAkF5oIlFX1kGCZAACYQTSI3Q/bVa8wy7826vCAiH1apVNRa3rnB6TY+rBKJyap2d5tfmg7DcQC2EYDFqy4cQO/7Vl6SIigqu+3befCOy2jCIDk2bNJYdO3aGCNew8u134w02mbeFODVYXyTCMtU69K906VKyXK2tYCVm3TtvT5QKakUUjftMLRI/VwsoWBPasmtUr+69dD1BX4ZfcsSiCuVZoRt+WFrC6hJ9rqeWXr+qEG852HiwcIV7vBCNJHSjD5dc0SmkL61atjB9WaiWaagLLlx0MoEp/IF1Ufere3kiL5JDTIKF2rLlK7w6q1apYsRuxIcL3f/8849c37e/WpqvQbRxOI4nntBKDh06JLCytw5CNwRvOFjW3XXv/caPfthj1fp/p5gwrC+KDxTsmuVprccUlsIfWJaPf2OiSXXXHcOk/ZmJ1p7h2a5XkQTjCm7ymxPUor6il2TU6LHy1qTJ3v5xaskIaz/MUuB+hPHk8EeTWE27gmJGC90Yl7DwhcO5Wr58OWMFaccRwmEt3rlTR3hDXFafr7EYryEdcnZSOx6RNaOvrfiAaNI7iVbItqmwooalqnvOnHzSifL4Iw/ZJN7WHVc2EOPy0KHfvA9bMAbsuZuc0I3zcJ9aR8JhDJUvV1aaqeWlteiG5fTVvft45zHSNW92nDnP3esf6hs3+jlfa80x416UN958C1mNQz11dKaBtWrpaccsIh5WsQpWutE4fMDz4suvmqQ4XnDoi52do6yKTfiYx3XpOZfdcsL9mX1fQf0ZdY90+/bY48NljH6QYR3uHxCVYfWPGWSse/zxR+WKy4/O2IHwn/UDtfMvuMgTI3FNbaLPAJix5Eu1FrYO1qsvv/RCkg9wrNiIdBBYIWqijGMbNjAzlFiRE2GwNp4+fYZJ10Jnr4C1tNs+PBfN/GSGmeXA1usKpxCgMf03HPpYrmw5ncHkW5vUbPvqc8qQIYNDwrCD/nS/6mpjRW4jIQSXLVvGWJjbdiJu7tdzPAv0VatWyYinEj9StAI9BFL0BQ7X7wcfSLyPYt/ycMXXD6d+JB99NM2su21FerS/Ro0ayGKs3rt3S/zYJSUBGDM2XHXVNfL9D4n3dTDHxwH4MGrhwkWeKIv6p+pHccfo85DrbPsQhjQQccG9fv36xrrb8kU8PozAxwepce7xsuUjP/qL2Sv69r1RzlGBOKV+2jrTI3SHnxd2XGLmI3y0Z93YMc9L+/Zn2V2zxawDl16W+KyEAHAGDzw3ztSP6ex4scJ9SGbukAAJkEAuIhCY98I6lQgdCZAACZAACZAACfgS2LPnlwT86EiABEggNxLocPFlCa3btEtQATTZ7qtYadIhLX4qOoak37jx54SOnbt5aT6aPiMkPqUdXIdt2Tfc1D9BX1qGZHn3/Q+8+CdGjAyJ05fgXpxtm4pNXhq0Hf2z5WPfdSoOJ6ilohcP/y9793pJ9MVywjOjnvfih9w6zIuL1qMvJL38arEeMVv/QUO8dGgv0v6r7bNu/YYNIZynfjTNRpmtvnT18j/06OMhcbPnfOnFjXzmuQR90ezFq/ibcN8DD3nxKqR4cdF4HnlsuJe357XXJ+CYgCsctio6efH2OKhwH1L06LEveGkwlnRa9QT0xzqXIeL93NDbbjdltD+vg1+0CYtFPZEK148yvD6gLX5u27btXpp+AweHJHnn3fe8uM7deiTotOUh8XO/+daLB8c1a9eGxOvHFF68TosbEufuPPbEk146FbDcqGT97vhC/TivdDpVLw/G6jtTjvYBaf74808vHp7scL7GYryGdCrCTjTjMaOvrfpRgXescTx0quOEP51jgmtttx49vTQ660NIb3Ta7wScT8iLn1qFJ7jn7vbt20PyI41aj4eU4Y5LxOMa//OmTSFpsKMfBCVg3Nu6VJwPuU6hbe41cuJbbycpA9dEmx/92vhzaD3T9N5k43H/c/uSpLAIATb/3ffeHyFFQkJ6z+WIBTsR7jUxI+8rmXGPnDjxzYQaNeuY32mnn5Hwk17/rUP9kydP8eJbtDwxwb3H69T4Cchj84/Ve0lo/L6EXr37ePE3D7nFuz/ZOrp06e7FoxwVg21Ugn4Yk3BVj2tC4q/o2ClBP5rw0qC9Z7U/x0vzedg99Dl9hrDtw/akk1snrF27zsuPsf/2pMkhaZYuPXptRUJwGDR4iJcGfrcNOK8f0Pu4refqa3p55bseG99Xz8NIzvIA63D3448/enW88cab4dFmH/xtPUOG3hKSBnFqEe/Fox06NbiXBvcktR734sEVx8B1tn22DowP8LFunl73GjU+zitjwYIFNiqqbfjxGnb7nSFttIUk10+bBtv39BnWtvUbvY+7zvbFj7VOd+7lAwd3zKCMSZPf8eKR3+WI+Hv0GmXr1ZkSQs4LnRkg4Yy2Z3nx+iEdstCRAAmQQK4kEJT3wpIrjw47TQIkQAIkQAIkEBWBoDzQRNUZJiIBEiCBVBKIVuiGMGFf7uu0y761hAs2rpjim8EJnPHJTK/82V9+5cQc9UJs1jWQE3TK26OB6tOpmr28i39cEhJnd1xxTS23bbDZ/rBwkZf/tjvuCnkR6Ca8/a57vHThYrmbzs+fFkHipVde8ysqYb6+sLXHAoK161whMlzoxr7Nhxec4Q6Czz33P2iEyu07doRHR9zHRwG2XGzDxVmbcewLL4WkCxeYdF3NBIjQPXpem6AWcjZbyPa558d4ZYTnR8JohMVY1BPSqLAdCP2Whx9niHM2XteT93Kr1boXjvhwEdsmdPOHj2VXUMwMoXvR4h9ts0K27rmybv2GkLisPl9jNV5DOhVhJ5rxmNHXVjznvj3pnQSMS3zs4ufUQtkbe7p+e0iSp1RwsuM1/HpjE+5XEcqmwTY5oTs5cVmn5PbK8ROxUZ/O2JBg71uoCx/pWKfWiV5+fAyDvvs5V4TGRzipdbavkYTuWJzL0bQps+4rGX2PhMhrxThsXfHW5eCKvJ9/McuLGvHUSC9/JPEW12IIgbYetST28sNjxUbEv/b6+JA47EAEtHmx3bRpc5I0X38910vz8suvhsSHC6db9QMSPzd8+JNeGeECMQRS24Zr+1wf8Vnluutv9NKpdXSSamwZkVghg+XhJ76mV+h2OeEDBZwv4Q6idc+evb1+jB49NiSJbR/68kzYc5BNqGuSe/knTJhog6PauscLH0m4IrpbQEYK3eBijxU46TrkbtWe3+2nzojghcNjxzwEbT+HY4nzCiI4zkM6EiABEsitBILyXphCd24doew3CZAACZAACURBICgPNFF0hUlIgARIINUErGCQnEX3jp07PfEAIlWkF36ofNSYsV7ar+bOjbo9X8ya7eWDZXG0DhZ+VnRIKZ8V39Bn17lCTrj1n5vOFctTa7GeFkEi3LLStgUvVm2fw63LkxO6n3zqaS/fh1NDLcFt2WnZuhaSOuV2xCJgOWrbja2fUB0x85EICHa2DL+PGqIRFlOqA/Ep1ZNcGe7sA+EW98gHId/24ZAKc9bBit6G44OOSA7HOJKYnplCNyxvIzmI7LYvEFGtyw7na2aO15TGY2ZcWy375Lbu9em18RO8pLDQt9bc2Cb38dKYcUdnZEhO6MbHLJGctebGNdqdySI8vStUu5bh7n3E79yz5UAct/3CDB6pdXZsRxK6Y3EuR9Mm97hFa9GdlvtKRt8jp079yBP0YMkbycFaVachNz9XDLfW3LDg9fu4yJYHK2srHD744MM22Gxd4XRL2EwaNqHNG0k01KVSvPJhWe06Vzi9NZlZYWDNDGtv1IX+uDNu3H3PfV754Za9bl3Lli330sFKPNzZfmSV0H3HnXd57Zv7zdH7Q3g7d+3a5aU759wLQqLd4xVJAN6oM6zYvj700CMh+VPacY/XLH0+jeQyUuieNm26135YdkdyuJ5Z63WI1q6z5wY44BmWjgRIgARIwJ9AUN4Lc43uXDSfPrtKAiRAAiRAAqklEJi1WFLbMaYnARIggSgIRLNGt7uebu+e10iP7olrMPoVP2/+d6LijonqdU0Pufqq7n7JkoSp4CNXdDpaLtbH7nj5pbo+8wm6/mLpJOltgFtf965d5Mx2bW1Uku0EXTv5U10zG85dH7pXnxu8tWVf07U7JS4uSV4E6Atnufb6G01c+Bq0vhmcQLVClz439DUhWC/6og4XOLFHve4a3V9+8ak2xb8tV3bpbtbUbtigvq5TO8orQD9CiLhG96zZc+Tu+x7w0mI9XqwPjrV2sRZ2Wp27Du3o556Wxo0aRSzqpgGDvLXK3WMQKcMeXXcTa1Du3LVb1q5dK9Omf+yt7+u3RrVd5x39+WTah5GKTRKe2nqSFOAEYP1hnFdwWFv4maee9GKxxjbWMoc795yzBetoW+euK/zc0yP0uDS1UUm2blqUj3rg3LWUM3qN7tNObS0P3X+vqTf8z8JFi8x68wi/c9itcvaRdUOzw/makeM1nENK4zEzrq3hbcK+Wu6Jztogu/S80o97BGs+Yw1qOFxH+/TuafxYy/yCixPXQsaa3sMffdiE+/35UdccvmlA4prC4ddHd1wOu2WInHfuOUmKwFqx5+gay3CtWraQm3SN4kgO686OfPY5E401xXEtg3PPi8ceftCssWwifP4Mu+Mucw3FWt8vjRvtkyJy0KlnnGki2+pat/fdc1eShG470nouJynUJyCz7isZfY901yD+5ONpUq9ePZ/e+gfpB1NyXLMWJvIkHQdvTpzgn1BDMe6bHne8icd60JPeftNL27XrVWadbKxhvGzpYi/c9TRu0sysaRypHrXAlSZNE9e8vvrqq+Seu4+ODXfN53FjR8tZZyWOIbd867+p3wBRkdPsfjlnllStWsX4O1x4saiIbfwfz/go4vMB1vG+8KJLTbrrrrtWbrv1FuO3f2rWqmu8559/njz3bOK63TbObi0Pd41uG4f1ry86cl146MEHpEuXTjbK2+oHWVKnbgOzf7k+yw1//DEvzu3HT6uWm/Wivcgwj5t29U8rvLXVbfuQfMP6NWG5EnfBoUHDxman4xWXy2OPPeKbzi/QPV6z9FmsxpF1yMPTJtdPN21a1uh2z4uXXhwnVapUdosM8auFv+gsA9K4cSNd0/x9L06nLpfXXx/v7Xft2lkuu/RSs3593rx5vXB6SIAESCC3EwjKe2EK3bl9pLL/JEACJEACJJAMgaA80CTTBUaRAAmQQJoJRCN0T5vxsTz6+BOmjgdV2DpdBa5Ibvv2HdKxSzcTffFFHeTmgQMiJU0SPuXd9z3xwo2sWqWKtG59ipzZ9gyBKOE6tRyVR460zQ1PyQ9xGCIxnGWQUh43HsIKBJZoXWoFiVIlS8qH770Tsfgu3a8WnSLc9CFaoVu/X5e79KXnnC+/SlJuo2Mb6nE9Vdop4/LlyyWJTy7g/gcf9j4g+OiDd6VE8eIRkz/82HCZ8fEnJt5P6P5TX0zrtKYy87PPjSCu0xRHLCs9Qnd664nYqCMRw+68W9SS2ey9O/ktKVe2rPG/qi+cdUp64x/55HBpcXyiKIKABx95TD6Z+amJ++DdyVK6VCnj9/sz89PPRC1RTdS9d98p7c5oY/yuoJjRQne4UO+20xU9XaE7O5yvsRyvbp/9/CkJ3Zl1bcUHMAtVHMZ5pWvXeh+L+LXZFbp1zXm56preJlnXzp3k+j6Jfr987rU/OaE70rjcsmWrdO7ew6/oZMNuuvF6uVJFLDj3HEo2U1jkHBWy4iN8VBSW1OymJHS77UjruexXb3hYZt1XMvoeOfjmoaJTJ5vuLV/2Y6o+vFq/foO0O7O9yRsuLofzwv7Jp5xqPp6qVq2qzJmd+OEbwq1w6ifsIh4uNUJ3jx5Xyb3ORxCucAohEoJkJPfss6NEp2M30e+9+440O/IhU8tWJ4nOjBIpm294W703vIQP+ByX1UK37UfNmjXli89nOi1L6r1NP5xUq3QT8e03X+vHKxWMP5rj5YrQ6RG6Z8/6TKpXr560cRri1hEu6LsZ0iJ0u+eFW1ZK/nVrf5L4+HiTbN++fXLxJZcZETw8H8bGueeeq7+zBR940JEACZBAbiYQlPfCFLpz8yhl30mABEiABEggBQJBeaBJoRuMJgESIIE0EbAvsMuVKyfvTjpq3eQW5grQz44cIc2Oi2xp6lrlwYoTIldqHCxB33x7ssDy08/BSvyq7t08UcJtm1/6SGFPjxguxzdPFBmtaBEprV94E31J/XwESyi/9JklSEDQOr1d4kt/PyHyPxW7p0+fIe/oRwXWgjO8vY88dL+0PuWU8OCI+3fcfa98+dXXJv6LmTM8iyu/DCNGPiO6xruJChe6cT8eqIIHrJ79HD5y+OOPP4zAj/i0Ct2xqMevfW4YeIAL3MB+N6kF1cVYUk06qdXgtu3bBR8yvD9lkvcyGulccXzWpx9LctZWrmX0bWohe/4RC9nsLnRnh/M1VuMVxywll5LQ7fLIqGvrP4cPyyOPPu59jBLe5krHHCOVK1eSBd//YKIwYwdm7oDDNeLqXn2MP6XZPGAdftmVnU3atAjda9aslZ5qlZha11MtZ69RURHOPYdSU87nn0xP1qo0vCx7z4hk0e22I63ncnidfvuZdV+x/fVrQ6Sw1Nwj+1x3g3yqH+/ArV2zSvLkyROp2CThy5evkAs6JM4EMHjQQOnXr2+SNG6AtRAOF7SjEU5jJXTPmD5VGjRItHZ222b9uuay3Htk9pW33nxDTtTZFOCsQG3TRbNt2aKFTNaPrVxny8kqi25bfyTLeLetrlXz55/NlFq1aproaI6XK0IHUeh2zwuXSUr+VSuXSYECBbxkeC4fO+4FmTjxLd8PJfDBwQvjxkjt2rW8PPSQAAmQQG4jEJT3whS6c9vIZH9JgARIgARIIBUEgvJAk4ouMSkJkAAJRE0gGqH78y9myb0PJFov3zpksFyg011Gcj+tXi29r0uc3rtzp45y43WJIkmk9JHC9+8/IMtXrJBly5fLF7PmGHHQpu2uUy/26d3L7Lptg/V4y5aJ05LatJG2ZdXCtuCRF4F2GnAIPhBPo3EF8ucXfBwQrcssQSIlodtt79Zt25TxSlm6dJmx9HStp5/QKT5PPKGVmzyiX9f+Fl0f1sS//sqLUrNGjYhpb9Ppgud+862JDxe63WnbMfU4xLLGjY4VWN6VKV3aiMLu9OtpFbpjUU/EDh6JwDT3HXT6cjC108uvXrNGMAUw3NVXdZNe11xt/PaPy3HCa69Ide13JDfpnSny7KjE6ZYxRfMpJ59kkkYrdKdG7HXbEO34imTRnR3OV5dzesaryyWSPyWh2+WRUdfW1ye8IS+89IrXRHx00UI/8qlZs4ZUKF/efJiCqfsvufxKk8YVut3wC847V24derNXTrgH1+obbkqcwSMtQrcrlOMjnau6dQmvwne/uM4gUbxYMRPnHtsXxjwvRYtGZ6VYuVKliNNA+1Vqhd9IQrfbjrSey371hodl1n0lo++Rd951t7zxRuKHdt/M/VKO0XtxtA5LW8BKG+6iCzvISP0YL5LD9at2nfomGkIzBGfrohFOYyV0T5jwmvwvmY/JXB7uVO6nt2lrLHNxT3xd7xHROAieFStWDElqheasErptP5KbJt42uPe114murW52Fy/6XkqUKGH80Ryv7CR0vz5+gtxzz32m7RPfGC8nH7lnIyBSX9xx8KHOloNrXTSuWrVqvtcz8Fily+gsWrxYvv12nvno0S1vwXffCp6N6UiABEggNxIIzHth/yXGGUoCJEACJEACJEACCQl79vxifmRBAiRAArmRQIeLL0to3aZdwiVXdIrY/R+XLDVpkO6Z556PmA4ROvWyl1bFuGTTRht5+PDhBF1D1ivXbeviH3/0wt94861oiwxJ12/gYK8M1JURbsXKVV4dKgpHrKL/oCEmHY5Lcq5ztx4mna4ZHpJMX2R69Tz06OMhccntqMVPwnPPj/Hy6lT1ySUPiXvltfFevtlzvgyJc3fQNhw7jCP8dG1VL/qPP//0wjt27pZwwInzEqnnnSnveenmL1jgRhm/rg9v4tuf1yFJHAJiVY9v4WGBTz39rNdWnWY+YfTYF7x9XRc5LHVCggqSXrxOL58k3g3A8bEc9eMSL2rZ8hVe+MQ33/bCXY+KPQn2vEcZKsq70cn6ox1f7nn58SczvTLd8Kw6X2MxXr0OpeBJaTxmxrXVvb6tXr3Gt8UIt+PphZde9tLgemjDce7+/c8/Xly4Z8LEN720z48ZFxLt9lOnrw+JszsYh7auwUNvtcGp2r76+gSvDJwLGeVsO+++937fKmJxLvsWHBaYWfcVdwxlxD3yuVHPJ9SoWcf89OOPsF4mv4txY/Oe1f6cZBPrGsZeWl3TOCRtly7dTVyLlieGhLs7jRofZ9J06tzVDfb8uga4V76ujeyFw+P28aWXXgmJC9+5/PIrvXL27t3nRaNe29f0HAdbRt+b+ntlh3uS4/GjPnfZMvQDhfCsZh/ts2mGDL0lJI0tG/F79uwJiQvfOenk1l45uHdZZ8tI7ni5bbjlltts1qi27vHauHFjxDxok+3n1df0iphu0OAhXrpvvvk2JF2kvugU9l6ehQsXheSJxc7WrVsTunXv4dWhywfEoliWQQIkQAKBJBCU98KYIoyOBEiABEiABEiABHwJBOWBxrfxDCQBEiCBdBKwgpcrHocXiRfJEA7xch/b3RFeTEIE6Xnt9Z7QsHHjz+FFRdzft29/gk73nDBt+oyIafr2H+iVvWv3bpPur7/+8toGgRT7kdwXs2Yn6PS8Rux000x8622v3BmOKOemgR91Tv1oWgJEy9Q6V5BAfZFcRgrdaDdEpu9/WOhbvU4L7nEAy2idWhF7+XCMIr2A/2ruXC8dxpIrdK9dt86LCxfJ3HZY4RD5kxO6Ee/XjljV47Ypkt8VnV8bP8ETl8M/TrD51TrTY3CDChB+7UdatcT30uH8hfhsHc5N9B0/tZ63wSFblwHSZabQnR3O11iM1xCgyey449XveGbGtdVeu3FtjuTemjTZGzdjX3gpJJmuBe/FTZ02PSTO7uBDGVsPxlT4ORyN0I2yhtw6zKvL72MQWx/OLVzP9+/fb4PMVmcB8fIn96HPP3qv0iUUEnDOuedPSGHJ7NhzDO31c7E4l/3KDQ/LrPtKRt8jlyxZ4oltEHkjOYjgV3TsZH6zZ8/xkvXq3cfL74Z7CY547r3vfi/dm2EfxkUSG90yYiV0Q7z1ux6gLgiaVjjVKdnd6hPGjXvBi5sy5d2QOHdn+/YdCW/pc0YkgdaWn5wwmxwPV+hGm/wc+mfrCRe6X9SPaWzc48l8VDfj40+8dAMGDAqpJrn22YRuGzJK6EZdp51+hmknxgeeo8IdrvN27KDf0QrdixYt9vofztCtA9ezCRMmJuA8cscV/DpjT4JOhZ+gS8K4WTy/WnZ7ddx5591eOD0kQAIkkNsIBOW9MIXu3DYy2V8SIAESIAESSAWBoDzQpKJLTEoCJEACUROIRuhGYc+oxZV9uT9oyC0J/zqWNbayMfrC06aBUJcaZy2Ukf+HhUmF2EMqpFhrYCPuOfU/9cxRy1lYnLsv+mwbZquFrG0bBBk3zfYdO0Li/ASWP9XiGAKlLQMiSWrcFrWcsXnvCrP0csvJKKEb9zpbP/q/d99RKzFbvyuAQtyK1kEosuMIdYz3sfCCAOseY6RzhW4IoLZ9EAf9HER6mwZbP6Eb4pZNg/6Eu1jVE16u3z4svfDBgG2P3Uay6Ef6Hj2v9dJDHA93GLc4t2xZL778akgSnJc2Dtuvvp4bEv/zpk3eeWTTZabQjcZk9fkai/EaAjWZnZTGI7Jm9LUV1tE41jjvIUiHO8wI4IrU4UK3K9qiHIjdrmXl9u3bQz5wQpq0Ct2YEcKOS4xz9xph240Pdtz2rlu33kaZ+1K3Hj29MiCG+zncJ2w9kayy/fLZMHu9Qzvce4mNj8W5bMtKbptZ95WMvkeC1znnXuAJbq+++lqSbsOyGda7ViDdvPnoB2czdSYZG440flbCs3Qs2DQQHX/55ZeQOqIRTq1YmV6LbrRj5MhnQurHDizCrWiKNBCEXbdFx77bh7Vrk97jILReeNGlXjoIoOHOckR//MYv0ifHA6KpbceNffuFF2/2Ua5NEy7S4pph47CdP/+7JGVArLe8kebrsHtZcu2zhbltyEihu2fP3l5/HnrokZCPZw4dOpRwVY9rvHj0JVqhG/cqzFJgWU3Ta6+fu/+BB700rpW++6EAxizKC3c6pbqXd9Lkd0Ki8QyF2WPwnLH/wIGQOO6QAAmQQE4jEJT3wnEAnxvnlmefSYAESIAESIAEUiYQmLVYUu4KU5AACZBAqglEs0Y3ClWLERly6+2ycNEiU0erli2k4+WX6TrVZeXQod9EpyeWj6bPMHFYu/qF0aOkTJnSZj+aP+6aw6VKlpQrtOzmzZrqR8siPyxcJDM//UxU4DBFdbziMul3Y+JaxwhQoU7bNkzXHfzRxGNN5M5XdpQqlSuLCqyyeMkSefOtSSYOf/r07indu4au//rtvPmC9XSt63n1VXJc06ZSpEhhs461CiaiVokmGutHT3l7oq7/WtQmj2pr1zlF4g4XnCdVK1eR+vXryfHNm3n57frRYPDhe+944eGeLt2vNjzs+s82XgUDOb1de7OLdW5vv3WojRJ3fWzkO+/cc6RJ40aCtXG/X7hQPpo2w6wpjQwPP3CfnNr6f17elDwqqMrtd93jJWv9v1OkzWmnSiVd+/bnn3+Wl18bL7t37/bi4Qlfo9tt3+ma97RTW0ud2rVFp9c0Y2DKe++H5Pdbo1st/rz15LHmOtYjhrvkogvNWsTwx6IelBONU9Ffxr34UkjSj3StzRIR1trcsXOn9Lm+r+zbv9/kufiiDtL+zDOlcOFC5jjpdN/eODz5pBPlkQfvlzx58oSUP/KZ58RldXb7swQssJbtnK++NscYY9iuyf7FzBkem5CCfHaSG19u8khrdCNNdjhfYzFe3f5G8kczHjP82jpZ13N/PnE993p168r55+l536iRHDx0UJYuWyFqreuNBfQD10ZcI10395tvzXljwzB+ateqKQcPHhIVvUxw1SpVvGt0WtbotmXjfMF5A4frYK+eV0vdOrXNPQjX4GnTP5Zt27eb+ON1rfGnRww3fvsH47zPDTd55xDGf7sz2kj58uVERXGZv+B7cz+x6Uc9M1KaNmlsd6Paumtwow1tTj9VChcqJKjLulicy7as5LaZcV9B/Rl9j9y2bZuoQCsqQJvuXnLJxXLB+edJ6dKl5Ztvv5X33/9A1qxZa+La6vF86aUXjN/+GfHUSNGpns0u1rAeNHCA1NXx/t9//4ouBSFPPvmUTSpvvfmGnHjiCd4+PF27XmXqKVOmjHy/YF5InN2J1RrdtryLL75InwXOlxIlS8jXX82Vd997z6zBjXj0YeYnMwRrbLsOzyK9el3rBQ0c0F9OOKGVFC1WVNQCWD76aJos0DEOhzWwseZ5+NrO7trPp5x8spxz7tlSVNOCuXUp8bDrbCN9p05XSs0aNaSJnkd27WkVVaVO3QamuMsvv1SGP/6YLdps58z5UtSi3PjRzv79btK11k+SOP23SteRfnLEU+aehQS33DJEbrj+OpPW/kmpfUjntqHjFZfLY489YrOnuB2l18wnnkhc7332rM+kevXqEfNM12dfFZi9+FatWkq7tm1l1+5dug72x6Yf6KN+aGTSRLtGNxJv2bJVLr7kstDzQsdMpWMqGk4Y2zg3rJs86U1p2bKl2dUP+0Q/avDqxZrsuEbhuVNngxD9eEB0ZgCbVdw1uiGjdL3qGu+a3qnjFdL3htBj4GWkhwRIgARyAIGgvBem0J0DBhu7QAIkQAIkQAIZRSAoDzQZ1X+WSwIkkLsJRCt0g5JapsgNNw3whA0/chBAxj3/nL4UrOYXHTHsT30hd/udd4tOLR4xDSKaNztO7r37TildqlRIumjahgxt25wud95+m6+wpxbD8sjjT4SU67fz3NMjjAjuF5dc2GR9oaiWhCFJIFY+/shDXlhGCt065bXc0Le/JwB5lYZ5Lr/0EvNCM2/evGExye+++PIr8tr4NyImghAG8RpiLVy40L1y1SoZePMtIaJbeGHHNW3iCb1+Qreu7S3X9L4uiag+Tj+8gLgPF4t6wtsVaV+t0qRjl25e9Bk6/u6/5y5v388DMadnn+v9orwwCJYYh4VUXAt3Bw4ckO5X94p4nCH+Q2ye+tF0kzWzhW5Umh3O1/SO13DufvvRjMdoeaT12oqPJobcMkxWr1nj10QT5p5XPbp3ld49r0mSNrnrI86tW4YMNuceMqZH6IbAguuwWiMmaYMbUEOFpxFPPCblypZ1g41fLRG9tiSJdALwwRQ+nEqtW7Z8ubkXhueb/dknIR+epPdcDi/fbz8z7iu23uTGgE2DbVrvkUuXLjVit1tWuL9582Yydszz+pFduZAojJuht9wWItyFJDiy88zTT0mHDhckiYpGOI2V0H3zzYNChPfwxkBsnzD+VWnQIFEoDo+f/M4UFX9vCw9Osv/2WxONCB4eobPmiE4RHx4sa9es8sZvSjxeeeU1UUvikDLcDxBckdlP6EZGtSCWW/UjxeRcN/3w5gH98C7cpdQ+pHfbkJFCN8aeWm0b4Ti8ndhvqh9NdrryCrn9jsR7f2qEbuRfqaL0eed3gDdZd+edt+uHQaHX7vAPIyIV8OQTj8ul+uxnHZ7JzzrnfLsr/zvlZHn0oQe8fXpIgARIIKcRCMp7YQrdOW3ksT8kQAIkQAIkEEMCQXmgiWGXWRQJkAAJeASsNRhEyIn6YjUlt0etrV559XX5UK2Gwh1ehPW6uodaUdUJj4pqHy/W3np7srz59qQkYicsUtuf1U6uvqq79yI2vFC07XUVWnVa8fAoY9HarUtnY80YHx+fJN4GzJo9RyZMfMtXFIKFdLcunaRa1ao2eaq3Mz/7XMaMe9ETYiFefTLtaHszUuhGY2FlOH7CRN/jV7tWLbXm7KyWSGekul82w+dqbfbSy696VkA2HAL3bUNvFp22Wy2cXzbBM6dPTSLUQox/4KFHjBW9zYstLDuHDB4oOHbD9IMIOD+hG+EQeh9X6z1d8x27xt1y82BjRW/3Y1GPLSul7Y39Bqjl7HKTDB814OOGlBzE+Jf1PJs3/7skSS9Tq7seV3UzTJJEHgmAuPmsflTx6edfhCS5WvPhHHpy5NNZKnSjUdnhfE3veA2BG2EnmvFoeWTYtfXPP2X4iJEhlsy2ubDexrXtkiPCVyShG+kx+wOuYbDi1mmBjZV0ff3oArM//P77H9JBLQ/h0iN0Iz/KflPvBRD07OwGCIfDNROziXRUK9FixYolBvr8XfXTann19fECa/RwBytsXOtatjg+PCrqfYjYjw5/MuReMeHVl5N85JXeczmaBmX0fcVtQ0bfIyHC6rTeSUTDihUrynk6G8HQITdLwYIF3SZ5fsyOMHbsCzJm7DjPitVGQjQeMKCfnHN24ownNtxuoxFOW7Y6yVjWnqTX8DcnTrBZvS0+4GnStLnZ79Ontwy77VYvLtxCeMOGjTJk6K2epa5NeNlll6o1en+prDPSJOdgRTx6zFhZduTe4qaFsHzD9ddLLZ11IZJbvnyF3Dbs9pD8n878WOro7Alw0fB4X5+1HntsuGd5DavlZUsXm/yuyBxJ6EbCqVM/0nvVKM9a32TWPxD7e/fqKdde28v3mS+a9rltyEihG23G2Bs9eqyMe+HFkLF30YUd5I47hpnr0KBBN5vupVboRiZ8BPK0ztbyedg9HXGwyr/xxuvlfzqTjp/DOfWcPg/o+vVJojGW77zjdmnU6NgkcXZGGlxzIXLjQ1M6EiABEsipBILyXphCd04dgewXCZAACZAACcSAQFAeaGLQVRZBAiRAAjEjoOtACqxV/1ABJW/ePIKX0JGmY05tpRA5du7aJbrOpnnBiSkx8QI1WgfBfOeOnaJrCmr+eKlQvryUUau/+Li4aIswU55jqm28vMS0nxUrVEgiykZdmE/CvXv3iq47LmX1ZS5eIma20zXHjeit65GZ41ajRnVfK/e0tOs/tW7C9MHoY758+QTHL3/+/KkqCuNr3fr18tdff5uPFCpUrJCq44fK/tFxtG3bdimkogim2I/zOf6xqCeljl3dq4/pC47zNJ22PDWW8vv3HxBdH1en3/3PjD9MVxpJ5PFrB170Iz+OA8Za+DTnfnkyOyyrz9dYjNdomEUzHlFORl9bdZ12vbb+Yixiq1atIvlSOXNDpL6uV+GuR8/eJtpv+vNI+ZILx/jFxzl4Vsf5W6pUSamg1+LUtFnXPdbr0U5zvcVSFOXL/Z+984CTokj7cJn1jGfOimLEnHMWMYsiJiSIAiIggkTJQRAQVBAQQRGzoJhQxOx55pxFz+wpZkXP87z7/Orfa/XW9HbPzO7M7k7PPvX77U5Pd3V11VPV3TXvv+qttc1q1lV0sYKWAPjKvitWW3W1rOkWei/nk9+6fK9oWZDafEfKhbmWrbDLd1vRd/0qM7iz8dB7W+f++ONP5r//+2/wDlnPDparzxAVuuUKWzOBJXh/+umnZnW71IsG0a26avXaptq23rfy0rGavT+0ZEt1+hRyp633pJaakZv4mgS1Ay1jsM46a1err+auJQ523W6zaNFXwa7VV/9rIPRX513p0iqFT9XJb7/9O3hWVed9nU/eNXBK7sz1XNPyOeuvv17e9fb99z/Yuv5n0DeTa/wN7QDXXH0C9RUVR30IAgQgAIFyJpAWuzBCdzm3QsoGAQhAAAIQKJBAWjo0BRaT0yEAAQhAAAIQqGMCz73wgunZq8LF7Cl2jdAudtYVAQJpI6A1sRc8+JBp3eqMwKtCXP7vtTNML7EznBV6XnC+OcHOZCRAAAIVBOKEbthAAAIQgAAEIFAaBNJiF0boLo32Qi4gAAEIQAACJUkgLR2akoRHpiAAAQhAAAIQiCUg19wX2DXH5eZZIc6tceyJ7IRACRF4+plnTe9+FwU5kovzHtb9c3SW4ocffWQ6ndctXHLimmlTa7yERQkVnaxAoGgEELqLhpKEIAABCEAAAkUnkBa7MEJ30aueBCEAAQhAAALlQyAtHZryIU5JIAABCEAAAuVLYMDgoYFrUbled+GIpoebAf0q12t1+/mEQKkT+My6gO7c5fxwrey11lrLHGDX5NZyB3Kp/9bb75gHFjwYFmM/u07sqBHDwu9sQAACxiB00wogAAEIQAACpUsgLXZhhO7SbUPkDAIQgAAEIFDvBNLSoal3UGQAAhCAAAQgAIGsBLS+ctOjMl02N9l2GzNh3JiirvGeNRMchECRCWgN4M7dLgjWZM6W9BmnnWrOPqtttdahz5YexyBQLgQQusulJikHBCAAAQiUI4G02IURusux9VEmCEAAAhCAQJEIpKVDU6TikgwEIAABCEAAArVE4LvvvzfXzpxlfvrpJ7PKKquYHXfY3hx80IFmqaWWqqUrkiwE6obAT4sXm3nz7jePPv64+fiTTwM35X/5y1/MFo03t3+NjWZy77rLznWTGa4CgZQRuPfeeebOu+4Ocj1yxHCzzjprp6wEZBcCEIAABCBQvgTSYhdG6C7fNkjJIAABCEAAAgUTSEuHpuCCkgAEIAABCEAAAhCAAASKQOD3//7XLG0HcCyxxBJFSI0kGiqBq6+7uaEWnXJDoOQInNPmtJLLExmCAAQgUBcE0mIXRuiui9bANSAAAQhAAAIpJZCWDk1K8ZJtCEAAAhCAAAQgAAEIQAACVQggdFdBwg4I1BsBhO56Q8+FIQCBeiaQFrswQnc9NxQuDwEIQAACEChlAmnp0JQyQ/IGAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCBNBNJiF0boTlOrIq8QgAAEIACBOiaQlg5NHWPhchCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEypZAWuzCCN1l2wQpGAQgAAEIQKBwAmnp0BReUlKAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABEUiLXRihm/YKAQhAAAIQgEAigbR0aBILwAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCoFoG02IURuqtVrUSGAAQgAAEINCwCaenQNKxaobQQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABGqPQFrswgjdtdcGSBkCEIAABCCQegJp6dCkHjQFgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQIgTSYhdG6C6RBkM2IAABCEAAAqVIIC0dmlJkR54gAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCKSRQFrswgjdaWxd5BkCEIAABCBQRwTS0qGpIxxcBgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA2RNIi10YobvsmyIFhAAEIAABCNScQFo6NDUvIWdCAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEICATyAtdmGEbr/WanH7f//7n/m///u/WrxCZdJLLrmkWWqppSp3sAUBCEAAAhCoIYG0dGhqWDxOgwAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgQiBtNiFEbojFVcbX2dcc60ZP36C+de/fq2N5KukucIKK5iePS8w7c9qV+UYOyAAAQhAAALVIZCWDk11ykRcCECg7gl89vnn5lfbF95ii8Z1f/GEK/7+3/+aN998K8jTin/5S0IsdkMAAhCAAAQgAAEIQAACEIAABCAAgYZHIC12YYTuOmibu+y6u/n++x/q4EqVl/jrX1czL734fOUOtiAAAQhAAAI1IJCWDk0NisYpEKhTAhOvnGK++eab2Gsus+yyZvNGjczWW29ltmi8uVlppZVi45X6zhtvvsUsXPieWXPNNU3X884Ns7vgwYfM8ItHB9/POO1U06nD2eGxQjb++OMPM9YOJv3l51/MTjvtaJoff1zeycnb0ulntjX//OKL4Jy7br/NrL766nmfXwoRL7tikv2N8b3Ze++9TLOmhwdZmjptuvnClmnnnXcyJxx3bJjN7777zlw+8crg+/777WsOO/SQ8Fg5bfzyr3+ZMWMvDYq07z57m6aHH1ZOxUtlWRpKndw//wHzzLPPBXXUr08vs/zyy6eyvso50998+62ZOGlyUMRTT2lptrHv3F9//dWMHjMu2Hdi8+PNjjvsUM4IKBsEjOuPbrXVlub0U08pSSI//vhj0GdRX63ZEU3N3nvtWZL5LKVMNbR30HMvvGDmzbs/qILzu56Xuj58KbWdQvKigcxXT78mSOLkFiea7Zo0KSQ5zoUABEqUQFrswgjdddCAtthyG/NfO2OkLsPSSy9t3lv4dl1ekmtBAAIQgEAZEkhLh6YM0VOkMiNwxNHHWe8+/8qrVJ07dTCnWSN82sL5PXqZl15+2fx1tdXM3XPnhNkfNHS4efSxx4Pv66+3nrn1puvDY4VsaFmgAw9tGiRxZLMjTH8rLuUbPvzoI9O6XaXgPmTgRebQQw7O9/SSiOfa1CkntzBdOncK8tS2fQfzjw8+MIdbIXvQgP5hPj/44EPTpv05wfeWJ59kunauHIgQRiqDjR9++NEc2/ykoCQy4J/bsaLMaSua2ucvv/xitCTVtttsk7bsZ+S3XOoko1AxXyZOnmJum317cOS+u+ealVdeOSYWu2qLgN6vH3z4YZD8uuuua9ZcY40ql/rk00/NGa0rvN6NGjnM7LfPPuanxYvN0cc1D+L2ubCHOeboo6qcxw4IFINAqTzXXd9Bg8FGjxxejKIVPQ2/z6KBky1bVLzXi36hMkqw0HfQ13Yw7qJFiwIijRs3Nssvt1y90XnjzTeDa6+88ipmk403is3H9TfeZKb9KbDefMN1ZsMNNoiNx87aJfDa62+Y87p1Dy6i32H6PUaAAATKj0Ba7MII3XXQ9pzQHRWfX3zpJdOiRcUIyv3szIrrZ80Mc3Ol/aE8btz44PuF9gfXeZ4x6szWbc2TT/49ODZnzq1m1112Cc9LulYYoYYbEupffuU18867C4O/L79cZGfrrBHMPNp6yy3MLjvvaJbLoyP0gx2Z+dzzL5r3//GBef/9D4xGaq6/wfpGaWhE9c477WCWWGKJGuayfk+7b/4Co5e8wvbbbWeOPrLC8Fq/ueLqEIAABAojkJYOTWGl5GwI1D4BZ1jUlbbcYouMC/7888/hzGJ34Ky2rU27Nq3d11R8JgndmnXRs1ffoAzdu3UxJzU/oSjlKUTo1mzwnr37mudfeNFIfJ9+1eTUCVOuTSF0VzanchFVe/e7yDz9zLPmL9al/gPz7q4sYAq3yqVOcqEvVGTIlT7HsxN4+513TYdzzwsiXdijuzn+2GOqnIDQXQUJO+qQQKk8113fAaG7Diu/Di5V6Dvo1tlzzKTJU4Oczrp2umm06aZ1kOv4S+x/cIU3nkMOOtAMHTwwNhJCdyyWOt+J0F3nyLkgBOqFQFrswmUndMvg9d133wcibL3UfMxFk8TntAjd//7tN3PJ2Anmw48+jildxa5111nHXNTvQrNyFlebEskvvWyS+f333xPT2a7JNqZ7185GgwLSFlS219+oGHko4b/beRUza9JWDvILAQhAwCeQlg6Nn2e2IVCKBJxhcdNNNjHXz5xRJYtyz/jwI4/avtIV4czv+jY0Vclkjh1JQrdO+/e//23+Z/vpxVwLuxCh2xVFLr1X++tfzZIpHGjp2hRCt6tNY8pFVC0VQaSSbM23yqVOchEoVGTIlT7HsxNA6M7Oh6P1T6BUnuuu74DQXf9topg5KPQdhNBdzNpoOGkhdDecuqakDZtAWuzCZSd0a+Zx3wFDTL9ePcwaa5TGOntpFroX2xlGIy4eYxZ99XXOO3oV655t8IC+sdwffvRxc/2Nt+RMQxE23WRj63qyp1nWrleZpoDQnabaIq8QgEC+BNLSocm3PMSDQH0RcIbFJKHb5Uti95DhI4OvSbPSXNxS+8wmdNdGXoshdNdGvuoqTdemELoriZeLqFoqgkgl2ZpvlUud5CJQqMiQK32OZyeA0J2dD0frn0CpPNdd3wGhu/7bRDFzUOg7CKG7mLXRcNJC6G44dU1JGzaBtNiFy1LoPrtT12BGd6mI3WkWuq+afq11m/dceDfvvdcepkXz483qq//VqJHfcdc95qmnnw2P77B9E9Pj/C7hd2189/33pkevyjUCl7JrzZ1i1wbcd589A3d8n//zC3Pd9TeZ997/R3hepw5nmb322D38noYNhO401BJ5hAAEqksgLR2a6paL+BCoawLOsJhL6P7NetI5rNnRQfb8dZZ/seuPXjPzumD/Buuvb0484fjEImiW8o233Bocl1vumrgK//XXX82DDz9i3n77HfPNt9+av6ywgtl2223MVltuaXbcYfvYpWaShG4/P4ccdJBpYtOJC9W9Zj5CtzwT3WDX8fvVzihX2H/ffc1OO+4QbM9f8KDtf75vZ5mvaOQq3gW5NRfrf1kGO+2wg9nfLjGktTVffe118/LLr5if7drJm2++mdlt113MHrvt5k5L/JRXIw1g+PSzz8x//vMfs/VWW1oG25p99t7LfG/7ya6ujm7WzGy2WaPEdKIHXJuqbaE73/p76ulnzIt2jXaFzh07mKWWWirYLjbPr7/+2mjJoA/sWuRaU3KtNde0dbGrOfzwQ82/f/13Xmt0q+3INbhc13/+z38G+WzUaNOgfR90wP5h3oMD3j/XZtZcfQ1z2qktzXvvvW+eee558+xzz5lf7bWHDh6QsU7k73YQ9gv2GgttvHfefdfo/l533XXM9k2amMPsOurLLLNMmLq8Omi9yf/+7792TfsnjMqpoDXVXTjtlJaxaw+/9fbb9jfZM+azz/9pvrFMVl11VbNZo03NYYccYjaxg4jjQrQsus9fffU184r9+/iTT4Lztt16a9P08MMSebh0i1EnLq2kz6/swOsFDz1sPrP30T+/+MIsv/zywbIDB9r62tHe03FeGfy2q/tLLN5+5x1bxtft/fxaMLC6sb2XD7buUfVszhbUZp565hlb18+bzy1nDbDXs+yA/fcPluAqVGTQtXWNJ//+VOAl7LPPPzeLFy+2do01g+scesjBZnXrfSJXeHfhQvOIHWSu8/W82WTjjYP8iVHcutV+ev59Ica6h7eyS4xpuY1ddt4pYO7Hj27rfpz/wALz4Ycfma9s+9Va2WqHB+y3n9lww/j1U6N1pGeglrt43S4JpmenljXbaKMNzcEHHmC2s/dNNLzw4kvm6WefNd98/Y155LHHg8OqlyZNtg22N2vUyC4p1izYLpbr8mK/jxe+915QZ3pHaGm3day3vM0thyMOPzx2EoEK43PL9l5Nei4HQPL8p3ez7r1/2OXn9LzUM13tck9rr5FYmmstX53/wIMPmbdsf+LLL78M2pHew4ccfJCRZ8Dos8hlyy+j7t917LPzOXv/Pf3sc2bhwvfMXnvuYTp1ONtFDz79NlydZ7ufiH8P6R7Uc2PzRpvZZ/bBZqUsHgz9NNx2Ic91PVfnL3jI3k8fBvfTaqutZjay95HeHZtvtpm7RLU/Xd/BCd26L/Q+fOutt80PP/5kn6vr2iUFdwyei/47KulCNbnvk9Jy+z/44EPTpv05wVe3RrfeTa+8+mrQjtQGN7BLIOrZomdTrqB28YxtN889/0LQhv/zn9/t+euZ7Wxf7BD7bE1qw1or+tHHnwiSP/P0081qq60ae6nbZt9uFn39lVl/XdvnPrFyeZ5oGy7Fd9Cdd98T9E/V33ceKvVOXGutNYOyHmD7wDvavnA06L5+6OFHbX28bd/JX9qlLJc166+/ntl7zz3N7rvvFvtOjqbhf//CpjFn7txgl3gq/NW2efXtFJZZepmM+z3qulzvt9feeCN4d7xrnw8r2D7CZvY+OaLpYcFzJkgky79C7ns9t1946dUeN44AAEAASURBVOWgT6jnuTyUioX6+bvb/mmu5TkLuXZSkaLP1aT+aqFtPF+hW+3F/a5Ue1lppRWD59iuu+wU276i5VI+9Q5Rf/qrr74ya6+9tmm06Sb2/X5k4n0ZTYPvEIBAzQmkxS5ctkK3qk5rSJeC2J1WoVud8vYdK0VrvYC6du5Y5a6YNmNmhtg9ferEDNfj8+5fYGbfXtFh0cmDLuoT/OD1E9K1evcbZL61hlmFPXbf1RrIMn+0+PFLcRuhuxRrhTxBAAKFEkhLh6bQcnI+BGqbgDMsSkyJc13urv+TNaoefVzz4OsxRx1p+vTqGWyrr9S8xSnm+x9+CL7Pv/cus+KKK7rTMj7n3nW3GW9doCt0OPssc+YZp2ccz/VFRqsLLuwTulCPxpcBTPmKuiFPErrzMYLU5JoyXB54aNMge0c2O8J6BOqVkVWJiv0HDg4Mmzogo+7wIYNCcTEpv366p596SmBYvmTc+Iy03RcNRuhnr5tkDL70ssvNnXfd46JnfEpAPPKIppZ172D/mFEjzd577ZkRJ9sX16ZqW+jOp/6UzzGXjjf33HtfkOVHFtwfMikmT3/GUZSNxLj+fXuZtu07BIdUd+d2rDCU+3F//Okn06XbBeajjz/2d4fbSkfeFLbZeqtwn9twbUZx2px5hrlo0BB3KPi8bsbV4WAFGf8HDx0eGo4zItova621lpk5Y5qRVywFiZKHHnFUsJ30T/F9gUOi0fjLJwYiRdI5p7Y82Zx3btXfcH5Z+tr7+awO8csuqawjhw0ORMu4axSjTuLSdfvEZfLUaeb2uXe6XVU+t9+uiRk/bkwVscJvu6NHDg8EiwfsAJe40KVzJzsYu0XcIWtU/dr+Vr3I/MMOrogLPS84PxAKnHH+vrvnmpX/rNe4+HH7Xn7l1eC5ndQudc7IYUOssL5f3OmBSH7xJWNNUvm03vvQQQMCcTAugVz3hd5do0YMSxSs9ZzT8y4paDDRGaedWsVrm19HSv+Fl14yt98RX9d6hg8e0N+sYAdeuXDtdbPswKRZ7muVT72vhv25xmuxhO5ivY/1bMxWZyqM3mt6v0WDzy0pjs5Jei5H00v6rnqdMu3qxP6AhKjJEy9PbBcaJNbpvG6x56tN6r2n+nvJDpLSs2bGtClhVvwyqg5vvvU2O1Dl3fC4X7famasNZ3u26/z/s+LpxaPHJN5DKmu3Lp0DoVnx8wk1ea4r3Vz30wnHHWt6dO+WU0CLy6PrO+h+OuboI02/iwbFRQveUWNHj8x450Qj5spn0n0fTSf6PUPo7nyuWfzzYjNz1g3RaMH3XXbe2YwYOijxmSsRssv5PRLf+WqHkydeFlvOqJi64QbxA3ZObHlaMDhNA0EnXT4hzKffhkv1HdS56/mJ/RQVRO2s+fHHhWXSRq4+u+61y8ePrdbAED0D1C9JCqqnB+bdHR726+ba6VeZMbaf7j8fwoh2I9szstD7XgJs/wGDw99n/nW1rXf2iKGDY+/VQq8dvZb/3e/jZeuv+hxvvuG6jMGafnr5tPEkzrnai9Zi72qfrXGD8fQMnXjlFKMBGUlhyqTLYwfCJcVnPwQgUH0CabELl7XQrWorBbE7rUK3RmFLfHYhTqDWMa3dPXTEaBfNXDx8cDAK1O247oabzRNPPBl81WjsIQP7uUMZn7fOucPcP7/C8KDRXZMuG5dxPN8vWv/xuedeMC/bEZ8ySuh7YzsiWqP1Nbo7aRSmS/9f//rVPPbE3+wo4ffNN1Z41+i7te1oxu3sufvtu3dotHPx3Wc2oVtC/3ffVximDz5wf/sjrrE7Lfz8wXbAb519R/i97Zmn21GRywXfNUPkhptvDQwY2tGhfdvgh5xmI8goIpeAO+6wXTDyVqPaXPjk08+CTuubb70T/LgU/wP33zdg4eK4T9XjgoceCb6uZ0dLH3fMUcF69xrV+aodUb9o0VfBqNktG29uDj74QKOZ+UlBs6c04vbDDz8O2scvduaTlhLQCGQZhHPVQVK67IcABOqeQFo6NHVPhitCoHoEnGExl9AtoUQzAxV69+xhjrXvYxc021MGCYWBF/UzTQ+rmOXgjrvPTud1NW/amTkKs2++IVGgcvH9T82oaHt2h9AoLcPSdk22DWZwafarCxttuGFgkPYFB2dUkTH47rlzXFTjG/rijCA1vaYvoEaF7t9//z0QITVLSUECskQiX5BOyq+frsRIzapSkOFu7bXXsjOJPwxmlAY77T/NXtbs3mi4+ZbbzOSrpmXs3sPOcvnss8/D85WmZn8oVFfonjR5qp1F/KvZZZedzaF2ZpyC2o9m42691VYZbcc3GmuGcFdrOM435Ko/l06SoFKbPGVU/vnnX0IBUu1SsyIV4oRuGcx69OoTzM53+ZZwLG9Vumf+ZT0nuDDn1pvMOnbGiB9cm9F94ceVKK7++rgxo4JzdKz5yadmxNl9t12DYy9ZrwCaLavgCzWaITxwyLBgv+K49Pfbd59g35K27y0BXveXgtq4BCTXfrRP+dIsy5/t8lP+/SqhW4K3H1xZtE9pahCNPEBoRu23dna3bzDWzMmxoy/2Tw+249p4deukSqKRHTJw3janYnaXO6T8aMaUz0n3uO4hP/htV2Vz3JVHsZKXBsdZ5029cmLwe9FPQyLJmW3bZxixxWsL+3vujTffCs/32151hW7lq93ZHcO0dH3lV79dP7Czo32BPcmgO3XadHPjzZVLhelds/HGGwWzq909oXT79b7QHPXnDGd9V9B90b1nrwyxo+J382rBLE83wErMJl02Pih7xZkV//Wc1UAAFxRvh+23s7N3F2WIS3HPHr+OdC+6surZq9+1PmOlr0ENE20enMeIhx5+xDz86GPB7HfVp4LK7maQ65nrRBr9Jp169XTzf//7PyOhUHUorwtXTplqfv/P7+Zw+051Hj+ChLL8K8b7+MopV5lbbpsdXkXtcvXVVw/qzB/wcOnY0VW8h/jc4t6rLtGk57I7nu3zib89WWUwj9qFnouv27bv3o2qq2uvnhp4kvDTk2e/szt2DuPpmOpYth5XV2orq1kPFLoH9D5MErrdM0pp6Hqyz+y0447hDM9Cn+1KN3oP6TqyIb1vZ7K7sirexcOHBp5etJ0rVPe5rvTk1aHfgEo7nJjpXpa3DjfjVvHatWltfG802pdPcP1Rn6nqQf0GDZ5yz0mlpf1XT73SbLzRRlWSLuS+r5JYZIffZ1E9OP7K81bWK47av//sFqNpUyZVGUgju1QPO5jQcXPPJr0/3rRt2D3blO70q6YEfTw/K8UUAUv1HXSVfSZ+9PEn5mP7594VmtHvBtOe2rJFxoxbeQtp3+HckL/qR54/dA9qxrwL6vNcYt/Jy1jW+QQ9+6dfMzOIqntAQfXSpEmFdw6JoBpU5oJfN+79q/rVM0o23Oi748ZZ18a240Lue/Ub9ZvLBV1fnkmXsctw/u3Jv4eMenY/35xw/LEuWvhZyLXDRBI2XB9PefLvlWh/1edYG0J39Deesqv2pWejuy+1T89/3cPu3a59Cm7Jh4pvFfHkGUuDqNz5aoMaRB4dgO3O4RMCECicQFrswmUvdKsq61vsTqvQLaPRnXffGxg2xbFli+ah8KrvLnz8yadm8LBKw8fY0cMDF4LueL6fWsNba3kryC3b+LGVaeabhm68wcMvDgxecedInO3apZN1Q7l93GH7g+sNM+GKK2OPaaeMowP69bIu4Kp29LMJ3eec2y0wRimNNq1Os0atA7SZEeSabfTYytGfV14+Luxcyr37RYMqDF86SYz7DxwWpukn5NK/286ouePO+FFvxx7dzLoxPd4/zSx48BFz060VP7Q3sG522lih/eJLLs2I477IRWTvC8+PredPP/3MjB1/hflp8WIXvcrnqS1PMs2aHlZlPzsgAIHSI5CWDk3pkSNHEMgk4AyLSUK3jO132pnYV0yaHJwo445mb8ro7YIM3xJcFOJEHe2XQaHl6a20GRgSrpgQ/y4PIkT+yUVnx85dQ0OXZoK3b9cmNDroedBvwMBQAIsKJs6oorznK3QXck1fQPWFbrEcZAVDZyiTKCaRe1lrePJDUn79dBVfBpRhgweEswU0A2Pu3LvMZRMnhck9OH9exmxS31AtI9NwO9to5512Cg1+L1oXh5pt7hufqit0hxfPY8M3GseJTdmSKFRQKQZPfyam8tq9W5dgoIebNat237N337DtKk5U6P7D1ttIb7beEU0PN507dQjdQctIe9X0GcFgAZ0fJ+66NqPjCsqHBLOoYe5xO2h2wOChQZwWJzY3Hc9pH7p9lnFPswblwlFhyMCLjNxS+8EZ9tR2/BlMfhzfQCoD+rAhA80WjRsbCeIK/prFOn7rTdf7pwczpzSDygWJZc2shwHnYlODVvv0HxC20SuvuCwQL138YtSJSyvpU65me/XtHx6WwCTh0g0GlltY1YkTBefOuTVjRpDfdpWIZv4N6Nc7uKf1XQNFLrXeL9wsaB3XLDQ/jB4zzsy7f36wS4ZYzX5vbAf+ipPalGaZTrnqav8UUx2hW8+TTp27hM9Vzf7SwATVmQv+rEmJtpph54e7751nxl5a8TtS542ys9c3a7RpEEV5lDtxzRxW0PNZnFyb1fVHjBwVtke1+0F2IJW7t+QZQ4Nq3Gwq3TcD+vUJ0tI/uUP1vQFMsDPrZcR26UsI0AAOJ1RFBxNE60hpama3G+DhjOF9LxoYtkXdd9ElOfz2rgEhxx97jJKqtVDo+1geCi67ouIdIpFmjJ05688Wlctx3X8uaMZi4803d19zDiBzEWsqdMsjRas2Z4XMzz6rXSDUrLrKKkHSel6OsW3O3Tuaca2BOy7oeLcLeoaD7uShpk3rVqELYQmQo+xz0Lmb13nZhG4d1yCHvnagRlR0Lcaz/d559xnnuUX9tBG2z+DbfO6zz4BR9lmgoHtIopm7R4KdefzL57keFc50P2mZFBckRiodJ0aLh3PN7+Lk+nT9URdP95Lcg7t7VvY9Df5wAwV1P18+flz4btB5hd737tpJnx98UOm63MUZ2L9vsJSGvsurgpahGDR0RPhsUX+1beszXfRgksiQ4SPtUiAVNkbNGtWzwdWb+oo33nSzmXHtdcE5qncNJPLd0xdTBNRFSvEd5ID53llmXTvdNNp0U3co/NTArw7ndgnbnwZ6qk/p2o68Koyy75q/P/V0cI68FsnrketXhAnl2Nj/4Apboeps6J8eOaKn+HWjYxKSz+1wTjA4Q99lj1Rfy+XFXw5KxxUKve/1O8B5IFH7VF/OsdD1O3m/qW65cZbR0lMuFHptl07SZ779VZ9jsYVuDbpUe3EDKOQR49ijjwr7w1oyZ/jI0YFHD5VDfXIt0eOCfnee0KLiu57/mhnv/y72PZipL+4PhHBp8AkBCBSHQFrswg1C6FaV1qfYnVahO99b4f4HHgxnIktInjEtWShOSvNHuxbQhX0HhMLt7rYzf965VV0NJp2v/RqhfdGg4XZdoR+zRQuOtTqtZbDGkh9Ro+VHjhobzAD390e3JXZfPHxQFZG3LoVuCc368ZkUDj/0YGuseDTpcLB/YP/edkR1ozCOL3S72U6aKZIUNMJ+8IC+GYejgnzGwciXNK7DHikCXyHQIAikpUPTICqDQqaagG9YlJDgB4m9Mm66IGOvhIa4tXU1i8LN4Lxn7u1VvKRIdJGbXwWJERIl8g2+C9gzzzjNuj2vENX9833jftQo7Ywq1RG6C7mmL6A6oVuiyOBhI4xmoilIFJM45oQxvyxJ+fXTVXzNHoyb5Se31e46110zPRSWdM7pZ7YNDTsymB9oxato0Nqyzm25jjUUobsmPP31j+OEPvGTh6Njm5+kzSBEhW7N/j2/x4XBMYloMpg5g+SfpwQffr1GRTnXZhQxm6triYr3z38gSC/uPtU9L4FVa3Xvaz1GaZ1aP+QjiEgI0Lq5EoNVFjeD1U/HnzEaFV/9srRv19YKBBUDZPzzfTFOBkQZEl0oRp24tJI+1QfR+vYP2PV9W7c63Wg97miQEVsiqEL0HvJFVA0auM2K/Vq/3A///ve/TcvTWgUz+6IDCzQj9fgTK2fCJxmB/dm9SjvK2r9edFsi3fMvvhisdaoZmxdbkTduvVi55NeMt+hzV+tFNzu60q3sXXfMDgdv+NcaMeqSUJT0OfnuYqOzpd35EiXbtDs7FDf8Nt3l/AvC2bly2atZydHgi2J6Jl86ptIbnF9HOi8qmLq0tDb6hX0qPMPpHaNy+uJJXQvdyldN38eyWzQ7pnLQeVTEdmX23+fZuNXGjG7V+RN2wI7uPa1zHrf8gUStY44/Mchu9Hn7mF3X2HmoUJudeuUVGR5VdJIGWXTr3iNsP9G27beNbOJyoc92DThTH01BA0Xkil3e6KLBfx5qWQyVuTohn+e670Y6+sx114qK4Q/ZgXZxfRwXP/rp90clzGmwVTRIpGvVul044/nqqZPtjO8tw2iF3vdhQgkbUaG7V88LrMfBo6vEltvoc7ucH+zX8/veu+4IBxT6fSzVq+4zxfGDnr8aUOJEfa35riUWXCimCFiq7yBX1nyEbg0KmDmrYtBckggdbTsaPLCd7etUJ1RX6NZ75wrbV1/SDkDzg98v1ICim66fGR4uxn1/XPMWwT0STdtdRO8lPTf2sLPb97X9TjfjuBjXdtdI+vT7eNn6q8Vs49F3kdqKG0gSFbFdvjXg8JTTzwyfNY8+OD9cClXrjI+0fReFpN9S6ouuZJfy2nuvvTKeUS59PiEAgeIQSItduMEI3arW+hK7y1noXrz4Z9Ojd/9QoN5h+yamx/ld8r6LvrfuvN+2M5lvsq4dNYPchdEjhlg3m5kGH3cs7lNGzSEjRgeuIN3x/a3R6ID99w0MWE8985x5/Iknw3xKyJ06aUJo3Ppy0SL7Q2xkxvEDD9jPup3ZLdj39LPPW0Pm313SwVp6I63YvfJKK4X76lLo1kXlhvxUO3ryX7/+y45S/ZtdJ+edMC9u4zAreO+z1x7WKPGleezxvwVut9yxnXfawZzfpdJtpS90uzhbbtHYHHNUM7PqKiubdxa+Z2e43JExEKBXj/Ota6CtXXTju5/XTg0o2Hln6/bI/qCQm3W5X3f1XNNZ++HF2IAABOqEQFo6NHUCg4tAoAACvmExWzIyhGn252F2qQ+3dq8f3x+93ufCHnZ9xUrX5ornxBBtz7dr2Tmjir7nCjI0aJaQ8jDnlhvDWS/R895duDBYQuUvK6yQYbxyRpXqCN2FXNMXpCV0i8ewEReHM8TkNjFJNFKZkvLrp5tkvNL5EjLdLEmtvaj1LhU046pV27OCbQmqmp2YFDqce144k9MXn5Li13S/bzSuzxndNeEpQ7TERrkYVduUwBUnBIqNXMXLnbZCVHjR7EkZHBVuuO7ajNl6wc4///miXHS2nGszijr/3rtC70v++doeb0Vs3asK0SUIgp05/uUjiORIIjjsL4UQFSH9stw557ZYYUcuSk9r1SZIy1/ru1h1kk8ZcsXxBc5z2rezgvgZ4Sm+UKZZi5oJHBf8gQnz7Pra7tnrz+LMZijWzPLjT6qchVQdoTsuP3H7XJvQsUesIdi5g3340cfMEDu4RyE6ozHY+ec/Da54+913g2+aGazfYgpaV/vOuyq8gMkDiGZvxgW9G9QellxiSbPjjjsE19dsrOYtKsS+uNlyfjpuAEm294PEKAkRcQNQlJYv1keFN78d1MWMbuWnpu9jDd7QTFMFuZ6WC+q4oHfROZ3Oix3c5rftqLjgp1XTGd1+GknbmlV70GFHBIej3hD0LnZeK66ZNrWKu3uXpu+1IZvQLfFRImRcKPTZrhm/g4YOD5KO61e5a8rmdLQV9gORyg4i9D0buDjZPt09rPdYnKeORV99ZVqccnqQhIQ7DQrzB3P4afvvulEjrQeEffbxD2fd9vuj2ermcTtgcIAd0KegmdJ6vigU474PEsryz++ziNfd9r2fJOZfPvFKM+eOuUFq/gz4SyfYZ9vdFc82eepQG40L/vNbLtDlTcmFYoqApf4Oykfo9vvss+3vBPeudLzcp++R4pSTWwQDA92xfD6rK3QnDZDSteTtx7lUf/zhBaHXm2Lc946HriPX2fIKkE8oxrVzXcfv42XrrxazjUffRW7Qr977d9p7ODoQwZXBH0Tku5j3OeXqY7i0+IQABGqHQFrswgjdtVP/GamWq9CtH15ytb3Quitz4dJLRsYaSdxx99m+w3kZgqnbr3UeO51zVrDetNuXz+dDDz9qRdQKo5biy836Uc2aZpz6ymuvW/dgk8N9mjGumeMK4y+fZN1/vRke63H+eRmu+XQgeo0jDj/UulVpEZ5Tl0L3OnZ9yNEjh2b88OnTf5BZ9NXXYX40q/sMKzT7QS7m5YpKQWlccnGl4TUqdMu9e/dunf3TrSF2oXXpNSHcJ/fncoPuQs/eF5lvraFHQWuRy/25H957/x/m0gkTzTrrrG02tTPCW59xaqIhwz+PbQhAoP4IpKVDU3+EuDIE8iPgGxbldtwPMp5qho6Mpy7IWDLpigl2sFmFm1C3X2KfZhAoSJDwXZP7s63dDGd3Xq5PuZc78tgTgmhJszRypeGMKtmEDN8IUug1fUG66eGHmT9s39QZ1mXQvGTUiERBVGVJyq+frtwIy+15XPBnQvqz53235Unrd7v0rrv+Rrsm4bXB14YgdNeEp2YHHX1c84CRBi+MH1sxu8Mx9D9ffuXVwF2u9kWFbn/25XUzrKvpJTJn/rh05HL3nE4VfeBoGkltxp3rPn3jnPbpntdMdIkXMtrnCrkEkaTzJUBojVX9Jnj//ffNvPvmh7Nkouv8urIorb89+lBskmJx6BFHBcfkHlfCv0Kx6iRIrJr/JNh+Ycv4lS2j1hXVGs1uXWctt9Dh7IpBJkrWFwMvOL+rOfGE42Ov5nuWkIt3Ca4K/oz4XLPS/BmOhQrdEhA1EHvRoq/s36JgQLMTbJSvBffdY/S7WcF3YR/1LBFEyPHP3RdJ4lu2032RUuw1QCsp3HDjTeHz2efj11GuQThiIPFKwXdjrO/1IXTX9H3s19mky8dnrH+rsvjBj+sPRPC5+e9V/1xtF1Polht7DXjQvadPzYJ1M2G17uu0KZWe/bTMivokCo899EDib365jz7k8Ap7QjahO7pUSpDwn/9cG9bXmjzbfcaXXDzCTrhY108+Y7uf9R6hskfzmhEp4Uuu57pYKo6CXMW3ObNy0E40Sf/eyzbAJXqevrv+aLSvFo2r5QZObHlasNsftOdfu6b3ffRa0e++0J2rP+sPHOne1S5rcGJFX9ZvF48suL+KRwH/mhlxvYFExRQBS/0dlEvo9vvs0d8fPktt+14r1O/RQLvqhOoK3UleMXRNf8CDBpi4Plgx7nt/kI2uJa83WgJGa5ZrHfikUIxrJ6Xt9rs+Xq77vJht3H8X+W1AffcunSsnWbk8us+XrccltxyU/1tI/aCTT618DmogSssWJ5o999gjL93Bpc8nBCBQOIG02IUbjNBdX7O51ZTKVei+avq19ofNc+HdcsJxx9gXe1V3QmEEbyNJ6D75pObWteO+dl2cFb3YuTd9AXc1645uwrhRGSKwS+FGK4ZrpKyCOlyHHnyg0Q+2jud1d1HsS3M3u7ZLVVedijDUzhr/8KOKH2zK46TLxoXn1aXQfW7H9mZP6/bND1EhPm5WvL9ut2a1Xz3lijCJqNCdtNa6L2bvbWeLdzy7XZiGf0yzBEaPrLoeZhiZDQhAIBUE0tKhSQVMMtmgCTjDogRsjfqPBrnw/OhDu4yKXU/OuSaXG1m50IyGfgMGhetP3zH7lnA5Fd9F3GWXjjW77hI/eyWanr776+1GxaK4+HH7kowqSQb5Qq/pC9LR/MhALENxtpCUXz/dbAbWV197zXQ5v0dwCV/ovuuee8248ZcF+32DTVxeFli3sMMvrnDjmytu3Pn57vONxrnEpGiaSfUXjZckqBTKU4M0W7WpqMtcM4P8GchRkdq5mIzmO9t3CdSqFxeS2ow77j4141lue7VWdzQ02XYb+3tn/2Atx7XtwNO4kEsQcefIvfCTT/49cGH+qh3U6w+WcXHcZ5LQnc0I6s/Y9IXuYtWJy1u2T7UfuSYO3LRbIUjiYlKIPrvybbv+gBNf6B7mzUqVS9zowCM/H/6scF/I9eNk21Z/S4LNI489lrGURdw5vtDtz3L298edF7fP3RdRoTIubnSfP+M9eizbdwmiup6CX0dav/Pkk05MPFXrxne1rq4Vom5Q60PoVj5q8j726yzJ1bzSVvDfEUMGDbD2i4OC/T43X1wIDnr/kp7LXpSsm3p36N7TMh1undW4E6Ltx/V58hGEJaZKVI3GzbeMrg3H5Stpn/9s9+sjKX7c/scfeTBxhmJc/FzP9Xl2HfDRf64DPsIOsItb8sSl+4X12Nfy9IrlJrQ2cc/u57tDOT9d3ST1MV0Ceo/Jxb7eK379FuO+d9dI+vT7LNm8Huh8DXSSNyMF/x3g2kU2TzLBSfbfJWMvNffed3/w1e9X15YI6K7rPkvhHZRL6Pb77C1ObG7O73qey37sp7uvNXBM79XqhOoK3TffcJ3ZcIMNYi/hi9G+0F2M+z66ZrmfAT1jDj7wgGDJFSeuu+PFuLZLK+kz3/5qbbXxzz6zHoHOrPACkZTHuP1R7zlaA92J4H583df77bePOcwuv6B3BwECEKhdAmmxCzcIobs+RW41s3IUuqMuqrfdZmvTq0e3WHE57lbre9EQo/XQNCL/f9Z44Qe5Iux9YfeMdQ7943HbvnB+3DFH2dH6levHxcX398m95ODho8Jd3c7rZN0a7Rh+9zfum7/A3DZnbrhL7s+XX3754HtdCt0jhw0yG6xfMdPAZeaNN9824yZUCtfTp06sMorwMeu+feasG4NTouupR4XumdOnuKQzPnUNXUsh6qo+OvhB11DnY9edd7Kdj81DVhkJ8gUCEChpAmnp0JQ0RDIHAUvAGRaThG4HSSPgu11wYSh233HbzWattTLFMBmc5QZWwc1ekUHy1DNaBzONJFzdefttoXu+IGKOf7675g5nt7fGwopZPDlOyzicZFRJMlYXek1fQM3IyJ9fxo6+2ETXQ/fjJeXXT7cmQvcdd95lJlw+MbhUrtl6/kwkhO74gQN+O8k1c813qRoVup3h1G8DubajQkBSm4lLR4NX7rOG8znWSOdmHEfjJbmczSWIKB29n7v37BXOmoymLcOf1j504lQxhe5i1Uk0z9Hvmu05yg7+cZ4aosdlQN9gg/XN8y+8GByKDnBJevZE00kSGZy7bcXPNSPQd1dfXaFb68z27N0vdqCCDOSaHfauXUbKDWR48P57w99VvtCabeZstMzuu7svcnlLcPH9zyQDtB8nbtt3I+zXUbYZj0ons91lritfX0J3Td7Hfp3565HGsfJnz/pLKfjcakvo9t9l0bypn9GkybbhoDsN4Jl6ZcV7T3Fdu8o181Nxnfvfmgrd7lpKK9/gP9v9+sj3fMV7+IH7zLLLLpv3Kbme6/79JLflO9klApKCP1vyiGq6UXf90XzqxonFft/Vz2dS/uL2+/d93HF/ny90t2+Xea/78bStGfZqQwr+QD7XLvIppz/D1nedXFsiYJBZ7199v4OUlVxCd7bnr1eUcNPNks82mC6MHNlwdZfNw1S+dZMkdBfrvtd7+eZbbzN33X1v7EA8CbKj7PJGm2y8UVjKYl07TDBmI9/+ar4c3cCF6Az9pHeR315ispe4K25gizxo3XzrbKP3YVzQb4PWZ7aq1sCjuHTYBwEIJBNIi1247IXu+ha51UTKTei+6555dj2qe8PWL8F10EV9EtfMCSMmbCy2rjLn3feAmb+g0mWexO4rJozJ64dDdEa2ZhhrpnG+4SXr4vCKSVPD6GNGDTNrRwzK7uDrb7xp1zGb5L6ai4cPtq7tKlxb1aXQHZfHqNA946pJVVyE5St0i//UKytmIoWF/XPj8klTzMuvvBZ8iwrdP/z4o+nVd2C41nn03HXXWceuX7mnOfywQ7K6Eo2ex3cIQKD+CKSlQ1N/hLgyBPIj4AyLvrEw6cy7751nxl5a4eZvxNDBwWwAP67cCR9r3Zf7s2w0C1xGJYW2rVvZtRTbBtv5/vNdVMr1Xs8L8p8d5K6RZFRJMoIUek1fkFYetH7niiuuGLq2lUA0Y9qUxFkeSfn1062J0O27rY6613Ws3OeNN98SuB7W97QL3UmiYKE85Sb3pFMqBl5kM3qK4TvvLkx0O+7EFImjEn3zCctZEcMfaJLUZnKl9fk//xnM0n399TeC2ZFOsNR54y4ZZT1K7Z6RRC5BRJFdXrStti5hfzsrPG1sjalrrL56MNDFb4vFFLqLVSfKe7Yw64YbzdUzKlz7K95J1iXtrnZZgkaNNrXLMK0duKL1BzcUW+j2XZ7Ouna6abTppjYX8aGvdWn896eeDg5WR+jWrPzDm1V6RZNrzuOOPdpsvdWWdmDz+mZV661MwTfW+0K3n8fbb73ZJHkJCBKJ+effF9WddecP1NGM0t122yXmClV3rbnmmuFvQf/9oLWqZeROChnXs+8ovatcqC+huybvY7/Obrju2gwBxJXHfd4253Yz8cqKAehyq73P3nsFh3xu2YTupOeySz/pUx4i5I7fBbmuPuiA/e1a21uY9dZdJ3Sd79ZgjQrdbr+enxqwlxR8rxE1Fbr9NlyTZ7tfH1r7XZ778gm6P5PW0I47P9dz3W/f2dYKV9rvLlxozu7YObjMaae2NFomJd/g+qO5ZjrrPaW4Cv5AGD+fNb3vc+XVF7qzrW2tdJ574QXTs1ffIEl/oKZrF3o/xq2J7uch6fmdrwjohNl8RUD/2tpOErr9tllb7yCXl1xCt99nz7Vesgb6HXjI4UHS0XXP3fWyfTqe2fp8+daN/+70Z3T7bItx3+tZpnarpaheeuUVo/6XH3zvHcW+tn8dt+36iLkGGuTL0dVJvm3c7yfqt1TrVpnLWrp8Rj9XsUt2Ja39/sMPP1q+b9mJV2+aRx59PBjk4s7XAG3d/wQIQKB2CKTFLlzWQncpiNxqXuUkdEdnNK9lf6QOHdTfGlcq1ggr5HZ62L6orr/xljCJVqefYt2QHBR+T9rQGnHdLugdHu7Wxc7ItmtW5hueefZ5M/Xqa8LoUyZOsD/cKmZphzv/3JDbcrkvd2HwgL7W4LFJ8BWhu4LK99//YGbMnBXO+nas/E+5l+/Xp2ewTri/n20IQKD0CKSlQ1N65MgRBDIJOMNiPkL32++8Yzqc2yVIwDfa+Sn6hhu57Lt33v1GoqlCLsO5n47b/v33380hTY8MvkaNzS5Ors8ko0qSQb7Qa/oC6sEHHWiGDR4YZNF3ISzeV0+9Mpz96JchKb9+ujURuv3609rhEruTQs/efc1zz78QHC5VoVtGu07ndQ3ymLTmuDwKHH/iyeFsFn/2a6E8JSS5daJzGebvn/+AUf0rRGd0d7ugpx2s+WpwrCYzX3ViUpsJEs3zn8SDa6+73txy2+zgjGOOOtL06dUz4+xcgogvjkq4v/qqybGGQX/mXTGF7mLVSUahY774dXbNtKlWZGtcJZY/a6jYQvfMWTeYGdfODK4ZN+jIZUZtvIVdR1JCgEJ1hG7fHbeM+oMG9K8yWFlp+jPAfKHbN1LX5Bni2rSuMe/uubHtSMfigr98w7kdzwnuubh42fb574d999nbLn01PDH6jGuvs97JKlzgRr0h1JfQrcxW933s19nIYUPMAfvvl1hm36WyBm7p/axQ6HM58YJ/HvDbfvdudt3j5idUOUXCzlHHNQ8G3UWFbvcM00nZ+iQSLM7tUjGwLtr38NtGNjHff07U5Nnui4yala6y1EZwTJKEV7+8LVucZLqeVzF4MS4vvkv7XC7/o+e7/qj2z7drFq9oheC44A8c8/tCxbjv467n7/OFbn/2vR/Hbd822w4GmVwxGMRvJ/6z7e65c4wEv6TgZqvq+BPWJb0bwHDzLbeZyVdNC05Lahu+S+98RcBoPvw26C+f4d+HtfUOcnnJJXTLw8ohhzcLouf6LeO71vfXd3fXyvXpRNXaFLp95kl1myuf2Y5ryUw9v53HGX/ga21fW/ly7T+X0F1bbdz/jbfH7ruZS8dU2tGzccv3mN4/jz3+hBkyfGRwSq5BVfmmSzwIQCCeQFrswmUrdJeKyK3mUS5C90OPPGZuuOnWsMVLrBw+dIBZeaWVwn2FbMhAdnbHLqEr8/3328e0b1vhgihbujqv3TkVo1kV7/RTTjZNDz8k2ykZx95+Z6G5ZNyEcN+Afr1M4803C7/7G39/6hlz9TXXhbvGj73YaC1qhXyF7jPPODVYGzxM5M+NZ62Rc8pVM8LdV14+LpiVpB2f//ML66J0WHisVGd0hxm0Gz///IvRSPB333vfvGtn1yyys3H8sMIKK5gpE8f7u9iGAARKkEBaOjQliI4sQSCDgDMs5jIO6aS5d91t5AZXISpOBTvtP9/IfU77dmbO7XMDkdFfR9HFzffTn9GS5GJShoVhdt3ab7/7zqxuZ406cVnXSDKq+MZb3wipcwq5ZpKAKm8/nbt2D92/H2rXbxtsxSNnuNR1FZLym5RuxVmV/31jr79Gt84/4aSWoeibZOT361Cp1kSkqsxN9i3faOy79sx+VsVRf8ZsksHSXyNTZxVT6FZ6PXr1CY2FSfdE8Jvg7I6hm/Co0C3XkpOnVhisL+rXxzSz7l7jwtfffGOete4Rd7IDZ6NrPia1mWg6Witcs7fXtrOOd91l5+jhYAmnw488Jtgft36lE0QUIU648XlHy+lfzE8nyi2fsuh+P+iwI4Ik/TW6taMYdeLnNW7bPTejApgf1zfO++uzKk62Z4+fhm909kUGefPq3LVChJOAcfn4cbEi9JNPPWX6XTQoTLI6Qrf/vNfzVIN2ouG/VmA4+E+BQcf8tbh9MSqbIPS0Xd/cDYYSJ+dFwBeJ4lyGurxodr0blKO1otdcYw3jz2ZWO75+5oxEj2ya3bbyyiub7bZrEs7mVtp+Hel70lqrWvLsjNbtwudqVJT3he7o+t1KtzaD/yzP530crTO5qV5qqaWqZNF3ySyxwl+SpNDncpWLRXYMGjo8nJF4/z132lnOVW0+PvNo38NfbzrbgK9effuH7mij97nfNqJ9Bz+7hT7b33r7bdOxc8VgLl/Q9a+hbd2HWsd56622sgMOGldreRid7z+P457rEoaOOeGkYOCAxHC50NZ9Fg0SHDvZ/MqTj8INM68xm2yycTRa4nf3XFWEbCK5v47wwIv6maaHHRqkWYz7PjFzfx7w+yza5Q/y8M/VcohnndMpXKJj9i03GnkRVPCfbdlmez5ulwMaMGhIcE50prLvFcUtFRRE9P75Lv6LLXTXxTvIFcV/l141eaLZdpuqAz78PnucNxqX1uUTrzRz7pgbfO3ds4c51i4vWZ3ghG4tP6RliOKCP2Ao6b2h8/yBSP6M7kLve/WP1BdTHSmf8vAQDRpcqYE4Ciccb71lWc8nCoVeO0gkx798+nhKojbbuP98T/otpDzoHfqVHRiwy847hV5stF8zuF9/441gydOjjqwYZKH9fpDnEdmdFe6YfYvRZDwCBCBQfAJpsQuXpdDdd8AQ069XD7PGGqsXv2ZrkGI5CN1P/O3v5prrbghLL3dOI4YONBK7swUZnKbNmGlkcFSQS/Hdd012adbpvO5GMxQUtE621svOJ5zbtUewBp3i7rrLTqZr546xp2m2sdaqU1jN/ljUTHQZs+Ru24UkIVrHb7LrgmgtaxdmTLvSaB1qhWxCt1+uo5o1NS1bNHdJhJ833HybeejhR8PvaRe6w4L8ufHPL74018y83rz/jw/CQ/IG4K9VEx5gAwIQKBkCaenQlAwwMgKBBALOsJhL6H7p5VesAHthmErSbBv1sdya3GFku3Fhj+7m+GMrBDR/fz7bvlijfI4fd0kVg8ENN91irrp6epDcccccbXr1vCBMOsmoks1YXcg1swnSXy5aZNqcdU64pm2Xzp3MKSe3CPOqjaT8ZkvXTyBJ6FYcX7ySQKH1+fxZYqpnzdD0XViXqtDtu6BU2UaNGGYkeLug2Uzde/YOZ7Nqf7GF7vvun29GjRkXXFI8Jag5l84uH75RW/uiArDaxMl21q2CRIRpU66s0g/Vb5au3XsYCTgKcuHvu0dOajNB5D//6b15QouWwTdd55YbZ1WZSeYL1XHrq/peCWbOmGbk+tMP/ozqvffaMxgk4R/Xtj+7Xd+LLXQXo06Ur2zBeTwQx7nWgKlPP0jo6dq9Z3gfFVvojg5a6XjO2abV6af6WTASHLvZPLi10HWwOkK3P6P1vHM7mlNbnpyRvvKgpSwksLngC93RAR5xYsyPP/1kzmxzVigSz775BrPuuusGyfnuRbVj8sTLjQRzP/hiiwTtm22bXnKJJYIol02cZOQ5QEGzUCUyR0VbX0hSHYqPi+O/H5SGBIPhdskOLaXlh8FWeH3kT1ewcTO/tTyA3okKvocPP43a2q7u+zhaZxLHW7eqeDa5PEpE0bNI7BXatj7TLknSxh02hT6Xw4QSNvxlNaZNmWS22XrrjJhqUz0u7BOKrVGhWwMT5FpbYr2CnqMSVZdZZpngu2w+4ydcbu5/YEHwXf9qKnQX+mwXyzbtzjYfffxxkJekASdyIS9X8grZZpoGEWL+5Xqu6xTNTNa7TEHuwsfZWZDuXgt22n/qg6kvppBtcEsQIeaf64/qkN6nV1x2qVGfzw++e3Ltjz7TCr3v/WvFbX/wwYemTftzwkMqp/pI0QEXvgvoKAvf1bYSmnT5eLPjDpnrnssW2Mo+G11fbMK4MWY3z17pD+bQs+uaq6dmCJparkLirwvFFrrr4h3k8j5/wYNm5KhLgq89unczzY+vcFvvjuvzyb/bQV2276qgtnPdtdOr9G+0jrIETgUxu/XG663tNbvdOIjs/XNrw0ffF14U6w30JjNt+jXBrpoI3YXe9/57TevAX2YHwkXvVf+3QL/eFxon1hZ6bZ9D0nY+/VWdW5tt/PEn/mYGDB4aZFH3p5bf0IA3P2hgqJbecvfgdTOuNptt1iiI4pbA0Je4Adi/WA9JZ7ZtH/z+UHu8847ZYR1oYJKWAnv11dcC7m5wn39ttiEAgfwJpMUuXHZCtzoCEjNLReRWk0m70P30M8+Zq6ZfG7Z+zcQdMWRA3ozPObdbuGbzevYH9chhA2NHv7678D1rwKqc4XvkEYdbg+SJ4XWzbWiWtWZbuzB29PAqhtnFi3+2Rrg+4Yzx44450px4QkXnrXvPvkbrSyuofBPsTO3ll1/eJRd8fvfd97bDNiA8X6N4+/epdDOYTejWbGzNylbQCDPNyPZnFWkkau9+g4zcsLuQNqH7xx9/Mq/YkXQyMny16GtzTvs24Yx0V6Yvv1xkNBDFhbPatLKu2vZ1X/mEAARKkEBaOjQliI4sQSCDgDMsymgTFUpk0P7662+CmQEa1e5CdGaJ2+8+r7/xZmvkqfQGo/333nWHWdWub1bTIDe9cpWoIKPBWdawrj6P8vf3p58JxDOXtoRCGbddSDKq+EJG3Kysml5T/f4DD20aXD5uFtaLL71s+369XPaC2ZiaLeBCUn5zpevOzyZ0K86Uq642N91S6Q1JbvXWX29da/j/MhSF5YrbCWSlKnSrLP6MGH2XOCvB68svvzQy9slApbbtDFXFFrp1j8idv5vBJpaakb3brjvbWaW/myeefNLcc+99yloYokK3DmhWq2bVuaAZrDJ8r7jiX4IZJY88+lg4M0Tluf3WmzIM60ltxqXnPv1ZT7pHZNyUkU+i4gsvvRQsNeBYXTx8qNl/v8z+sC8yiPNJJ1a4DZbh2QlF/jUOtGvnyv1x4803N59bo6Ha/u1zK8RHl6diC93FqhOXv7hPf/CCRLCjj7IcmzQxi39ebAXAt4L7y3HU+cUWupXm3578u+k/cLA2g+DWKl7fzt762Apj11g39M5luYsTFYXc/rjP6GxteVzYbZddgrW233v/H3YdyseCduuf6wvd2i8XqedYUfH7H34Ioun+lBAnceHFl16xA7UfCkW8uIERzz73vLmwT7/wErovdtpxR6NnoUQLGYsd5+igIQ260LluWQC199NOaRl4Q5CI9Mprrxm5RnWhw9lnBfXkvvvvB7dPaUg039A+HzXzzb8vFSdpZphbk1dxNINwow02NFvZtc79576O1Uao7vtY4myHTueFdabZfk0PO8w+R1cInhMSmt0sNdWZBhi5wQEu/4U8l10aSZ/+rHP1BU46sXkwoWCJJZY077z7rrnzrnvCNqU0VGfqE/hBdoFzz+sWllHHdB8vtdSS4WAiPWcV1L5qKnTr/EKf7XqX6R3j30OHHnxQcB/+ww7Ul/c93UcuXHnFZWaH7bdzX/P6zOe5rlndF/bpb156+eUgTYnduhfWWmvNwGPefDswwA160Xvwasu8urZP1x91mVYddGh/ltna1uE39p59wj7z/LLKhbry4IdC73s/rbjtqNCtOBLj1X/exH6qLyC3xc4ttI5H+6Ta5z/bVM62Z7Yyu9iJMbLFqV6vvmZm+PyOG8ik91zb9h0y2roGTS677LLmgw8/tDbIp3WZMBRb6FbCtf0OcpmPDnpq16a1tYkuF/QtfO82fp9d/RMNwGm06aaBnfS55583V8+otB3LW8VOO+7gLpH3pz+AYZeddzYHHbi/+Yu10+rd5kKhQrfSKeS+9z0bKC29c9UPa9SokZ3c8w/zwgsvZgzk8dfoLvTaOj9XyLe/WtttXL9T9X5U0Luk/VltzRaNNw80Ar3j5t03PxwQpbqWoO2CBhZpgJGCzj3ZPod23mkHY8cmBf1cPafcb6iotyp/prrOj/LXPgIEIJA/gbTYhctO6M6/iuouZpqFbhlJJk6eFsKSYWX4kItCd0DhgSwbEydfFfzAdlG2a7KNOcYaKbTGmpsN/fyLL1lD7cxQEFdcf/1rd27S5wcffmSGjawYfag4cicuEVou7BU0O0NuwSXEuuC7/553/wIz27r8dEEvz45ntwvF7sU//2wunTDRdnA/cVGC2eaade5CNqF70pRp5oUXK36sKP6ee+xm2lmRVyPVP/3sc3PFpKnBzHKXlj7TJnR/+ulnZuDQkWER9th9V9O549nhd23MX/CQXY+wYoSyvo8fY12/r/5XbRIgAIESJZCWDk2J4iNbEAgJRA2L4YGEjdNObWn7Iu2rGLb96P4aeNpfjFlsmmUg1+QyyGYLfS7sYY45OtMVYZJRxRcy4oTuml4zH0HaX3tORs5ZdvbJOmuvHRQtKb/5pKsEcgndKtfYceNDo3SUp9Zm1rp1chGrUMpC9492QKhmTTgRIFoWCbEy+jmxudhCt673me0zd7PeDqKiop8Xucgcc2nFwNk4oVtx/ZnI/rnR7bjZX0ltJnpunMATjaPvLax4pFm8Sy+9dMZhzZZsZ92wR8vqG/K1Frxm0jsBMiOBP7/I6O7EsmIL3bpEseokLu/ap/Z2Ye9+4QCHuHh+GYu9Rre73vRrrjVyb54UNGBFBm7nGrw6QrfSvHfefXYprYp2m3QNCYnO04C/RreL7wuTbl/0U14lNLBCy05Eg4TLSy+7PLo743swK7freWaZSHv92f5W1jrLbkZsxkneFwkBA/r3DQdr6JD/fjjjtFMDbxjZ2vSQgRcZLUcRF2bffof9XT0541CcsJ8RoUhfavI+9teXT8qGxF89izQYPxoKeS5H04p+13twtF1fVp4hkoLavZYxUX1F1+h256iMXezM9Lg61Tt5/NhLzGjrrUNtpxChW9cr5Nmu8yVO6bmbK3TtbIVfOyCluiGf57rSzOd+ErtpkydZ0Xfj6mbDuP7okUc0DZaXc6J6XEISFjUTNTrIIt98Kl7cfR93LX+fL3RrcMzsOXck9j90XjZvRr4bff8a/rbvVtrfr+1X7IxQeVdICsOHDDJXWDFO7+vaELp13dp+B7myaba2Zm37ITq4SR4pRl0yNkPE9eO77WzPahcn6dP3dOLH8V3+F0PoVtqF3PfRATZ+Xv3tixKWyynk2n76cdv59ld1bm228aC92Gd8tneJ8qCBLFFPYvL80d+2SX9Ai+JGg2bUa0kVt6yojkcHn109dbJddmLL6Kl8hwAE8iSQFrswQneeFVpINCd0F5JGdc+VseS9hZUzgqp7vuJ/ZTtqmmVc3XBkMzsTu0XlTGylc9Gg4RkittKUyC2Xg/rR49yVu2vtt89e5uyz2riveX0+9sSTdgZSpQFC6W+66Sb2h/TSdtTxexlpbLjhBsGsdH/nlVOuNhLc/bCp/eHwP/sjTyKuH449upk5qfnx/q6srst1/dFjqxovlEelHxfSJnSrDH36D8pYi1uu7ZvYgQ36oSy3a1q324V11l7Luq4Z5r7yCQEIlCiBtHRoShQf2YJASMAZFsMdkQ0ZLdew4sNOdrDdIQcdlOE6MRI146vWj3WuTYsllGqAoNa8vPnW2VWM05qV2vGc9lXcPypTSUYVX8iIE7p1bk2umY8gLQPL4GEjwrVGlX+55lVIym8+6er8XEK34ihIgHr8iScC7z4a5Lj++uuZba3gtMduu2XM1ClW/VVcNfO/bzSOznrIjJn8TaLjRCsiPfjwIxmR2rZuFbjVlUhWm0K3Lqp30jUzrwtmmPqZ0EwPudqU2Ohm+UdFTz++ZnrI9aubIe4fk3cAzRrbeKON/N3BdlKbqRLR7tCMzetvuKlKXhVXrsi1ZmiSYKc4ErHGWJfVT9gZ8y5E17qUoD585KhgNrqLo0/xkOF/Sftbw7kZrQ2hW9cqVp0orbggz1djx1+WMcPQxZMAovpq3uKUYFe0zvN59ujEpDW63XX0+bCdWT3Dzv5zs4fcMbW5vr16mjvvvid0oxqdce3iZvvUzEOJ3dHBDRK4+9j077OCo3NpHCd0K22JA9fMnFXFKKyZnwcfeIARr+XsMygpPGBd14pFtIwyQJ/U/IRgjdGkc+XCfZY9V65ao0Gz/lqdflowI19t0g/ROtrezpLVYCsn6ru4EpA041LP8GxhwUMPm6nTpocc9W7Vuqx1EWryPtaAFdWZZs5Hg5i3sc9X3c9JoabP5aT0/P16f954862Bi+CoUK11tyX4SgCUSJ0kdCs9natZ+apTibirWK8zW225hdnLzlTXGtQaRFUMoVvXqumzXecq6H09c9b1VWbq6phmG+q57bu21v7qhHye60pP99O1tl3Im0I0yHV/+7Ztgokj0WP5fHf9UQ22u+D8rkZuyN27252vNtfOenY43rqcj7pjdnH0WdP73k8jbtvvswy0g2MkZA21zwU3cMudo+djBzsoNFedaPCmvBVFB+OonKe0bBEsGREn5rvraMCG+ji+1yU9Vzt3PMccdugh5sSWp9Wq0K181PY7SNfQ7F7NyNZz3N3zcd6ltE78TfbZIK9FLp7OV1D/Rm3nQPtuLCSIuQbb+P00fz36YgndymMh973eu3pvxj3D1W71nNxii8aJKAq5dmKi9kB1+qtKpzbbuDzX6DflbDtDOzpgVu9oeYxoaTWEqFtz5UtawS32XP0ujbY19S2aHn5o8Bskev+qLyUG6s/I69EIuyQKAQIQqDmBtNiFEbprXsd5n7nLrrsH7tTzPqEIEf/619XMSy8+X1BK0VnS+SZ2+KEHmzNOq1iXzp2jG2LMpZdliKDuWPTz5JOam6OPrHBDGT2W6/ttc+ZaI0DlOk9x8TewxsW+dg33lVdeKeOwBOfR1nW6XMRlC/vsvad17dS2SpRsM7oVOU5Id4lopnync9plzJ5Po9At1+Sa1S2XW9mCBPALL+hq3dFtkC0axyAAgRIgkJYOTQmgIgsQqBcCcqmo9X5lKJghR5rhAABAAElEQVRn3ZZHZ4YWkikZshbZd7vcBC+91NJm00abVpnJV0j6cefWxzXj8lFX+ySOyUWjQlSIrKs8VPc6MoR+Yd28qv8qkSJqXKpuejWJ/8svv5h/2mWB/mP7nGtbl64yONckyLWyjGHqu0p8WXeddWJnTtYkbXeOhFqJ3t98822wrEDFQNxl3OGcn7onVNYV7LJKcl/rL3/kTv7111+D58Bvv/0ncCm/zrrrZBUn3HnF/CxWnSTlSYbSjz/5JOCo+t5oow1r/XkUzYu8NMjV6Xd2Fqvaf6NNNw1c2EbjFfJdRuCPrRcxiYyarenPUMo3XaWh32V//PF/QZuOm8GdlJbKqHtC7XXppZcymrWr90u+QUZpvTe0LJhcVMuDxhp26a4ksSwqdGvggoL6n//48APb7lcw69klH/SsqU5QHf1snxM6rzr5r841onELeR//8MOPwXNVA600e1vLXESXUotez/9em89ltUWtta2BNSuvtJLZZOONi8rUCa9x7s/9MlZnu9Bnu9YY1z2kNqSlLdZea+1qrzOcLb/5PNd1vp7t8hbwq32P6H5cd911C1qeJilPum8XLnwveBfqntU9V513e3Xv+6R85Nqv+0SzYPX81bKIa9vJE/kGtWNNwtGzTWHVVSve+dXpN2tQ5peLvgpsiRLJ497H+eanJvHq4h2kfImV+nn6XNu2h6gXD5d3v8+uZ5Duk+rUiUsn26fETdXbaquuVtR7MO6ahdz38tjwlW0b3//4Q9APkwgbHdgVd023r5BruzSK8VmbbVxtRP1hvd9170i3WMf2u5Pal18e9QG1TIuWV9CzSf2vFVdc0Y9SZVvtV4Or4gT0KpHZAQEIZCWQFrswQnfWaizOwRnW1dn48RPs6KNfi5NgjlT0w6hnzwvs2hftcsTMflizmH1X1NljVx49qllTOxqreeWOP7f0Yppzx112FOYbQafJj7DSSiuazRptao6wI4ObbLu1f6ja2wsefMTcbd2/+bOHlYiuofW5Wp9xaqIRWC/12TaPj9vZ4VGxVjNwNFv9OLveV1yH1he697RuKM/t2D4j73rJynX3HXfek5G2hPf27bT+zfJ2/beh4TmTrxhvf0BWuCjT+t5a59uF8XYN8ajR44033zbjJlzhopgZV02q8uPkSbuO+XS7nrmCZpPPmHZlGF/cbrp1dvBdrCZdNi485m9cPmmKXYPttWDXrnZ9o66dM9176YfDHdb13VNPP+ufFmyrbcotfOtWpwVu26tEYAcEIFByBNLSoSk5cGQIAnVA4LkXXjA9e/UNrqQ1A+VekFA6BLQE0FfWKOOEm7icjRh1idEsSoXrrpke9Ifj4rEPAhCAQDkSSBK601ZW3sdVa0wi7U12fXbNTNd68XHhk08/NWe0rrBbycX10MED46KxDwIQgAAEIAABCECgngikxS6M0F1HDUQjlzQ6ty6CRo1VZ/RlXeQpeo2KEYKLApd8G9vR+LlGYkXPz/Vd6esm/NqKrhKlN9pw/WpdQ/W16KuvzQ92NLzCGnYk+FprrlGtEXnZ8vjjjz+Zn+yIP6XrxOxs8dN4TAMF5MqqYjbAMnbk90ZlW9Y01g95hkC+BNLSocm3PMSDQLkQ0Dv2gp69QzeMvju/ciljmsshV7TXXjcrKIJcgzY//rgqAyUfsi7A5YpTQTMO75k7p+izQ4PE+QcBCECgRAmUg9DN+7hq4/rs88+DgXiaCa6Z2sOsgK3ZyH7QTLsevfqEbuq7d+1iTjrxBD8K2xCAAAQgAAEIQAAC9UwgLXZhhO56bihcHgIQgAAEIFDKBNLSoSllhuQNAsUkMGDwUPPZZ58Hbopdukc0PdwM6NfHfeWzBAg89fQzpk//AWFOZOjX+p5yt/yTHfD49LPPWg85r4bHMfCHKNiAAAQaEIE0C928j5MbqpZs6D9wcMZ68fJu0tiu3ysvbx998rG5d9794ZqrcgN9y42zgkFfyalyBAIQgAAEIAABCECgrgmkxS6M0F3XLYPrQQACEIAABFJEIC0dmhQhJasQqDEBuQFtetSxGec32XYbM2HcmKKvK5xxEb7UiIDvyjZbAoMH9DeHHXpItigcgwAEIFCWBNIqdPM+zt0ctSzcwCHDzNPPVF3SzD97D7vs24D+fY3EbgIEIAABCEAAAhCAQGkRSItdGKG7tNoNuYEABCAAAQiUFIG0dGhKChqZgUAtEfju++/NtdYltpY/WWWVVcyOO2xvDrZrWpb6kjW1hCMVyWr90bvuudc899wLoZt5GfO3tjO8G2++mWl2RFOz8UYbpaIsZBICEIBAsQl88OFHZtr0GUGyLU5sbnbbdZdiX6JW0uN9nB/W3//7X/PoY4+bBxY8aN57733z/Z9Lw225xRZmyy0am+23a2Ka2ZneS9rl5ggQgAAEIAABCEAAAqVHIC12YYTu0ms75AgCEIAABCBQMgTS0qEpGWBkBAIQgEACgf/74w/zf//7n1l66aUTYrAbAhCAAAQgUL4EJHwvvdRSZgmE7fKtZEoGAQhAAAIQgEBZEUiLXRihu6yaHYWBAAQgAAEIFJdAWjo0xS01qUEAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQaLgE0mIXRuhuuG2UkkMAAhCAAARyEkhLhyZnQYgAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACeRFIi10YoTuv6iQSBCAAAQhAoGESSEuHpmHWDqWGAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIFB8AmmxCyN0F7/uSRECEIAABCBQNgTS0qEpG+AUBAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAPRNIi10YobueGwqXhwAEIAABCJQygbR0aEqZIXmDAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIJAmAmmxCyN0p6lVkVcIQAACEIBAHRNIS4emjrFwOQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACZUsgLXZhhO6ybYIUDAIQgAAEIFA4gbR0aAovKSlAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEICACKTFLozQTXuFAAQgAAEIQCCRQFo6NIkF4AAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhUi0Ba7MII3dWqViJDAAIQgAAEGhaBtHRoGlatUFoIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAArVHIC12YYTu2msDpAwBCEAAAhBIPYG0dGhSD5oCQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhAoEQJpsQsjdJdIgyEbEIAABCAAgVIkkJYOTSmyI08QgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABNJIIC12YYTuNLYu8gwBCEAAAhCoIwJp6dDUEQ4uAwEIQAACEIAABCAAAQhAAAIQgAAEIAABCECg7AmkxS6M0F32TZECQgACEIAABGpOIC0dmpqXkDMhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEDAJ5AWuzBCt19rbEMAAhCAAAQgkEEgLR2ajEzzBQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABGpMIC12YYTuGlcxJ0IAAhCAAATKn0BaOjTlXxOUEAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA3RBIi10Yobtu2gNXgQAEIAABCKSSQFo6NKmES6YhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCJQggbTYhRG6S7DxkCUIQAACEIBAqRBIS4emVHiRDwhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACaSeQFrswQnfaWxr5hwAEIAABCNQigbR0aGoRAUlDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEGhQBNJiF0boblDNksJCAAIQgAAEqkcgLR2a6pWK2BCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIJBEIC12YYTupBpkPwQgAAEIQAACJi0dGqoKAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEikMgLXZhhO7i1DepQAACEIAABMqSQFo6NGUJn0JBAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEKgHAmmxCyN010Pj4JIQgAAEIACBtBBIS4cmLTzJJwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBUieQFrswQneptyTyBwEIQAACEKhHAmnp0NQjIi4NAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKCsCKTFLozQXVbNjsJAAAIQgAAEiksgLR2a4paa1CAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEINFwCabELI3Q33DZKySEAAQhAAAI5CaSlQ5OzIESAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBvAikxS6M0J1XdRIJAhCAAAQg0DAJpKVD0zBrh1JDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACECg+gbTYhRG6i1/3pAgBCEAAAhAoGwJp6dCUDXAKAgEIQAACEIAABCAAAQhAAAIQgAAEIAABCECgngmkxS6M0F3PDYXLQwACEIAABEqZQFo6NKXMkLxBAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEEgTgbTYhRG609SqyCsEIAABCECgjgmkpUNTx1i4HAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBsiWQFrswQnfZNkEKBgEIQAACECicQFo6NIWXlBQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEBABNJiF0bopr1CAAIQgAAEIJBIIC0dmsQCcAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAASqRSAtdmGE7mpVK5EhAAEIQAACDYtAWjo0DatWKC0EIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgdojkBa7MEJ37bUBUoYABCAAAQiknkBaOjSpB00BIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQiUCIG02IURukukwZANCEAAAhCAQCkSSEuHphTZkScIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAmkkkBa7MEJ3GlsXeYYABCAAAQjUEYG0dGjqCAeXgQAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQ9gTSYhdG6C77pkgBIQABCEAAAjUnkJYOTc1LyJkQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCDgE0iLXRih2681tiEAAQhAAAIQyCCQlg5NRqb5AgEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAjUmkBa7MEJ3jauYEyEAAQhAAALlTyAtHZryrwlKCAEIQAACEIAABCAAAQhAAAIQgAAEIAABCECgbgikxS6M0F037YGrQAACEIAABFJJIC0dmlTCJdMQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABEqQQFrswgjdJdh4yBIEIAABCECgVAikpUNTKrzIBwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBtBNIi10YoTvtLY38QwACEIAABGqRQFo6NLWIgKQhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCDQoAmmxCyN0N6hmSWEhAAEIQAAC1SOQlg5N9UpFbAhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEEgikBa7MEJ3Ug2yHwIQgAAEIAABk5YODVUFAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACxSGQFrswQndx6ptUIAABCEAAAmVJIC0dmrKET6EgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCNQDgbTYhRG666FxcEkIQAACEIBAWgikpUOTFp7kEwIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAqRNIi10YobvUWxL5gwAEIAABCNQjgbR0aOoREZeGAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIFBWBNJiF0boLqtmR2EgAAEIQAACxSWQlg5NcUtNahCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEGi6BtNiFEbobbhul5BCAAAQgAIGcBNLSoclZECJAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAXgTSYhdG6M6rOokEAQhAAAIQaJgE0tKhaZi1Q6khAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCBSfQFrswgjdxa97UoQABCAAAQiUDYG0dGjKBjgFgQAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQzwTSYhdG6K7nhsLlIQABCEAAAqVMIC0dmlJmSN4gAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCKSJQFrswgjdaWpV5BUCEIAABCBQxwTS0qGpYyxcDgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBA2RJIi10YobtsmyAFgwAEIAABCBROIC0dmsJLSgoQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAgAmmxC5es0O0A0pwgAAEIQAACDZnAGmusXq/Fd+/j+s5HvULg4hCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEGhCBtNiFEbobUKOkqBCAAAQgkD4C9S0wp6VDk76aJccQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABEqTQFrswiUrdJdmtZIrCEAAAhCAQMMikJYOTcOqFUoLAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKD2CKTFLozQXXttgJQhAAEIQAACqSeQlg5N6kFTAAhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACJUIgLXZhhO4SaTBkAwIQgAAEIFCKBNLSoSlFduQJAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEAaCaTFLozQncbWRZ4hAAEIQAACdUQgLR2aOsLBZSAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIlD2BtNiFEbrLvilSQAhAAAIQgEDNCaSlQ1PzEnImBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEI+ATSYhdG6PZrjW0IQAACEIAABDIIpKVDk5FpvkAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgECNCaTFLozQXeMq5kQIQAACEIBA+RNIS4em/GuCEkIAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQqBsCabELI3TXTXvgKhCAAAQgAIFUEkhLhyaVcMk0BCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIESJJAWuzBCdwk2HrIEAQhAAAIQKBUCaenQlAov8gEBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAIO0E0mIXRuhOe0sj/xCAAAQgAIFaJJCWDk0tIiBpCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAINikBa7MII3Q2qWVJYCEAAAhCAQPUIpKVDU71SERsCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAASSCKTFLozQnVSD7IcABCAAAQhAwKSlQ0NVQQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQHEIpMUujNBdnPomFQhAAAIQgEBZEkhLh6Ys4VMoCEAAAhCAQJkReOWVV80bb75p3nvvffPxxx+bL7740g6q+9b8/PPP5j//+Y/5448/yqzEFAcCEIBA7RBYYoklzLLLLmtWWmkls8Yaa5j11lvXbLLJJmaLLRqb7Zo0MTvttGPtXJhUIQABCEAAAhBoMATSYhdG6G4wTZKCQgACEIAABKpPIC0dmuqXjDMgAAEIQAACEKhtAp988ql56KGHzRN/+5t55plnzW+//VbblyR9CEAAAhCwBJZbbjmz1157mgP2398cdtihZuONN4ILBCAAAQhAAAIQqBaBtNiFEbqrVa1EhgAEIAABCDQsAmnp0DSsWqG0EIAABCAAgdImMHvO7Wbu3DvN008/U9oZJXcQgAAEGgiBvffeyzRvfoI5ucVJDaTEFBMCEIAABCAAgUIJpMUujNBdaE1zPgQgAAEIQKCMCaSlQ1PGVUDRIAABCEAAAqkg8Msvv5gZM641s66/IXBHnopMk0kIQAACDYyA3Jy3PrOVad++nVlxxRUbWOkpLgQgAAEIQAAC1SGQFrswQnd1apW4EIAABCAAgQZGIC0dmgZWLRQXAhCAAAQgUFIEpk6dZiZPmWoWL15cUvkiMxCAAAQgEE9g5ZVXNp3P7WQ6deoQH4G9EIAABCAAAQg0eAJpsQsjdDf4pgoACEAAAhCAQDKBtHRokkvAEQhAAAIQgAAEaovAI488ai4ZM84sXLiwti5BuhCAAAQgUIsEttxyS9On94XmkEMOrsWrkDQEIAABCEAAAmkkkBa7MEJ3GlsXeYYABCAAAQjUEYG0dGjqCAeXgQAEIAABCEDgTwKDhwwzs2ZdDw8IQAACECgDAq1bn2mGDhlUBiWhCBCAAAQgAAEIFItAWuzCCN3FqnHSgQAEIAABCJQhgbR0aMoQPUWCAAQgAAEIlCSBt9562/Tt29+8/sYbJZk/MgUBCEAAAjUjsP1225nRoy822267Tc0S4CwIQAACEIAABMqKQFrswgjdZdXsKAwEIAABCECguATS0qEpbqlJDQIQgAAEIACBOAL3z3/AXHBBT/Pbb7/FHWYfBCAAAQiknMByyy1nJky41BzZ7IiUl4TsQwACEIAABCBQKIG02IURugutac6HAAQgAAEIlDGBtHRoyrgKKBoEIAABCECgJAjccONNZuDAwSWRFzIBAQhAAAK1S2D48KGm1Rmn1+5FSB0CEIAABCAAgZImkBa7MEJ3STcjMgcBCEAAAhCoXwJp6dDULyWuDgEIQAACEChvAtdcM9MMHzGyvAtJ6SAAAQhAIIPAwAEXmbPOapuxjy8QgAAEIAABCDQcAmmxCyN0N5w2SUkhAAEIQAAC1SaQlg5NtQvGCRCAAAQgAAEI5EWAmdx5YSISBCAAgbIkwMzusqxWCgUBCEAAAhDIi0Ba7MII3XlVJ5EgAAEIQAACDZNAWjo0DbN2KDUEIAABCECgdgloTe7OnbvU7kVIHQIQgAAESprA5MmTWLO7pGuIzEEAAhCAAARqh0Ba7MII3bVT/6QKAQhAAAIQKAsCaenQlAVsCgEBCEAAAhAoIQJvvfW2OfGkk81vv/1WQrkiKxCAAAQgUNcElltuOXPH7bPNtttuU9eX5noQgAAEIAABCNQjgbTYhRG667GRcGkIQAACEIBAqRNIS4em1DmSPwhAAAIQgEDaCBx3XHPz+htvpC3b5BcCEIAABGqBwPbbbWfuvntuLaRMkhCAAAQgAAEIlCqBtNiFEbpLtQWRLwhAAAIQgEAJEEhLh6YEUJEFCEAAAhCAQNkQGDxkmJk16/qyKQ8FgQAEIACBwgm0bn2mGTpkUOEJkQIEIAABCEAAAqkgkBa7MEJ3KpoTmYQABCAAAQjUD4G0dGjqhw5XhQAEIAABCJQfgUceedS0P7tD+RWMEkEAAhCAQMEEZkyfZg455OCC0yEBCEAAAhCAAARKn0Ba7MII3aXflsghBCAAAQhAoN4IpKVDU2+AuDAEIAABCECgzAgc0exos3DhwjIrFcWBAAQgAIFiENhyyy3NA/PnFSMp0oAABCAAAQhAoMQJpMUujNBd4g2J7EEAAhCAAATqk0BaOjT1yYhrQwACEIAABMqFwNSp08wlY8aWS3EoBwQgAAEI1AKBPr17mU6d8PxRC2hJEgIQgAAEIFBSBNJiF0boLqlmQ2YgAAEIQAACpUUgLR2a0qJGbiAAAQhAAALpI/DLL7+YvffZ3yxevDh9mSfHEIAABCBQZwRWXnll8/RTfzMrrrhinV2TC0EAAhCAAAQgUPcE0mIXRuiu+7bBFSEAAQhAAAKpIZCWDk1qgJJRCEAAAhCAQIkSuOKKSWbCZZeXaO7IFgQgAAEIlBKBC7qfb7p161JKWSIvEIAABCAAAQgUmUBa7MII3UWueJKDAAQgAAEIlBOBtHRoyok5ZYEABCAAAQjUB4Hddt/LfPvtt/Vxaa4JAQhAAAIpI7DGGmuYF55/JmW5JrsQgAAEIAABCFSHQFrswgjd1alV4kIAAhCAAAQaGIG0dGgaWLVQXAhAAAIQgEBRCcyec7vp3btvUdMkMQhAAAIQKG8CY8aMNie3OKm8C0npIAABCEAAAg2YQFrswgjdDbiRUnQIQAACEIBALgJp6dDkKgfHIQABCEAAAhBIJnD6GWeap59mZl4yIY5AAAIQgECUwN7/3959gEtNdA0cP0iTroKKr4KF3pEiRSlSBASxwSuKIF1EpQuK9CZNsCAC0gQBBRTFAliQpqAU6dXyimJFFGmioF9O/BKSvbu37l12dv/zPNxNncz8ktVsTmamWlWZN3dO4GLmEUAAAQQQQCBGBEx5LkygO0YuOKqBAAIIIIBAegiYckOTHnUnTwQQQAABBOJB4MCBb6RW7TrxUFXqiAACCCAQZoFVK1dIwYIFwpwr2SGAAAIIIIBANAiY8lyYQHc0XC2UAQEEEEAAgSgVMOWGJkr5KBYCCCCAAAJRLzBjxiwZNnxE1JczVAELFLhC7ruvtbv677//lmeemSjHjh1zl8XSRPduXSVnrpxulWbNelG+/fagO89E5AXi4ZxcdNFF0qVLZxf3zz//lDFjxrnzTMSvwID+j0u7dm3iF4CaI4AAAgggEMMCpjwXJtAdwxchVUMAAQQQQCCtAqbc0KS1nuyPAAIIIIBAvAq0adteVq1abWz17/pvcxk1aqSv/E2b3i7bd+zwLYuVmS+/2CcZMmRwq/NIn76yaNFr7rxpEw892EU6d+4kpcuUN63obnlj7Zy4FfNM1KpVU2bNnO5ZInL1NUV888zEp0CwayM+Jag1AggggAACsSdgynNhAt2xd+1RIwQQQAABBMImYMoNTdgqTEYIIIAAAgjEmUDxEqXl1KlTxtaaQLeZge7rr68uTz81XvLmzWtfeyYHTQl0G/ufDwoeBoGsWbPKnt2x+WJRGHjIAgEEEEAAAaMFTHkuTKDb6MuMwiOAAAIIIJC+Aqbc0KSvArkjgAACCCAQmwJbtmyV2+9oZnTlCHSbF+gO1gKUQHd0fw1j7ZxFt7Z5pVv82iIpX76ceQWnxAgggAACCCCQqIApz4UJdCd6GlmJAAIIIIBAfAuYckMT32eJ2iOAAAIIIJA6gZfmzpMBAwalbuco2YtAt3mB7lub3iJPWa25vYlAt1cj+qYJdEffOYmmEg0bNkTubXlPNBWJsiCAAAIIIIBAGARMeS5MoDsMJ5ssEEAAAQQQiFUBU25oYtWfeiGAAAIIIJCeAoMGD5XZs+ek5yHSPW8TAt2lSpWUI0eOyLffHkyRR5Eihe3t9+//3N0vpd1k58uXT6668krZsnWrnD592s0nJRMXXniB/Oc//5GdO3eF3C1//vxyzdVXy5Hfj8h3330vv/76a8htUxvo1nIUuqaQne+3Bw/KTz/9JH///XfI4yS24oorLpfcuXPLrl27E9sswbpwnJMEmXoWZMyYUa666kq59JJL5dAvv1iW38mxY8c8WyR/MlOmTFYr2/Kyd+9eOXr0aLJ3PO+886Rs2TL29Xro0CF7v3AHutN6LvU86Ln/4osvk12vUBuWKVNGvvrqq0Sd0/o9KlToGtEuvlN6vYUqc7Qtb926lQwZPDDaikV5EEAAAQQQQCCNAqY8FybQncYTze4IIIAAAgjEsoApNzSxfA6oGwIIIIAAAukl0KZte1m1anV6ZR+RfNMa6F76zptyzTXXuGWdMWOWjB4z1p3XiU6dOkivnj3cZT/88KPUql3HnX910QIpXbqUO9+ufUf55+9/ZNSokXLZfy6TTFbwUpMGmrdu2yZt2rQPGVTTgNj0aVOlQIECogFHZz8NMrdq3Ua2btkkGTJksJfrn0f6JGzRnT//pTLthalStGgRyZw5s7vt8ePHZePGTXJ/5y5Bx2Vfu2alXHzxxe72d9zZXKZOed4OcuvCv/76SzZs3CgtW7a2t2nZ8m7b5YILLvCVSVdqYP+99z6QJ0aNlsOHD9vb659dO7dJ5ixZXBNnxZ9//mlP6vXY6f4HnMVSqWJFGTiov5QuVSrBMXSj77//Xrp27SEbN21y99GJPn16S/t2bd1l8+a9LM9Nel5mzHhBihUtKlmsMmj6559/7GByu/adZN++fe723olwnBNvfoHTGtx+7LG+0uKu/0qOHDkCV4vaLF/+rnTtdvYadDbS6+H888+3Z/X6KlW6nDzQ+X5p166NPf65c62cPHlSFi9+Qx7vP8DZNcHnzTc3kuFWy1zv+dT93n57qbz19tsya+Z03z4pbYWf2nPpHLRgwQJ2Ga60Xtxwvht6TWrwWL8bc2bPkhIlijub29eR89+3u+9uIYMHna37ypWr5PMvvpBOHTuIvhCg6aeffpYhQ4fJO+8stedT+z2yd7b+VK9WTcaNGy2XXnqpW1693n777Yi8/MorMmbMOGdT4z+DvQhhfKWoAAIIIIAAAgiIKc+FCXRzsSKAAAIIIIBASAFTbmhCVoAVCCCAAAIIIBBSoEHDxiGDeyF3irIVaQ10b9yw3g4IOtVavPh16dnrEWfW/uzfv58vaHrixAk7oOhspAHiyy+/3Jm1g5I33VQ/aGBWNzpx4qRUvq6q9XnC3UcnWrS4S0YMH+oGxXwrrRndL3v2bL7FgYHu22+/TcaMfsIN3vk2/v8ZDXi3vq+tbN78mW+1BqGzZTubv5Yve/bsvm00sFz9+pqy4JX5UrlyJd+6YDN6rHr1G4i+HKDpqy/3B9vMXbbNehHg1tvutOfr1q0jL0ydHNLR2Ulb9vbs2VveWPKms0jGPzlW1MJJ23fskMKFCvnq56zTTw1A3temnaxZs9a7OCznxJdhwIwGbPVli6JW8D2ppC37b2l6m+8lhcAW/m+99bY0adI4ZFb6wkSTW25NsH7C+HFy220JlzsbasDbe23o8pQEutNyLvVYd955h31dOwFuXeZN2updrwNtpe+k7t17utfEQw918b2sEqw+up++ZPHee+/b105qv0eaz4jhw+See1roZMj0+edfyH/vujvR3g9C7hxlK/T6Xb7s7SgrFcVBAAEEEEAAgbQKmPJcmEB3Ws80+yOAAAIIIBDDAqbc0MTwKaBqCCCAAAIIpJtApcpVrbf0f0m3/CORcTQGupNT7/nzX5Z+j59tYapdOW/c8EnIIHeoPL2Bbu1eXIPuTiveUPvo8lOnTtnB+jNnzribBQa63RWeiYkTJ8nPP1stX4ckf2x3bXHbuElTO5fkBrq1la3d+tvTIt1TjAST2uq5WPGzreoDA90Jdgiy4Ouvv5baN9Zz14TjnLiZhZgYNHCA1cL/3xbyITbxLZ70/GQZO/ZJd1lgoNtdkcjEPS1bybp1690t9KWMKZMnufPJnUhuoDut5zJXrlzWSxkbEvQCkFQ5Ewt0B9tXvxPFS5S2ezBIy/dIW4+PHDEs2CESLAv14kGCDaN8Qd68ea3/fp29pqK8uBQPAQQQQAABBJIpYMpzYQLdyTyhbIYAAggggEA8CphyQxOP54Y6I4AAAgggkFYBDepocMfkFK2Bbm1d+vbb78j7738gTZveItqi1Zu0hXO16je4i6ZPf0Hq3FjbndeJH3/8ScaPnyB/WV1Sd334QWvs5qt863XGG+hesmSxlCld2t1GWykvWLBQli5bLvXr1bNbmHqD4FOnTrO7Fnd2CBXo1m7INdio+5YrX1FeeXmer4vo3bv3yL2t7rO6ZP5Nrr++ugwbOli0e2lvqla9htWq+weZO3e2XG4F5APXf/LJp/bmK1etksmTp0pgC1xdqZ6LX3/Dfhmgn9XVd6BHzVo3yjfffGvnEyrQrQHe1608Clmtuzt2bO97KUC9ril0tmV1OM6JXZhE/gSaq7UGsz/7bItUr15NunV92FdGb4t3zTZYoFtb0b/8ygLR8/Kw1ZI50FpbfT/ctbtbqs2bPpULL7zQndcJPR8vvjjb7nq/V8+eCXoS0G2SG+hO67nUVv316tXVQ7pJxy4fP+FpuxW31vFqa3z4wJScQPdp60WP41Zr8Dx58tjf1Y6dOktavkca1N+2dbOv9bt2rz5+wlOiLfLbt2sr1apV9RW16a13yPbt233LTJvR8cf37N5hWrEpLwIIIIAAAggkIWDKc2EC3UmcSFYjgAACCCAQzwKm3NDE8zmi7ggggAACCKRWQIN6GtwzOUVroLv3I33l1Vdfc2nfe3eZFC5cyJ3XrpbLlL3Wnd+/b7evu/Fff/1NKlWuYgfynI0+WrvKHS/bWeYEurU1t673pmeffc4OsDnLJj77tDRufLMza4/97G0FHRh01Q11vHINPOs40tdee601xvdGWb/uI2vc4UvcfA4ePGiPH+10ha5B0/nz5thBZx3Te/XqtbJnzx53+1utwP9TT41353UiMGhao8YNcrfVlXvx4sUkf/78smnTZnscZmenUqVKyltvvuHM2p/aDfSGDRvt6WCBbh3nuvMDD7r7BGtNredEz42mtJ4T90AhJrQb7v6P95Oy5crIVdaLATlz5hQdF11bwDtp2dK3pFixYs6s/O9//5Mb69R35wMD3Rq4rVOnnhvw1+7GdRxv71jtGuzXVt2aypUrK68vftXNTydWr15jd+PuLNQg8MaNnyRoUR14zpztAz/Tei4Dz4P2QnFdleq+78bKD99PENBPKtCtLwSULVfBzqdQoWvk6NGj1ncwc5q+Rw92eUB69+7pI9Cu4rXltpMCvz+x0KpbX4LRa5GEAAIIIIAAArElYMpzYQLd/3/dbdmy1brx3Cn7rDcstbuq77//QQ4f/sW60T1m//gz/cd/bH29qA0CiQvoj6wsWbJYrQ5yykUX5ZXLLstv/+gtWqSwlCpVSsqXL5d4BqxFAAFXwJQbGrfATCCAAAIIIIBAsgUIdIvd3a52u+ukcIzR/dNPP0uVqtWdLO3PIYMHSuvW/wYXdYG28ixarKS9ToORGmT2piFDh8msWbO9i+wxlHUsZW9yAt06vrJ3XWDrZN0nWHC4QsXK1hjBv9lZBga6A4PxznFfmvOi3XLbmXc+tftwbbX67nvvyUsvzbOeqRx2Vvk+kxPo9u3gmdGAZKNGDaXxzY2sIHhxzxqRB6wg9jIrmK0pWKC7cJHi4u2qXX8XLn5tkS+PRjffYgflw3FOfBmnYEZbx2rrfn0poU6dG32tgwN7AggMdC9duky6PPiw72jvv7fcasF+jbts79690rBRE3v+3pb3yLBhQ9x1OlH5umpy6NAh37KpU56X+vXPduuuK5Mb6PZl5JlJzrlM7nmoVaumzJo53ZO7SFKB7qHDhsvMmS/69knr9yjQKfB86cH69XtUOnZo7x5XA+6ly5R3502cINBt4lmjzAgggAACCCQtYMpz4bgNdB848I28/8EH9puq69d/Ynx3bUlfkmyBAAKOgD44qFq1itSsWUPq1a0rBQsWcFbxiQACAQKm3NAEFJtZBBBAAAEEEEiGAF2XJy/QHdjy98SJE/b41g6xjud7+eWXO7N2t9PaMtebunZ9SHp07+YuOm11R16kaAl7vnLlSrLglfnuOp2oV7+BfPHFl75l2lJau5n2JifQPXBAf2nb9j7vqqDPOfS3kDd5W0EHBro3bd4szZrd5d3cnr7ZCjI/N/GZBMsDF+hY3qPHjPO1bNdtUhLozpcvn2gr2RtvrGUba9fQoVJige4TJ05a56ysb9dgreBvu/1O2bp1m4TjnPgOlsRMwwY32S9C6MsIuXPnDrl1YOA0MND95JMTZOJz/vG2NZjvfdn788+/kPo3NbSPMWL4MLtLe+eAgWOdO8s7deogjz3a15m1P1Ma6E7NuQx2Hho0bCz79vlbD+t1oS2/vSmpQHewgH5av0fvLl8qRawX7L0pcHiIjFZZM1k9JDhJhzkoVPhsq31nuUmfdF1u0tmirAgggAACCCRfwJTnwnEX6F64cJE9ppN21URCAAEEVEDHyLrdagHRvHkzQBBAIEDAlBuagGIziwACCCCAAALJEKhUuapoN8Amp3B3Xf7mm2/Z3XB7TUaOGCZ3393CXZRUoPujjz62x6x2d7AmtAWntuR0kjfQHawOJUuVlZMnTzqbu59ffbnfndYJJ9CtrVm1VWtKU7/HB8j8+S/buwUGul9/Y4n06NEraJZ9+zwinTt3CroucOHGTZus31pn/ZIb6G5hdV2u9tpaNDmpQ8f75YMPVtibBrbo1rGvy19byZfNhRdeYL04sMG3zAl0h+Oc+DIOMZM9e3ZZ+s5byX75+vvvv5fq1589z4GB7kGDhsjsOS/5jqYvUWjA2EneQPfMGdOkdu1azioJ9kKArmzSpLE8+8xT7nY6kZJAd2rPZbDzULFSlaC9BQRaJBboDtbjgdYprd8jvZ70ukpp0iEE9CUDU5P2irFxA89ZTT1/lBsBBBBAAIFQAqY8F46LQLd2AzR9xkyZY93sHzpk9o/4UBccyxFAIO0C+fLllVat7pX27dpKjhw50p4hOSAQAwKm3NDEADVVQAABBBBAIOICwVpGRrwQaTxgsEBY06a3y/YdO5KVswZnvF2XL122XLp0eci3r3YJrl0aOympQPeKFR9K+w7+ILD+xujfv5+ThXgD3cFarQZr0Z0rVy7ZtnWzm4dOOIHuCROelNtubepbl5zA2YgRT7iB0cBAtwbANRAeKmm5e/boLpUqVfSNLx5s+/4DBsrcuf+2Wk9OoPvOO++QcWNHJ8hKA9Y7rGHnPlyxUh5//DFfELxdu47y4cqV9j6Bge5ff/1VKlS8zpdfME8n0B2Oc+I7WIiZT9Z/LJdccrFvrY6z/c2BA/LppxvkqquukipVzpZbx0O/oUZtd/vA4K5zPbgbWBPz571k92jmLPMGupPbojvw+tW8khvoTsu51JfS582d4xTd/mxxd0v55BN/zwY6jviWzzb6tkss0B2qFXVav0cff7TaGjrtMl85kvM9vP6GWgm6i/dlEuUzRYsWleXL3o7yUlI8BBBAAAEEEEipgCnPhWM+0D15ylSZNGmyNdb20ZSeQ7ZHAIE4FdAHHl26dJbO9/sfTsUpB9WOcwFTbmji/DRRfQQQQAABBFIl0KZte1m1anWq9o2WncId6F679iNp1bqNr3oLF8y3grlnW8QmFejWVsXautibAgOF3kB3sHGIhw8fab+w780jWKtaJ7Cp3Xv37t3T3TxUIM/dIMhEYKBbxwjXscITS9oiWT00IHmL1eq3evVqVuvkgr4AtO7vdU1OoPutN9+wxxR3jq1jmje6uYmvO/fAIO99bdrZw9PpPoGBbu25QHsw8KbEAt3hOCfeYwWb1jGqdfxsb3rrrbelW/eeoudPU2A9vv32oNSoWdtep38CDXo/0jdBV/GJBbqDjdFdrfoNol2ke9OkSROlUcMG3kXJDnSn5VwGO0evLFgojz569qURLVT3bl2lWzf/2OSJBbq93z9vpdL6PXp10QKpUOFaN8v//e9/cmOd+u58rE4EGyM9VutKvRBAAAEEEIgnAVOeC8dsoPuDFStkzJgnE4zbE08XIXVFAIG0CehbyX369JK6deqkLSP2RsBgAVNuaAwmpugIIIAAAgicM4FBg4fK7Nn+1pLnrDCpPHBaA92B42t/9913oq0rnZTPGiNaW2lmzpzZWWQHdkuVLufOB+aR0kC3ZqTjC3vHn9bxrbWLag3IOemjtatEx5X2JifQra1+X54/17tKBgwYJC/Nnecu0+7Xhw4ZZDUEOCbfHvxWdu/eI6NHj3W7gQ4MdE+ePNUaY3usu79OaHBWW73+x2q1mueCC+yxhh/r119efvkVdzvtunnN6pW+XrL27/9cbmrQyN4mWMA+sHXw3j07JUuWLG6egS3t69atI9NemOKu14nEWnSnNNCt+aX1nGgeiaVHHuklXR7o7NtEx233nvPAFsJJtehOaaC7TOnSsmTJYl8Z3n//A+nY6Wy5tLX0xo2f+MaV1h0Cz5kvE89MWs/lnt07xDu2vPo0bnKr+7yvePHisuSN13zfUT18YoFufXGiaLGSnlL+O5nW71HgMAfaRXqt2nXkm2++dY81+fnnRK/fw4d/la+++ko2bd4sY8c+6a43caJ161YyZPBAE4tOmRFAAAEEEEAgEQFTngvHZKA7Fn6sJ3JtsQoBBCIswI+2CINzuKgSMOWGJqrQKAwCCCCAAAKGCGgQVIOhJqe0BrrfeP1VKVu2rI9g3ryX5cnxE6TGDdfb3Y1rsNubwt2iW/N+YepkqVevrvcwdnBszNhxdg91j/d7TIoUKexbrzNOoFunN2/61Bof+EKdtJN2ga3nd4HVAvYGqy5TpzzvDxha68uVq2AH7nWHwED385OnWA0Ixv2b2f//1aDnZ5s3+Fps61ji2gpZA/warL/ppvry9FPj5bzzznP3ff31N6RHz972fLAgtY6N/u3Bg/LHH3/IM89MTFCWY8eOiXa1ry8iNGxwkzxl5e8NfmrG3br1kCVWPpoCW0KnJtAdjnNiFybEH22BrC2Rvcl5uUCvuSdGDk9wTegLENdVqe7uktYW3ZpRYPf9umzlylUybfoMq1v1S2TY0MG+lxZ0vabkBroDr6uUnsthw4aItjz3Jm3xvm3bdsmYKaOUKlnSd6052yUW6NbuxHVc7GApLd+jiy66yPb0jiuv3e1r7xk7duyUe++9RwYN9A8HcODAN3YwPFhZTFkW7ByZUnbKiQACCCCAAAKhBUx5LhxTge5du3bb3Rcldyyu0KePNQgggIBfQN90HzVqpJQsWcK/gjkEYlzAlBuaGD8NVA8BBBBAAIF0EdiyZavcfkezdMk7UpmmNdD90ENdpFfPHikqbnoEurWL5s1WADlTxowpKos30N2ixV12cDS5GeiY2Tp2tpMCA5LBAt267fTpL0idG2s7u7mf2npVkzfIp/O6vEHDm0VbdWu68sorZeWH79vTgX+cFss63q/2sBWYNHgfyujZZ5+T8ROesncJR6A7HOcksPze+WCtqXV9YnU8deqUFC9R2s0mHIHuYC8euAdIZCK5ge60nkvtHl+Dz4EvNiRSNHtVagPdaf0ezZo5XbQr7+Smli1by8fr1iV386jcbvFri6R8+bO9XERlISkUAggggAACCKRYwJTnwjET6F66dJn9drDe9JMQQACB9BDQH9YTxo+TRo0apkf25IlAVAqYckMTlXgUCgEEEEAAAQMENGhm8u/otAa6tdXx8mXvSOHChUKerc8+2yLXXlveXZ8egW7NXFutDrVazwYGip0D63nSf7lz53YW+Vp068JgYxW7G3smgr3kkNxAd0YrGP/2W29IsWLFPDmGntTuz7Wlsjdt3bLJVw9n3fHjx6V0mfL2C8Y6tnMoC90+MCDs7R49HIFuPUY4zonmEyotXPiyVKpYMdRquxtzb5f2umHTW++Q7du32/uEI9CtGT038Rm5+eZ/u5a3Mw74o11vX3HF5b7zkdxAt74snpZzqUXRYy9b+nbQluW6XnsE0C7xvdeLt4V/4AstibXo1vzS8j3S78fs2TOlerVqmlWiaeLESXbvEYluFOUr9TmJdi9PQgABBBBAAIHYEzDluXBMBLrnWt2t9Te8u7XY+wpQIwRiV2C41XVay4Cu02K3ttQs3gVMuaGJ9/NE/RFAAAEEEEitgHapu2rV6tTufs73u/POO2Tc2NG+cjS6+RbZs2ePb1liMzoW9OuLX5XixYv5AmXaJffMWS/KK68skFUrV7hZaNfLZcpe686vXvWhFChwhTv/7rvvyf2du7jzOtGmTWtfl8WhxgguV66svDhrhmgX4d6kLZ2bNb9L5s2dI1dffbW7yttq1VmorUknjH9SLrggj68+ul4D5dpl/fDhI53N3c+dO7aKtp51UmJBOH1BoFevHnKfNTZvjhw5nF18n19//bUViH9UNmzY6FuuMxoEnDZtimTLls237tdff5UKFa+zl2lLYw1YewP7ukKDlEOHDZfTf522e91yMtCW49cU+rcV+OjRT8h/m5/treDQoUNS+Tp/4DFnzpyyfdtnzu72Z+MmTUV7C/SmcJwTb37eaQ0STnz26QRdlOs2O3fuklat28gH779rdUl/gbubtxv4Lz7f6+u2O9j1MHfubF/Qde/evdKwURM3P2eidat77a76vePRaxfhn3zyqV2OPda46d7W9MkNdGv+aTmXTvm0W/5RT4ywWg6Xl4svzidnrLJ9c+CAzH95gbzwwjQJDPp7/9vWuXMn6dvnEScr+3vgbRnvrvBMpPZ75GTRs0d3ad++nfWd8l/jul6vx1Gjx8qrr77mbG7spzppK3YSAggggAACCMSegCnPhY0PdM+YOUuGDRsRe1cQNUIAgagWGDDgcWnXtk1Ul5HCIRAOAVNuaMJRV/JAAAEEEEAgHgVmzLB+Uw/nN7Wee22JWbNGDbnmmqvl7XeWyg8//HDOLgkd67eBNRa1jlm9bNly0aB7SpMGo7U74euuqyyHfzksO3ftkt2794gGL8OZtLVtSWuc5CKFC9vjiWugWI+VVJm1fGXLlrHL9+23B2X16jWiLxEEJm0RXOHaa+U86/ysWLFCdNtzkcJxTkKVW19sqFSpohQtUsS2+/jjdXZr7lDbp+dyHQu+dq1asnffPlm79qOwXi+pOZf6wkJGa+z33bt329dvsB4o9GWVvVYg3pu8Ld+9y1M6ndbvkb7McH31alKiRAn52grMb9u2TXRc7lhJA/pbz0batYmV6lAPBBBAAAEEEPAImPJc2OhANy25PVcckwggEHEBWnZHnJwDngMBU25ozgENh0QAAQQQQCAmBDTgUqt2nZioC5VAAIHYE9i4Yb3kzZvXrZh2Wd+s2X9l69Zt7rJ+/R6Vjh3au/M6UaRoiXP2soCvIDE+o71dFCxYIMZrSfUQQAABBBCITwFTngsbG+jWMbm7PPhwfF5d1BoBBKJGYNJzzzJmd9ScDQqSHgKm3NCkR93JEwEEEEAAgXgRuKdlK1m3bn28VJd6IoCAQQLz570kVatW8ZVYW3V/9tkWuxv7okWLSv78l/rWf//991L9+pq+ZcyEX6Batar2cArhz5kcEUAAAQQQQCAaBEx5LmxkoFu7wbrjzub2mDrRcLIpAwIIxK+AdkP22qsLra76SsQvAjWPaQFTbmhi+iRQOQQQQAABBNJZYOGiV6WPNZYyCQEEEIg2gRo1bpDZL85MUbGaN28hGzdtStE+bJxygTFjRknzZnemfEf2QAABBBBAAAEjBEx5LmxkoLtp09tl+44dRlwIFBIBBGJfoEzp0rJkyeLYryg1jEsBU25o4vLkUGkEEEAAAQTCKFCpclX55ZdfwpgjWSGAAALhEXjowS7So0c30fGyE0v//POPzJ03XwYMGJTYZqwLg4B2J6/dypMQQAABBBBAIHYFTHkubFyge9DgoTJ79pzYvXKoGQIIGCnQunUrGTJ4oJFlp9AIJCZgyg1NYnVgHQIIIIAAAggkLfDMMxNlwlNPJ70hWyCAAALnQEB7U3uwywNy++23Se7cuSRbtmx2KY4dOyY///yz7N//uYx8YrR8991356B08XfIHt27SdeuD8VfxakxAggggAACcSRgynNhowLdH6xYIR063B9HlxFVRQABkwSmTZsidevUManIlBWBJAVMuaFJsiJsgAACCCCAAAKJChw/flyqVa8hR48eTXQ7ViKAAAIIxLdArly5ZN3HayRHjhzxDUHtEUAAAQQQiHEBU54LGxXobtCwsezbty/GLx2qhwACpgoULVpUli9729TiU24EggqYckMTtPAsRAABBBBAAIEUCUyePFVGjxmbon3YGAEEEEAgvgT69nlEOnfuFF+VprYIIIAAAgjEoYApz4WNCXRPnmL94B7ND+44/C5RZQSMEujbmP0NLAAALb1JREFU1/rBdz8/+Iw6aRQ2UQFTbmgSrQQrEUAAAQQQQCDZArxgnmwqNkQAAQTiToAX/OPulFNhBBBAAIE4FjDlubARgW66UIvjbxJVR8AwAbrwMuyEUdwkBUy5oUmyImyAAAIIIIAAAskSWLHiQ2nfgRc3k4XFRggggECcCUyfNlXq1LkxzmpNdRFAAAEEEIhPAVOeCxsR6H7m2YkyYcLT8XklUWsEEDBOoEePbtL14YeMKzcFRiCYgCk3NMHKzjIEEEAAAQQQSJ3AoMFDZfbsOanbmb0QQAABBGJSoHXrVjJk8MCYrBuVQgABBBBAAIGEAqY8FzYi0F35uqpy6NAvCZVZggACCEShQL58eWXDp+ujsGQUCYGUC5hyQ5PymrEHAggggAACCCQm0LTp7bJ9x47ENmEdAggggECcCJQpXVqWLFkcJ7WlmggggAACCCCgAqY8F476QPfChYukT9/HuKoQQAABowTGjH5CmjdvZlSZKSwCwQRMuaEJVnaWIYAAAggggEDqBXbt2i133NlcTp06lfpM2BMBBBBAwHiBrFmzymuvLpSSJUsYXxcqgAACCCCAAALJFzDluXDUB7rvadlK1q0zr2XkeeedJxkzZrSvmDNnzsjff/+d/KuHLRFAwHiBatWqyry5dPdo/ImkAsa8ucepQgABBBBAAIHwCyxdtly6dGFInvDLkiMCCCBgjsCkSROlUcMG5hSYkiKAAAIIIIBAWAQIdIeB8cCBb6RW7TphyCnyWTzY5QHp3bunfeBx48bLc5Oej3whOCICCJxTgVUrV0jBggXOaRk4OAJpFTDlhiat9WR/BBBAAAEEEAgu8NLceTJgwKDgK1mKAAIIIBDTAsOGDZF7W94T03WkcggggAACCCAQXMCU58JR3aJ7xsxZMmzYiODCUb40GgPduXLlkm7dHg4p97fV8vzIkd+t8dAPyTfffCuffPqpaGv0eE4PPdRFLrjgApdg9uw5oi9gmJa6d+sqOXPldIs9a9aL8u23B935lE706tVDsmXL5u6m18zkyVPd+VAT5cuXkyZNGrurD3x9QGbPecmdv+22W6V06VLu/Jo1a2XVqtXuvGkTAwY8Lu3atjGt2JQXAZ+AKTc0vkIzgwACCCCAAAJhFZgxw/ptPtzM3+ZhhSAzBBBAII4EBvS3nmm0axNHNaaqCCCAAAIIIOAVMOW5cFQHutu0bW9skCsaA90VKlwrry5a4L1OE53W7tZXr14jvXr3kcOHDye6bayu/OLzvaLd0DtJWzJoiwbT0pdf7JMMGTK4xX6kT19ZtOg1dz4lEzfdVF+mTJ6UYJcGDRvLvn37Eiz3Lhg5YpjcfXcLd9GRI0ek/LWV3PmVH74vV155pTu/du1H0qp1G3fetIlatWrKrJnTTSs25UXAJ2DKDY2v0MwggAACCCCAQNgFaNkddlIyRAABBKJWgJbcUXtqKBgCCCCAAAIREzDluXBUB7qLlygtp06dithJC+eBYiHQ7XjoOWhxd0vZsmWrsyhuPgl0JzzVS5YsljKlSydYsXLlKmnbrkOC5d4F8Rbozpo1q+zZvcNLwDQCxgmYckNjHCwFRgABBBBAwEABHbO7R49exv5ON5CcIiOAAAIRFdDnGBMmPMmY3BFV52AIIIAAAghEp4Apz4WjNtCtQdXb72gWnWc3GaWKpUC3Vvf48eNStlwF0Vbe8ZQIdPvPtnZ/v3XLJl/rcGeL01Y398WLl0q0u/t4C3SrzeLXFol22U5CwFQBU25oTPWl3AgggAACCJgmsGvXbnn00X6yfQcvdJp27igvAgggkJiANmoYNWqklCxZIrHNWIcAAggggAACcSJgynPhqA10z7W6h+5vdRNtajIl0K1dQ+sDikyZMsn5558vl15yiVSpcp3kyZMnAb2OTz1o8NAEy2N5AYFu/9nt91hf6dgxdKvtJ0aNlqlTp/l38szFY6B7+LAh0rLlPR4FJhEwS8CUGxqzVCktAggggAAC5gvob0P9jUhCAAEEEDBfoHXrVjJk8EDzK0INEEAAAQQQQCBsAqY8F47aQLfpP5pNCXT37PWILF78eoILv02b1jJo4ADfcm3NXa36DfLTTz/7lgfOaKvfYsWKWdv9KAcOfBO4Oirm7TIWLSpbtm6V06dPhyxTagLd+fNfKldffbXs2bNXfv3115B5h1qRP39+a/+r5NjRY3Lwu+9SPD56kSKF7az37//cPUS4xujevGmDXHjhBW6+gRM//PCjfY0ELnfm4zHQzY9F5+zzaaqAKTc0pvpSbgQQQAABBEwWWLHiQxk9Zpzs27fP5GpQdgQQQCBuBYpaz8b69uktdercGLcGVBwBBBBAAAEEgguY8lw4agPdbdq2l1WrVgfXNWCp6YFuJZ465XmpX7+eT3vholelT59Hfct0pkCBK2TwoIFSo8YNkjlzZne9BscPHz4ss2bNlucmPe8u14m33nxDnKCszj///BR56ulndNJO5513nmzbutmX3+Ahw2T+/JedTSRfvnyyds1KX1fa9eo3kIsvvljmz3vJ3W7fvv1y6213yLPPPCU1a9aQnDlzuut+//130XyDBfyTG+i+/vrq8mjfPlKqVElfWf766y/R4O/j/QfImjVr3WMGTrRocZd0ffhBueyyywJX2d3F79y1S+67r60VOP8twXpdUKjQNTJ92lTrPBQQddOkAfydO3dJq9ZtEnQ3/kifvrJo0Wv2dsn9U6lSJVm4YL5v8++sQPx//vMf37JGN99iBfn3+JY5M/EY6K5Vq6bMmjndIeATAeMETLmhMQ6WAiOAAAIIIBBDApMnT5VJz0+Wo0ePxlCtqAoCCCAQuwLaAKTLA52lc+dOsVtJaoYAAggggAACaRIw5blw1Aa6GzRsbPRb4bEQ6M6ePbvs2L7FF7j93//+JzfWqe/7cvy3eTMZMXK4ZMqY0bc8cOarr76Se1u1EQ2Oapo0aaI0atjA3Sww77p168i0F6a463Vi/fpP5O577nWXdev2sHTv1tWd//PPP6WYNU70jbVry4wZL7jLNUCsD10KFizgLguc0C63tettb0pOoHvMmFHSvNmd3t2CTr/77nvy4ENdE7QgHzxogBXEbh10H+/CEydOSNNbb5cvvvjSu1g0SD5i+FA3wO1bac2cOHFSsmfP5lucmkD3vLlzpFq1qm4+Gkhv2KixvP/ecneZTqxcuUratuvgW+bMxGOgW9+OXr7sbYeATwSMEzDlhsY4WAqMAAIIIIBAjAkcP35cpk+fKbPnvCS//PJLjNWO6iCAAAKxIZA3b15p3epead++reTIkSM2KkUtEEAAAQQQQCBdBEx5Lhy1ge7K11WVQ4fM+HGsQcShQwbLk+Ofku+//96+oEIFurXFbq+e3WXgoMF2ADJdrr4QmVaocK28umiBb22orsudjT5Z/7FccsnFzqycOnVKipco7c5Xr1ZN5s6d7c4nNfHbb0ekYqXr7FbKlStXkgWvnG0h/M8//0iRoiXkzJkzdjba+rpJk8a+LPXhSeky5d1lGkDUQKKT1q1bL/e0bJUg0O2sT+zztHXc0qXL2XV0tksq0D1wQH9p2/Y+Z/MkP7VVd2urZbaTKlWsKAsXnm2h7iwP9akttJvccqu7WrsR37jhk5BBbnfDgImUBrqzZMkiu3dt9x3no48+tl5cuE/WfbxWtLt2J6ljCesaCdYlfDwGuvPlyysbPl3v8PCJgHECptzQGAdLgRFAAAEEEIhhAe0JTHvM0t9nJAQQQACBcy+gDRduv/22ZDXUOPelpQQIIIAAAgggEA0CpjwXjtpAtwZTNaga7UmD3DOmT5MqVa6Tb7751m5tfPDgQQkW6L788svt7rS1m+9PPvlU2rXvENFgd2oC3dq9uHbH7STtirxQ4WLOrOzcsdVqLZzdndcJHcP7/fc/kNy5c8lNN9UXDZJ60ysLFsqjj/azF+3ZvUOyZs3qru7evae8seRNez4wyO5sVKVqdfsY2kX3/n27fcFXbTH9zjtLQwa6dczwufPmyZnTZ6Rnzx4JWjo/8MCDsmz5u86hJLFAt3a7vnzZO74W77rjjh07ZePGTXZ34jfccH2C9e3adZQPV660j7H4tUVSvnw5e1r/qO/06TOs9aukoNUN+UBrnHRva+zAFw2mT39B6txY291fJ3788ScZP36C/GW1uNbu0K+66irfep1JaaDbez07mbW4u6V9HWuLem1Z703aMl5byAemeAx06/Wt1zkJAVMFTLmhMdWXciOAAAIIIBDLAvr7S38brl6zxu6dy4Tf+LF8PqgbAgjEj4A+i6hatYrUrFFD6tWrm2gPh/GjQk0RQAABBBBAICUCpjwXjtpA9zWFioq28I32VLFCBZk3b44bzNUgt7YovqVJE+ndu6dd/HHjxsubb70l2vWzBrs1aRfb99zTSjZt3mzPR+JPagLdOiazjs3sTVdfU8SevfPOO2TcWH9X306Lamd7bXH84Yr3JU+ePM4i+7xqq2ztivulOS+Kjm/tJKfFc2LBQSeI2rDBTda43s85u4q2JC5SpLg9H9h1uS78+eefpWq1G+xgss5rgFkDzd40bPgIq8vzWe6ixALd2q26dq/uTcOHj5TpM2a6i/QYr7260BfsPnTokFS+rpq9jXYXpT84ChcuJBdddJE8++xzvrHM+/V7VDp2aO/mF/iigQb6M2XK5K7XLtorVa7i1lFXfLR2VYJxtFMa6A5ste1tWR+si3sdl7xa9RvccjkT8RjozpAhg3z5xT6HgE8EjBMw5YbGOFgKjAACCCCAQBwKbNmyVXbs3Cn7938uX3/9tdUj2g92N+fHjh2zfyOb8AwgDk8bVUYAgSgU0GcN2rAkZ86cot2RX3ZZfrnyyiut52KFpXSpUr5GFVFYfIqEAAIIIIAAAgYImPJcmEB3GC6mWrVqytQpz7vB7h9++EHWrP3I7Q5Iu22rYbXszZ8/v300DXJ3uv8BWbVqdRiOnvwsUhPo1q7OdT8n6YMHfQlBkwZwr732bDfiGmguV66CHcB2ttdPbdU9ZfIk7yJrLKCOsuLDlRIYrHYCqM2a3SFjx/iD6E4GGzdtkubNW1gtn/2tmT/7bIvccWdze7Ngge6Hu3aXt97yj5UcGCh+4YVpMvKJs8dNLNC9c8c2X2trbdFfs9aNTjHdz2eeniC33NLEnfcaugs9E9pSvWLFCtL45kZys/Xv4ovPdh3v3Tdbtmyya+c2z54iQ4YOk1mz/F3J33bbrTJh/DjfdikJdOuPpHeXL/Xtv2jRa3arcGfhm0tet7p9L+XM2p+Nbr5F9uzZ41tGoNvHwQwCRgiYckNjBCaFRAABBBBAAAEEEEAAAQQQQAABBBBAAAEEDBAw5blw1Aa6Tem63LkWA4PdzvLAz3MV5NZypCbQvWzpW1Ks2Nmuyp1AtOYX2Mr3yy+/krr1btJVCZK2aNW3TZ30wrTpMnLkKHs2MNhc+8Z6MmTwQFFTJ+l4z07L5RMnTkqp0mVl+7bP7DdXnW369n1MFiz8t4V2sEC3tjDWlsbepIFiDRg76cUXZ8vgIcOc2US7Lg+s05yX5lpdjQ9293UmatasIS/OmuHM2p+NmzSVXbt2u8u0e/guD3S2W89ffHE+n5W7kTXhDXQHjnGu29Wr30C++OJL7y5y4YUXyuZNn/qWpSTQrS9x1K9fz7f/xImT5Lv/H49eV9Sy6tjAamHvTfoiR5u2Z1uj67p4DHQn1juB14tpBKJVwJQbmmj1o1wIIIAAAggggAACCCCAAAIIIIAAAggggIBpAqY8F47aQHfl66rKoUO/GHXekwp2n8sgt0KmJtC95bONvm7HtXs5DURr2rxpgxVEvcCe1j/aDXuzZne5896JwIDySmsM6rbtOtibBI5TrV1/N7O6RXe6O9dx3D79dIPUqHG2K2wdH/rl+XPdQ2iX3kWLlZQzVqtyTcEC3cWKl7K7w3N3siZ2bN8iOXLkcBclN9Ct3UPt3bPT3U8nxowZJ89PnuJbpjPakn/dx2t8y3WMch2rXFNgi2/fhgEz3kD3Xf9tLqNGjfRtUbJUWTl58qRvmc589eV+37LkBrq1dbmOL505c2bf/smZ0Rb+JUqUFn1JwUnxGOjOly+vbPh0vUPAJwLGCZhyQ2McLAVGAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSiVMCU58JRG+hu0LCx7Ntn3ri2oYLd5zrIrd+TlAa6CxS4Qlav+tD3FXPG0NaFH3+02hoD6DJ3/d69e6Vho7NddLsrrInALsC1e23tZltTixZ3yRMjh9vT+kdbXefPf6k7v3XrNnlp7lxfV+bfffedb9zp3bv3yM2Nb3H3CRbodsYWdzeyJlIb6NY8Alt0e1upe49Rpcp1vqC8rmt66x2yfft2eW7iM3b35N7tNZj9008/y+bPPpNvv/lGOnb894UA3cYb6E5ui+5cuXLJtq3+seCTG+i+++4Wditsb/lSMj16zFiZPHmqu0s8BrqLFi0qy5f5u8x3QZhAwAABU25oDKCkiAgggAACCCCAAAIIIIAAAggggAACCCCAgBECpjwXjtpAt3Z5HOkxrJO6sjJmzCgPP/Sgu9lTTz/jTnsnAoPdSQW5u3fr6u7+7MTn3FbJ7sIwTaQ00B3Y0lqL8eSTE2Tic/+Otx04LrO2vC5VulyC8pcvX040L2964IEHZdnyd+1F2iX5vr27QnbXPW7ceJk2fYbs3rU95DbDho+QGTNmuYeIRKBbWzprt9RO2rlzlzS55VZn1v3Ubthbt27lzmuwunCR4qKt0LVVuLYOd5KO863dmh89etReFFgPb6A72Bjdw4ePFG0R701NmjSWZ595yrvIHl9bx9lOKq344F25+uqrk9os5Hp9aUG7jHdSPAa69b8Hs2ZOdwj4RMA4AVNuaIyDpcAIIIAAAggggAACCCCAAAIIIIAAAggggECUCpjyXDhqA92DBg+V2bPnRNXp1e6bNSDrpGAthJ11TrBb5zvd/0CiQXtvt9La/fZff/3lZBPWz+QGurU78hEjhkujhg18x9cuqEuULON2Rf3QQ12kV88evm3mz39Z+j0+wF2mLwesWvmBXH755e4ynShbroIbzNV5bfGqLV+DpSpVq9stnNev+0guvfSSBJto8FfLpYF2JwUGiHV5sPOVlhbdwV4EuK9NO1m9+mw35ZdccrF8tHa1O764luO3347ItRUqSZEiheXd5Ut1kZse7tpd3nrrbOvfCROelNtubequ9wa6dWHg+OY///yzVL++pnuOdJuP1q7ytX7XZclp0R2sy/VDhw7JunWhu+G+8cbavnHT9Vje8cjjMdCtLznoyw4kBEwVMOWGxlRfyo0AAggggAACCCCAAAIIIIAAAggggAACCESbgCnPhaM20D137jzpP2BQVJ3XlAS6teAa7NaUVMv0cxnoPnbsmBw7dtwuZ8aM59ktlLWr6wwZMtjLvH8Cu+bWFsUaKNZxnL1Jx+peuGCR5M6TRzp1bC/58uXzrpZ3331P7u/cxbesc+dO0rfPI75lOqPlK1P2Wnt5YNDX2fjLL7+SuvVucmbtz0gEuuvXrydTpzzvO64Got94Y4l8+OFKa3zqEtK+fdsE41v37PWILF78ulxxxeWyZvVK3/7btm2TFnffa7eK79ihvfTu3dO3Xme8AfsXpk6WevXq+rbRVuFjxo6zXyR4vN9jdkDdt4E1k5xA99gxo60x1+/w7dqjZ295/fU3fMu8M506dZDHHu3rXWRf/9pDg6Z4DHQPHzZEWra8x2fCDAImCZhyQ2OSKWVFAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSiWcCU58JRG+jesmWr3H5Hs6g6xykNdCe38Ocy0J3cMgaOge3s90Dn+6VPn97ObJKfJ06ctFsza3fu3pQzZ07Zvu0z7yJ7+qOPP5Z7773Pnq5R4waZ/aK/W25d8eR4qzv1if92p25vaP2JRKBbj7Vw4ctSqWJF57BJfgZe14EtsjUDDZZrCvaygS6vVbuOHDjwjU6KvpSwefMGyWS1nE9JSk6ge+eObZI9ezY3W+1pQHscSCxpN/TaHbv35YfTZ85YQf/SdivzeAx0B2v5n5gh6xCINgFTbmiizY3yIIAAAggggAACCCCAAAIIIIAAAggggAACpgqY8lw4agPdeuKLW8Exb3fU5/piiMdAtwZd16xZKx07dZbA4LRzPkaPfkL+2zzplxK0y+7WrdvI9h07nF19n6tXfSgFClzhW6bdoGt36E764vO9viCqLg/sBl2XRSrQrWN0L3njtZDdrmtZnKRjeN99z72+LttDtWR39gkW9J4580UZOmy4s4nca7UWHjp0cMjAuH6H9F/u3LndfZIKdGu39ZMmTXS31wntmcBpme1bETAzb+4cqVatqm/p6DFjZfLkqXHXoluvDx3LnYSAyQKm3NCYbEzZEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBaBIw5blwVAe6NaiWVLffkT7pmzZ+IhdddFG6HPbw4cNSsVKVdMlbMy1TpowdlE3qAH///bf8/vtR2bJlizzef6B89913Se0i2tpaxyG++uqrE2yrY3u//c5S6WV12X3Gat0bKmmX19r1tTeVKl1OTpw44S56/73lUqjQNe78wYMH5YYatd15Z+L666vLS3NedGbtT2+X386KrVs2+QLAgd2z79+/x9da+rF+/eXll19xdnc/tdydOnaQvHnzusucCW3FPnbcOJk1a7azyPfZvVtXedAa7zywVfYvv/wiHTreL926Piy1a9dy99Hzcf0NZ+d1RblyZeXFWTMkj9VdvDepT7Pmd4kGn73npnv3nvLGkje9m/qmFy6YL5UqVfItu7PZf63W4wlb3fs2smYqV64kC16Z71v89ddfS+0b69kB+Vb3tnTX/frrb1KhYmV3/oP335Vrrjl7DSU3uO5mEGUTOnzBrJnTo6xUFAeBlAmYckOTslqxNQIIIIAAAggggAACCCCAAAIIIIAAAggggEAoAVOeC0d1oHvGzFkybNiIUMbnZPmjffvI/fd3TJdjT5nygowaPSZd8o5UphporVSpoh2M/s0KYq5es0Z++OHHSB3+nB9Hx90uX768XFmwoOz//HNZu/YjX6A+VAGzZMliB6t1359+/FFWWq2njxw5EmrzkMv1JYwGDW6SP/74Q5YtWy4nT54MuS0r0l9gwIDHpV3bNul/II6AQDoKmHJDk44EZI0AAggggAACCCCAAAIIIIAAAggggAACCMSVgCnPhaM60K3jEOt4xNGUtPvyXj17SPPmd4atZbe25F648FV7rGkdB5mEAAKxIbBq5QopWLBAbFSGWsStgCk3NHF7gqg4AggggAACCCCAAAIIIIAAAggggAACCCAQZgFTngtHdaBbz8k9LVvJunXrw3x6wpOdBr3DkQhuh0ORPBCILgEdp1y7jCchYLqAKTc0pjtTfgQQQAABBBBAAAEEEEAAAQQQQAABBBBAIFoETHkuHPWB7oULF0mfvo9Fy3mlHAgggECyBMaMfsLq+aFZsrZlIwSiWcCUG5poNqRsCCCAAAIIIIAAAggggAACCCCAAAIIIICASQKmPBeO+kC3nvTK11WVQ4d+Men8U1YEEIhjgXz58sqGT6OzJ4o4Pi1UPZUCptzQpLJ67IYAAggggAACCCCAAAIIIIAAAggggAACCCAQIGDKc2EjAt3PPDtRJkx4OoCYWQQQQCA6BXr06CZdH34oOgtHqRBIoYApNzQprBabI4AAAggggAACCCCAAAIIIIAAAggggAACCIQQMOW5sBGB7uPHj0u16jXk6NGjIbhZjAACCESHQK5cuWTdx2skR44c0VEgSoFAGgVMuaFJYzXZHQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQOD/BUx5LmxEoFtNJ0+ZKqNHj+UCQwABBKJaoG/fR6Tz/Z2iuowUDoGUCJhyQ5OSOrEtAggggAACCCCAAAIIIIAAAggggAACCCCAQGgBU54LGxPoVuoGDRvLvn37QquzBgEEEDiHAkWLFpXly94+hyXg0AiEX8CUG5rw15wcEUAAAQQQQAABBBBAAAEEEEAAAQQQQACB+BQw5bmwUYHuD1askA4d7o/PK4paI4BA1AtMmzZF6tapE/XlpIAIpETAlBualNSJbRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRCC5jyXNioQLdyDxo8VGbPnhNanjUIIIDAORBo3bqVDBk88BwcmUMikL4CptzQpK8CuSOAAAIIIIAAAggggAACCCCAAAIIIIAAAvEjYMpzYeMC3XoJNW16u2zfsSN+riZqigACUS1QpnRpWbJkcVSXkcIhkFoBU25oUls/9kMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDwC5jyXNjIQPeuXbvljjuby6lTp/zqzCGAAAIRFsiaNau89upCKVmyRISPzOEQiIyAKTc0kdHgKAgggAACCCCAAAIIIIAAAggggAACCCCAQOwLmPJc2MhAt14+S5cuky4PPhz7VxI1RACBqBaY9Nyz0qhRw6guI4VDIC0CptzQpKWO7IsAAggggAACCCCAAAIIIIAAAggggAACCCBwVsCU58LGBrqVeu7cedJ/wKCz6kwhgAACERQYPmyItGx5TwSPyKEQiLyAKTc0kZfhiAgggAACCCCAAAIIIIAAAggggAACCCCAQGwKmPJc2OhAt146M2bOkmHDRsTmVUStEEAgagUGDHhc2rVtE7Xlo2AIhEvAlBuacNWXfBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgXgXMOW5sPGBbr3QaNkd71836o9AZAVoyR1Zb452bgVMuaE5t0ocHQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiB0BU54Lx0SgWy8bHbO7R8/ecurUqdi5iqgJAghElUDWrFllwvhxjMkdVWeFwqS3gCk3NOntQP4IIIAAAggggAACCCCAAAIIIIAAAggggEC8CJjyXDhmAt16Ye3atVsefbSfbN+xI16uM+qJAAIREihTurSMGjVSSpYsEaEjchgEokPAlBua6NCiFAgggAACCCCAAAIIIIAAAggggAACCCCAgPkCpjwXjqlAt3PZDBo8VGbPnuPM8okAAgikSaB161YyZPDANOXBzgiYKmDKDY2pvpQbAQQQQAABBBBAAAEEEEAAAQQQQAABBBCINgFTngvHZKBbL4YPVqyQMWOelH379kXbtUF5EEDAEIGiRYtKnz69pG6dOoaUmGIiEH4BU25owl9zckQAAQQQQAABBBBAAAEEEEAAAQQQQAABBOJTwJTnwjEb6HYuu8lTpsqkSZPl6NGjziI+EUAAgUQFcuXKJV26dJbO93dKdDtWIhAPAqbc0MTDuaCOCCCAAAIIIIAAAggggAACCCCAAAIIIIBAJARMeS4c84FuPdnHjx+X6TNmypw5L8mhQ79E4vxzDAQQMFAgX7680qrVvdK+XVvJkSOHgTWgyAiEX8CUG5rw15wcEUAAAQQQQAABBBBAAAEEEEAAAQQQQACB+BQw5blwXAS6vZfgwoWLZPHrb8i6deu9i5lGAIE4FqhWrarcftut0rx5szhWoOoIBBcw5YYmeOlZigACCCCAAAIIIIAAAggggAACCCCAAAIIIJBSAVOeC8ddoNs5kQcOfCPvf/CBrF69Rtav/0ROnTrlrOITAQRiXCBr1qxStWoVqVmzhtSrW1cKFiwQ4zWmegikXsCUG5rU15A9EUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBLwCpjwXjttAt/dk6fSWLVtl586dsm//5/L111/L99//IIcP/2KN7X1M/vzzT/nnn38Cd2EeAQSiVCBDhgySJUsWyZUrp1x0UV657LL8cuWVV0rRIoWlVKlSUr58uSgtOcVCIPoEDh/+1f5/4EUXXSj63SIhgAACCCCAAAIIIIAAAggggAACCCCAAAIIxK6AxkT1ubA+D9bnwtGcCHRH89mhbAgggAACCJxjgSNHfpfTp09L7ty5JHPmzOe4NBweAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAID0F/vrrL/n996OSKVMmyZMnd3oeKs15E+hOMyEZIIAAAgggELsCx4+fkD/++EOyZ88u2bKdH7sVpWYIIIAAAggggAACCCCAAAIIIIAAAggggAACcvLkH3LixAk5//zzJUeO7FEtQqA7qk8PhUMAAQQQQODcCpw6dUqOHTtut+bWVt0kBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgdgV0Nbc2qo7Z84ckjVr1qiuKIHuqD49FA4BBBBAAIFzK+CMx6KluOCCPJIxY8ZzWyCOjgACCCCAAAIIIIAAAggggAACCCCAAAIIIJAuAmfOnJHffjti563jc+s43dGcCHRH89mhbAgggAACCESBgNN9uQld1UQBF0VAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSMFDDtWTCBbiMvMwqNAAIIIIBA5AROnz4jR478+xafdl+eOXPmyB2cIyGAAAIIIIAAAggggAACCCCAAAIIIIAAAgiku4B2V67dlmvKkyePZMoU/b17EuhO98uCAyCAAAIIIGC+wIkTJ+XkyZPWzU0m6yYnt/kVogYIIIAAAggggAACCCCAAAIIIIAAAggggAACrsCRI7/L6dOnJVu2bJI9ezZ3eTRPEOiO5rND2RBAAAEEEIgiAedGJ2vWLJIzZ84oKhlFQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEitwLFjx+TUqT+Na+hEoDu1Z5z9EEAAAQQQiDMB7cL8999/l3/++UcIdsfZyae6CCCAAAIIIIAAAggggAACCCCAAAIIIBCTAk6QO0OGDJI7d24juix3TgSBbkeCTwQQQAABBBBIUkC7rtFxWjTYrd2Yaxc2jNmdJBsbIIAAAggggAACCCCAAAIIIIAAAggggAACUSWgY3LrkJX6zPffIHcu+5lvVBUyicIQ6E4CiNUIIIAAAggg4BfQlt3Hjx+3b4B0zfnnn2/9yyoZM2b0b8gcAggggAACCCCAAAIIIIAAAggggAACCCCAQFQJnDlzRv7445T17w+7XNqgKUeOHEa15HZACXQ7EnwigAACCCCAQIoE9G2/kydPuvtoy279lylTRvvNP30LkIQAAggggAACCCCAAAIIIIAAAggggAACCCBw7gS0d05tta0NmLQVt/5zUrZs2exeO5150z4JdJt2xigvAggggAACUSSgN0enTp19+y+KikZREEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBIIIaC+dWbNmNbIVt7c6BLq9GkwjgAACCCCAQKoE9K3AP//8034rUN8O1O5vdBkJAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA4NwJaM+bOuykdlGuvXFmyZLFHpP73JUofEcm0B0+S3JCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEIiAAIHuCCBzCAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB8AkQ6A6fJTkhgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCERAgEB3BJA5BAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBA+AQIdIfPkpwQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBCIgQKA7AsgcAgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgfAIEusNnSU4IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAhEQINAdAWQOgQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQPgEC3eGzJCcEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgQgIEOiOADKHQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAInwCB7vBZkhMCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQAQECHRHAJlDIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgiET4BAd/gsyQkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIAICBLojgMwhEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTCJ0CgO3yW5IQAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEAEBAt0RQOYQCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALhEyDQHT5LckIAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiIAAge4IIHMIBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHwCRDoDp8lOSGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIRECAQHcEkDkEAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggED4BAh0h8+SnBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEIiBAoDsCyBwCAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCB8AgS6w2dJTggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACERAg0B0BZA6BAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBA+AQLd4bMkJwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBCAgQ6I4AModAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEAifAIHu8FmSEwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBABAQIdEcAmUMggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCIRPgEB3+CzJCQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgAgIEuiOAzCEQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMInQKA7fJbkhAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQAQEC3RFA5hAIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAuETINAdPktyQgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIgACB7gggcwgEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgfAJEOgOnyU5IYAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghEQIBAdwSQOQQCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQPgECHSHz5KcEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQiIECgOwLIHAIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIHwC/weQENk6VSFYnwAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Go to the bike sharing demand competition and agree to the terms\n",
    "![kaggle6.png](attachment:kaggle6.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:23:02.338275Z",
     "iopub.status.busy": "2025-12-07T15:23:02.337907Z",
     "iopub.status.idle": "2025-12-07T15:23:02.342441Z",
     "shell.execute_reply": "2025-12-07T15:23:02.341567Z",
     "shell.execute_reply.started": "2025-12-07T15:23:02.338241Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from autogluon.tabular import TabularPredictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:23:12.144878Z",
     "iopub.status.busy": "2025-12-07T15:23:12.144602Z",
     "iopub.status.idle": "2025-12-07T15:23:12.212043Z",
     "shell.execute_reply": "2025-12-07T15:23:12.211355Z",
     "shell.execute_reply.started": "2025-12-07T15:23:12.144855Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
       "0 2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "1 2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2 2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "3 2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "4 2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "   humidity  windspeed  casual  registered  count  \n",
       "0        81        0.0       3          13     16  \n",
       "1        80        0.0       8          32     40  \n",
       "2        80        0.0       5          27     32  \n",
       "3        75        0.0       3          10     13  \n",
       "4        75        0.0       0           1      1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the train dataset in pandas by reading the csv\n",
    "# Set the parsing of the datetime column so you can use some of the `dt` features in pandas later\n",
    "train = pd.read_csv(\"train.csv\",parse_dates=[\"datetime\"])\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:23:37.949498Z",
     "iopub.status.busy": "2025-12-07T15:23:37.949231Z",
     "iopub.status.idle": "2025-12-07T15:23:37.994357Z",
     "shell.execute_reply": "2025-12-07T15:23:37.993497Z",
     "shell.execute_reply.started": "2025-12-07T15:23:37.949476Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10886</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.00000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2011-12-27 05:56:22.399411968</td>\n",
       "      <td>2.506614</td>\n",
       "      <td>0.028569</td>\n",
       "      <td>0.680875</td>\n",
       "      <td>1.418427</td>\n",
       "      <td>20.23086</td>\n",
       "      <td>23.655084</td>\n",
       "      <td>61.886460</td>\n",
       "      <td>12.799395</td>\n",
       "      <td>36.021955</td>\n",
       "      <td>155.552177</td>\n",
       "      <td>191.574132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.82000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2011-07-02 07:15:00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.94000</td>\n",
       "      <td>16.665000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>7.001500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2012-01-01 20:30:00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.50000</td>\n",
       "      <td>24.240000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>12.998000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>145.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2012-07-01 12:45:00</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>26.24000</td>\n",
       "      <td>31.060000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>16.997900</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>284.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2012-12-19 23:00:00</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>45.455000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>56.996900</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>886.000000</td>\n",
       "      <td>977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.116174</td>\n",
       "      <td>0.166599</td>\n",
       "      <td>0.466159</td>\n",
       "      <td>0.633839</td>\n",
       "      <td>7.79159</td>\n",
       "      <td>8.474601</td>\n",
       "      <td>19.245033</td>\n",
       "      <td>8.164537</td>\n",
       "      <td>49.960477</td>\n",
       "      <td>151.039033</td>\n",
       "      <td>181.144454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            datetime        season       holiday  \\\n",
       "count                          10886  10886.000000  10886.000000   \n",
       "mean   2011-12-27 05:56:22.399411968      2.506614      0.028569   \n",
       "min              2011-01-01 00:00:00      1.000000      0.000000   \n",
       "25%              2011-07-02 07:15:00      2.000000      0.000000   \n",
       "50%              2012-01-01 20:30:00      3.000000      0.000000   \n",
       "75%              2012-07-01 12:45:00      4.000000      0.000000   \n",
       "max              2012-12-19 23:00:00      4.000000      1.000000   \n",
       "std                              NaN      1.116174      0.166599   \n",
       "\n",
       "         workingday       weather         temp         atemp      humidity  \\\n",
       "count  10886.000000  10886.000000  10886.00000  10886.000000  10886.000000   \n",
       "mean       0.680875      1.418427     20.23086     23.655084     61.886460   \n",
       "min        0.000000      1.000000      0.82000      0.760000      0.000000   \n",
       "25%        0.000000      1.000000     13.94000     16.665000     47.000000   \n",
       "50%        1.000000      1.000000     20.50000     24.240000     62.000000   \n",
       "75%        1.000000      2.000000     26.24000     31.060000     77.000000   \n",
       "max        1.000000      4.000000     41.00000     45.455000    100.000000   \n",
       "std        0.466159      0.633839      7.79159      8.474601     19.245033   \n",
       "\n",
       "          windspeed        casual    registered         count  \n",
       "count  10886.000000  10886.000000  10886.000000  10886.000000  \n",
       "mean      12.799395     36.021955    155.552177    191.574132  \n",
       "min        0.000000      0.000000      0.000000      1.000000  \n",
       "25%        7.001500      4.000000     36.000000     42.000000  \n",
       "50%       12.998000     17.000000    118.000000    145.000000  \n",
       "75%       16.997900     49.000000    222.000000    284.000000  \n",
       "max       56.996900    367.000000    886.000000    977.000000  \n",
       "std        8.164537     49.960477    151.039033    181.144454  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Simple output of the train dataset to view some of the min/max/varition of the dataset features.\n",
    "# Train dataset summary\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:24:16.907691Z",
     "iopub.status.busy": "2025-12-07T15:24:16.907413Z",
     "iopub.status.idle": "2025-12-07T15:24:16.924719Z",
     "shell.execute_reply": "2025-12-07T15:24:16.923937Z",
     "shell.execute_reply.started": "2025-12-07T15:24:16.907668Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10886 entries, 0 to 10885\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   datetime    10886 non-null  datetime64[ns]\n",
      " 1   season      10886 non-null  int64         \n",
      " 2   holiday     10886 non-null  int64         \n",
      " 3   workingday  10886 non-null  int64         \n",
      " 4   weather     10886 non-null  int64         \n",
      " 5   temp        10886 non-null  float64       \n",
      " 6   atemp       10886 non-null  float64       \n",
      " 7   humidity    10886 non-null  int64         \n",
      " 8   windspeed   10886 non-null  float64       \n",
      " 9   casual      10886 non-null  int64         \n",
      " 10  registered  10886 non-null  int64         \n",
      " 11  count       10886 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(3), int64(8)\n",
      "memory usage: 1020.7 KB\n"
     ]
    }
   ],
   "source": [
    "# Information of the feature datatypes\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:25:08.240372Z",
     "iopub.status.busy": "2025-12-07T15:25:08.239966Z",
     "iopub.status.idle": "2025-12-07T15:25:08.246120Z",
     "shell.execute_reply": "2025-12-07T15:25:08.245390Z",
     "shell.execute_reply.started": "2025-12-07T15:25:08.240347Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime      0\n",
       "season        0\n",
       "holiday       0\n",
       "workingday    0\n",
       "weather       0\n",
       "temp          0\n",
       "atemp         0\n",
       "humidity      0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "count         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for the count of missing values\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:25:29.157733Z",
     "iopub.status.busy": "2025-12-07T15:25:29.157474Z",
     "iopub.status.idle": "2025-12-07T15:25:29.177454Z",
     "shell.execute_reply": "2025-12-07T15:25:29.176807Z",
     "shell.execute_reply.started": "2025-12-07T15:25:29.157714Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>11.365</td>\n",
       "      <td>56</td>\n",
       "      <td>26.0027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
       "0 2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
       "1 2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n",
       "2 2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n",
       "3 2011-01-20 03:00:00       1        0           1        1  10.66  12.880   \n",
       "4 2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n",
       "\n",
       "   humidity  windspeed  \n",
       "0        56    26.0027  \n",
       "1        56     0.0000  \n",
       "2        56     0.0000  \n",
       "3        56    11.0014  \n",
       "4        56    11.0014  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the test pandas dataframe in pandas by reading the csv, remember to parse the datetime!\n",
    "test = pd.read_csv(\"test.csv\",parse_dates=[\"datetime\"])\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:26:03.095953Z",
     "iopub.status.busy": "2025-12-07T15:26:03.095572Z",
     "iopub.status.idle": "2025-12-07T15:26:03.102883Z",
     "shell.execute_reply": "2025-12-07T15:26:03.102232Z",
     "shell.execute_reply.started": "2025-12-07T15:26:03.095925Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime      0\n",
       "season        0\n",
       "holiday       0\n",
       "workingday    0\n",
       "weather       0\n",
       "temp          0\n",
       "atemp         0\n",
       "humidity      0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "count         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Similar Check for the count of missing values in test data\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:26:48.420973Z",
     "iopub.status.busy": "2025-12-07T15:26:48.420635Z",
     "iopub.status.idle": "2025-12-07T15:26:48.424845Z",
     "shell.execute_reply": "2025-12-07T15:26:48.423976Z",
     "shell.execute_reply.started": "2025-12-07T15:26:48.420944Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Shape :  (10886, 12)\n",
      "Test Shape  :  (6493, 9)\n"
     ]
    }
   ],
   "source": [
    "# Shape of the train and test data\n",
    "print(\"Train Shape : \", train.shape)\n",
    "print(\"Test Shape  : \", test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:31:07.977981Z",
     "iopub.status.busy": "2025-12-07T15:31:07.977710Z",
     "iopub.status.idle": "2025-12-07T15:31:07.981682Z",
     "shell.execute_reply": "2025-12-07T15:31:07.981165Z",
     "shell.execute_reply.started": "2025-12-07T15:31:07.977961Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features in Training data:\n",
      " Index(['datetime', 'season', 'holiday', 'workingday', 'weather', 'temp',\n",
      "       'atemp', 'humidity', 'windspeed', 'casual', 'registered', 'count'],\n",
      "      dtype='object')\n",
      "\n",
      "Features in Testing data:\n",
      " Index(['datetime', 'season', 'holiday', 'workingday', 'weather', 'temp',\n",
      "       'atemp', 'humidity', 'windspeed'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# List all the features in train and test data to identify features that can be safely removed\n",
    "print(\"Features in Training data:\\n\", train.columns)\n",
    "print(\"\\nFeatures in Testing data:\\n\", test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:31:10.340039Z",
     "iopub.status.busy": "2025-12-07T15:31:10.339728Z",
     "iopub.status.idle": "2025-12-07T15:31:10.345146Z",
     "shell.execute_reply": "2025-12-07T15:31:10.344284Z",
     "shell.execute_reply.started": "2025-12-07T15:31:10.340000Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing features are\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['count', 'registered', 'casual']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Thus missing features are\n",
    "print(\"Missing features are\")\n",
    "list(set(train.columns).difference(set(test.columns)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset Dictionary\n",
    "**temp** - temperature in Celsius<br>\n",
    "**atemp** - \"feels like\" temperature in Celsius<br>\n",
    "**humidity** - relative humidity<br>\n",
    "**windspeed** - wind speed<br>\n",
    "**casual** - number of non-registered user rentals initiated (removed)<br>\n",
    "**registered** - number of registered user rentals initiated (removed)<br>\n",
    "**count** - number of total rentals<br>\n",
    "**datetime** - hourly date + timestamp<br>\n",
    "**season** - 1 = spring, 2 = summer, 3 = fall, 4 = winter<br>\n",
    "**holiday** - whether the day is considered a holiday<br>\n",
    "**workingday** - whether the day is neither a weekend nor holiday<br>\n",
    "**weather** -\n",
    "\n",
    "1: Clear, Few clouds, Partly cloudy, Partly cloudy<br>\n",
    "2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist<br>\n",
    "3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds<br>\n",
    "4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:27:04.566775Z",
     "iopub.status.busy": "2025-12-07T15:27:04.566497Z",
     "iopub.status.idle": "2025-12-07T15:27:04.580676Z",
     "shell.execute_reply": "2025-12-07T15:27:04.579822Z",
     "shell.execute_reply.started": "2025-12-07T15:27:04.566748Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  count\n",
       "0 2011-01-20 00:00:00      0\n",
       "1 2011-01-20 01:00:00      0\n",
       "2 2011-01-20 02:00:00      0\n",
       "3 2011-01-20 03:00:00      0\n",
       "4 2011-01-20 04:00:00      0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same thing as train and test dataset\n",
    "submission = pd.read_csv(\"sampleSubmission.csv\",parse_dates=[\"datetime\"])\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Train a model using AutoGluon’s Tabular Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Requirements:\n",
    "* We are prediting `count`, so it is the label we are setting.\n",
    "* Ignore `casual` and `registered` columns as they are also not present in the test dataset. \n",
    "* Use the `root_mean_squared_error` as the metric to use for evaluation.\n",
    "* Set a time limit of 10 minutes (600 seconds).\n",
    "* Use the preset `best_quality` to focus on creating the best model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:39:03.266995Z",
     "iopub.status.busy": "2025-12-07T15:39:03.266736Z",
     "iopub.status.idle": "2025-12-07T15:39:03.270192Z",
     "shell.execute_reply": "2025-12-07T15:39:03.269382Z",
     "shell.execute_reply.started": "2025-12-07T15:39:03.266975Z"
    }
   },
   "outputs": [],
   "source": [
    "# Requirements:\n",
    "label = 'count'\n",
    "ignored_columns = [\"casual\", \"registered\"]   # Ignored columns during training\n",
    "eval_metric = 'root_mean_squared_error'\n",
    "time_limit = 600                             # 10 minutes that is 600 seconds\n",
    "train_data = train\n",
    "presets = \"best_quality\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:40:21.524396Z",
     "iopub.status.busy": "2025-12-07T15:40:21.523973Z",
     "iopub.status.idle": "2025-12-07T15:50:29.571302Z",
     "shell.execute_reply": "2025-12-07T15:50:29.569653Z",
     "shell.execute_reply.started": "2025-12-07T15:40:21.524369Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No path specified. Models will be saved in: \"AutogluonModels/ag-20251207_154021\"\n",
      "Verbosity: 2 (Standard Logging)\n",
      "=================== System Info ===================\n",
      "AutoGluon Version:  1.4.0\n",
      "Python Version:     3.12.9\n",
      "Operating System:   Linux\n",
      "Platform Machine:   x86_64\n",
      "Platform Version:   #1 SMP PREEMPT_DYNAMIC Mon Nov  3 18:38:36 UTC 2025\n",
      "CPU Count:          2\n",
      "Memory Avail:       1.45 GB / 3.74 GB (38.8%)\n",
      "Disk Space Avail:   4.87 GB / 4.99 GB (97.6%)\n",
      "\tWARNING: Available disk space is low and there is a risk that AutoGluon will run out of disk during fit, causing an exception. \n",
      "\tWe recommend a minimum available disk space of 10 GB, and large datasets may require more.\n",
      "===================================================\n",
      "Presets specified: ['best_quality']\n",
      "Using hyperparameters preset: hyperparameters='zeroshot'\n",
      "Setting dynamic_stacking from 'auto' to True. Reason: Enable dynamic_stacking when use_bag_holdout is disabled. (use_bag_holdout=False)\n",
      "Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=1\n",
      "DyStack is enabled (dynamic_stacking=True). AutoGluon will try to determine whether the input data is affected by stacked overfitting and enable or disable stacking as a consequence.\n",
      "\tThis is used to identify the optimal `num_stack_levels` value. Copies of AutoGluon will be fit on subsets of the data. Then holdout validation data is used to detect stacked overfitting.\n",
      "\tRunning DyStack for up to 150s of the 600s of remaining time (25%).\n",
      "\tRunning DyStack sub-fit in a ray process to avoid memory leakage. Enabling ray logging (enable_ray_logging=True). Specify `ds_args={'enable_ray_logging': False}` if you experience logging issues.\n",
      "2025-12-07 15:40:26,554\tWARNING services.py:2070 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 407875584 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=0.84gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.\n",
      "2025-12-07 15:40:27,855\tINFO worker.py:1843 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n",
      "\t\tContext path: \"/home/sagemaker-user/udacity-AWS-ml-engineer-nanodegree/project/AutogluonModels/ag-20251207_154021/ds_sub_fit/sub_fit_ho\"\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Running DyStack sub-fit ...\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Beginning AutoGluon training ... Time limit = 144s\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m AutoGluon will save models to \"/home/sagemaker-user/udacity-AWS-ml-engineer-nanodegree/project/AutogluonModels/ag-20251207_154021/ds_sub_fit/sub_fit_ho\"\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Train Data Rows:    9676\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Train Data Columns: 11\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Label Column:       count\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Problem Type:       regression\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Preprocessing data ...\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Using Feature Generators to preprocess the data ...\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Dropping user-specified ignored columns: ['casual', 'registered']\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Fitting AutoMLPipelineFeatureGenerator...\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tAvailable Memory:                    1071.15 MB\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tTrain Data (Original)  Memory Usage: 0.66 MB (0.1% of available memory)\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tStage 1 Generators:\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t\tFitting AsTypeFeatureGenerator...\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tStage 2 Generators:\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t\tFitting FillNaFeatureGenerator...\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tStage 3 Generators:\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t\tFitting IdentityFeatureGenerator...\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t\tFitting DatetimeFeatureGenerator...\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tStage 4 Generators:\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t\tFitting DropUniqueFeatureGenerator...\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tStage 5 Generators:\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t\tFitting DropDuplicatesFeatureGenerator...\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tTypes of features in original data (raw dtype, special dtypes):\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t\t('datetime', []) : 1 | ['datetime']\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t\t('float', [])    : 3 | ['temp', 'atemp', 'windspeed']\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t\t('int', [])      : 5 | ['season', 'holiday', 'workingday', 'weather', 'humidity']\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tTypes of features in processed data (raw dtype, special dtypes):\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t\t('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t\t('int', [])                  : 3 | ['season', 'weather', 'humidity']\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t\t('int', ['bool'])            : 2 | ['holiday', 'workingday']\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t\t('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t0.1s = Fit runtime\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t9 features in original data used to generate 13 features in processed data.\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tTrain Data (Processed) Memory Usage: 0.83 MB (0.1% of available memory)\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Data preprocessing and feature engineering runtime = 0.08s ...\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tTo change this, specify the eval_metric parameter of Predictor()\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Large model count detected (110 configs) ... Only displaying the first 3 models of each family. To see all, set `verbosity=3`.\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m User-specified model hyperparameters to be fit:\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m {\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t'NN_TORCH': [{}, {'activation': 'elu', 'dropout_prob': 0.10077639529843717, 'hidden_size': 108, 'learning_rate': 0.002735937344002146, 'num_layers': 4, 'use_batchnorm': True, 'weight_decay': 1.356433327634438e-12, 'ag_args': {'name_suffix': '_r79', 'priority': -2}}, {'activation': 'elu', 'dropout_prob': 0.11897478034205347, 'hidden_size': 213, 'learning_rate': 0.0010474382260641949, 'num_layers': 4, 'use_batchnorm': False, 'weight_decay': 5.594471067786272e-10, 'ag_args': {'name_suffix': '_r22', 'priority': -7}}],\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, {'learning_rate': 0.03, 'num_leaves': 128, 'feature_fraction': 0.9, 'min_data_in_leaf': 3, 'ag_args': {'name_suffix': 'Large', 'priority': 0, 'hyperparameter_tune_kwargs': None}}],\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t'CAT': [{}, {'depth': 6, 'grow_policy': 'SymmetricTree', 'l2_leaf_reg': 2.1542798306067823, 'learning_rate': 0.06864209415792857, 'max_ctr_complexity': 4, 'one_hot_max_size': 10, 'ag_args': {'name_suffix': '_r177', 'priority': -1}}, {'depth': 8, 'grow_policy': 'Depthwise', 'l2_leaf_reg': 2.7997999596449104, 'learning_rate': 0.031375015734637225, 'max_ctr_complexity': 2, 'one_hot_max_size': 3, 'ag_args': {'name_suffix': '_r9', 'priority': -5}}],\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t'XGB': [{}, {'colsample_bytree': 0.6917311125174739, 'enable_categorical': False, 'learning_rate': 0.018063876087523967, 'max_depth': 10, 'min_child_weight': 0.6028633586934382, 'ag_args': {'name_suffix': '_r33', 'priority': -8}}, {'colsample_bytree': 0.6628423832084077, 'enable_categorical': False, 'learning_rate': 0.08775715546881824, 'max_depth': 5, 'min_child_weight': 0.6294123374222513, 'ag_args': {'name_suffix': '_r89', 'priority': -16}}],\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t'FASTAI': [{}, {'bs': 256, 'emb_drop': 0.5411770367537934, 'epochs': 43, 'layers': [800, 400], 'lr': 0.01519848858318159, 'ps': 0.23782946566604385, 'ag_args': {'name_suffix': '_r191', 'priority': -4}}, {'bs': 2048, 'emb_drop': 0.05070411322605811, 'epochs': 29, 'layers': [200, 100], 'lr': 0.08974235041576624, 'ps': 0.10393466140748028, 'ag_args': {'name_suffix': '_r102', 'priority': -11}}],\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m }\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m AutoGluon will fit 2 stack levels (L1 to L2) ...\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Fitting 106 L1 models, fit_strategy=\"sequential\" ...\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 95.94s of the 143.93s of remaining time.\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=1.51%)\n",
      "\u001b[36m(_ray_fit pid=2137)\u001b[0m Warning: Low available memory may cause OOM error if training continues\n",
      "\u001b[36m(_ray_fit pid=2137)\u001b[0m Estimated GBM model size: 0 MB\n",
      "\u001b[36m(_ray_fit pid=2137)\u001b[0m Warning: Early stopped GBM model prior to optimal result to avoid OOM error. Please increase available memory to avoid subpar model quality.\n",
      "\u001b[36m(_ray_fit pid=2137)\u001b[0m Available Memory: 484 MB\n",
      "\u001b[36m(_ray_fit pid=2136)\u001b[0m Available Memory: 484 MB\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m I0000 00:00:1765122044.733394    2047 chttp2_transport.cc:1182] ipv4:169.255.255.2:46357: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {grpc_status:14, http2_error:2, created_time:\"2025-12-07T15:40:44.731363101+00:00\"}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m(_ray_fit pid=2215)\u001b[0m [1000]\tvalid_set's rmse: 132.725\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m(_ray_fit pid=2215)\u001b[0m Warning: Low available memory may cause OOM error if training continues\u001b[32m [repeated 2x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.)\u001b[0m\n",
      "\u001b[36m(_ray_fit pid=2215)\u001b[0m Estimated GBM model size: 5 MB\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
      "\u001b[36m(_ray_fit pid=2215)\u001b[0m Warning: Early stopped GBM model prior to optimal result to avoid OOM error. Please increase available memory to avoid subpar model quality.\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
      "\u001b[36m(_ray_fit pid=2215)\u001b[0m Available Memory: 497 MB\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m(_ray_fit pid=2279)\u001b[0m [1000]\tvalid_set's rmse: 128.154\n",
      "\u001b[36m(_ray_fit pid=2318)\u001b[0m [1000]\tvalid_set's rmse: 135.845\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m(_ray_fit pid=2279)\u001b[0m Available Memory: 509 MB\n",
      "\u001b[36m(_ray_fit pid=2279)\u001b[0m Warning: Low available memory may cause OOM error if training continues\n",
      "\u001b[36m(_ray_fit pid=2279)\u001b[0m Estimated GBM model size: 9 MB\n",
      "\u001b[36m(_ray_fit pid=2279)\u001b[0m Warning: Early stopped GBM model prior to optimal result to avoid OOM error. Please increase available memory to avoid subpar model quality.\n",
      "\u001b[36m(_ray_fit pid=2318)\u001b[0m Warning: Low available memory may cause OOM error if training continues\n",
      "\u001b[36m(_ray_fit pid=2318)\u001b[0m Estimated GBM model size: 7 MB\n",
      "\u001b[36m(_ray_fit pid=2318)\u001b[0m Warning: Early stopped GBM model prior to optimal result to avoid OOM error. Please increase available memory to avoid subpar model quality.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m(_ray_fit pid=2357)\u001b[0m [1000]\tvalid_set's rmse: 137.712\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
      "\u001b[36m(_ray_fit pid=2357)\u001b[0m [5000]\tvalid_set's rmse: 135.358\u001b[32m [repeated 8x across cluster]\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m(_dystack pid=2003)\u001b[0m I0000 00:00:1765122087.407635    2048 chttp2_transport.cc:1182] ipv4:169.255.255.2:43119: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {created_time:\"2025-12-07T15:41:27.406511658+00:00\", http2_error:2, grpc_status:14}\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t-145.7723\t = Validation score   (-root_mean_squared_error)\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t42.79s\t = Training   runtime\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t3.92s\t = Validation runtime\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Fitting model: LightGBM_BAG_L1 ... Training model for up to 43.14s of the 91.13s of remaining time.\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=1.55%)\n",
      "\u001b[36m(_ray_fit pid=2318)\u001b[0m Available Memory: 507 MB\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m(_ray_fit pid=2431)\u001b[0m [1000]\tvalid_set's rmse: 129.285\u001b[32m [repeated 5x across cluster]\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m(_ray_fit pid=2432)\u001b[0m Warning: Low available memory may cause OOM error if training continues\n",
      "\u001b[36m(_ray_fit pid=2432)\u001b[0m Available Memory: 511 MB\n",
      "\u001b[36m(_ray_fit pid=2432)\u001b[0m Estimated GBM model size: 4 MB\n",
      "\u001b[36m(_ray_fit pid=2432)\u001b[0m Warning: Early stopped GBM model prior to optimal result to avoid OOM error. Please increase available memory to avoid subpar model quality.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m(_ray_fit pid=2496)\u001b[0m [1000]\tvalid_set's rmse: 135.098\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
      "\u001b[36m(_ray_fit pid=2561)\u001b[0m [1000]\tvalid_set's rmse: 124.896\n",
      "\u001b[36m(_ray_fit pid=2594)\u001b[0m [1000]\tvalid_set's rmse: 134.058\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m(_dystack pid=2003)\u001b[0m I0000 00:00:1765122112.643472    2051 chttp2_transport.cc:1182] ipv4:169.255.255.2:35635: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {created_time:\"2025-12-07T15:41:52.643469442+00:00\", http2_error:2, grpc_status:14}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m(_ray_fit pid=2629)\u001b[0m [1000]\tvalid_set's rmse: 134.479\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m(_dystack pid=2003)\u001b[0m I0000 00:00:1765122117.485265    2047 chttp2_transport.cc:1182] ipv4:169.255.255.2:38187: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {created_time:\"2025-12-07T15:41:57.485260789+00:00\", http2_error:2, grpc_status:14}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m(_ray_fit pid=2662)\u001b[0m [1000]\tvalid_set's rmse: 136.511\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t-131.8496\t = Validation score   (-root_mean_squared_error)\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t28.15s\t = Training   runtime\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t1.13s\t = Validation runtime\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 11.28s of the 59.27s of remaining time.\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tWarning: Reducing model 'n_estimators' from 300 -> 165 due to low memory. Expected memory usage reduced from 27.22% -> 15.0% of available memory...\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t-119.8184\t = Validation score   (-root_mean_squared_error)\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t9.09s\t = Training   runtime\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t0.33s\t = Validation runtime\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Fitting model: CatBoost_BAG_L1 ... Training model for up to 1.63s of the 49.63s of remaining time.\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tMemory not enough to fit 8 folds in parallel. Will train 2 folds in parallel instead (Estimated 36.38% memory usage per fold, 72.76%/80.00% total).\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=36.38%)\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tTime limit exceeded... Skipping CatBoost_BAG_L1.\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Fitting model: WeightedEnsemble_L2 ... Training model for up to 143.94s of the 44.73s of remaining time.\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tEnsemble Weights: {'RandomForestMSE_BAG_L1': 1.0}\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t-119.8184\t = Validation score   (-root_mean_squared_error)\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t0.05s\t = Training   runtime\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t0.0s\t = Validation runtime\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Fitting 106 L2 models, fit_strategy=\"sequential\" ...\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 44.64s of the 44.53s of remaining time.\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=1.64%)\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m I0000 00:00:1765122139.974969    2048 chttp2_transport.cc:1182] ipv4:169.255.255.2:41167: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {created_time:\"2025-12-07T15:42:19.974967078+00:00\", http2_error:2, grpc_status:14}\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t-120.6799\t = Validation score   (-root_mean_squared_error)\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t22.41s\t = Training   runtime\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t0.11s\t = Validation runtime\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Fitting model: LightGBM_BAG_L2 ... Training model for up to 18.41s of the 18.30s of remaining time.\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=1.54%)\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m I0000 00:00:1765122179.617099    2048 chttp2_transport.cc:1182] ipv4:169.255.255.2:34669: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {created_time:\"2025-12-07T15:42:59.617092874+00:00\", http2_error:2, grpc_status:14}\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t-120.7225\t = Validation score   (-root_mean_squared_error)\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t21.71s\t = Training   runtime\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t0.05s\t = Validation runtime\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Fitting model: WeightedEnsemble_L3 ... Training model for up to 143.94s of the -7.63s of remaining time.\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \tEnsemble Weights: {'RandomForestMSE_BAG_L1': 0.64, 'LightGBMXT_BAG_L2': 0.2, 'LightGBM_BAG_L2': 0.16}\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t-119.6136\t = Validation score   (-root_mean_squared_error)\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t0.03s\t = Training   runtime\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m \t0.0s\t = Validation runtime\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m AutoGluon training complete, total runtime = 151.71s ... Best model: WeightedEnsemble_L3 | Estimated inference throughput: 230.1 rows/s (1210 batch size)\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/home/sagemaker-user/udacity-AWS-ml-engineer-nanodegree/project/AutogluonModels/ag-20251207_154021/ds_sub_fit/sub_fit_ho\")\n",
      "\u001b[36m(_dystack pid=2003)\u001b[0m Deleting DyStack predictor artifacts (clean_up_fits=True) ...\n",
      "\u001b[33m(raylet)\u001b[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "\u001b[33m(raylet)\u001b[0m I0000 00:00:1765122193.733110    1958 chttp2_transport.cc:1182] ipv4:169.255.255.2:33069: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {grpc_status:14, http2_error:2, created_time:\"2025-12-07T15:43:13.730510236+00:00\"}\n",
      "Leaderboard on holdout data (DyStack):\n",
      "                    model  score_holdout   score_val              eval_metric  pred_time_test  pred_time_val    fit_time  pred_time_test_marginal  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0         LightGBM_BAG_L2    -117.919129 -120.722489  root_mean_squared_error        5.780917       5.435322  101.741279                 0.055483                0.050322          21.712719            2       True          6\n",
      "1       LightGBMXT_BAG_L2    -118.071990 -120.679868  root_mean_squared_error        5.853463       5.499512  102.436621                 0.128028                0.114512          22.408062            2       True          5\n",
      "2     WeightedEnsemble_L3    -118.126919 -119.613562  root_mean_squared_error        5.911371       5.550899  124.174591                 0.002425                0.001064           0.025251            3       True          7\n",
      "3  RandomForestMSE_BAG_L1    -118.673134 -119.818431  root_mean_squared_error        0.638798       0.334076    9.089286                 0.638798                0.334076           9.089286            1       True          3\n",
      "4     WeightedEnsemble_L2    -118.673134 -119.818431  root_mean_squared_error        0.641325       0.334865    9.141027                 0.002527                0.000789           0.051741            2       True          4\n",
      "5         LightGBM_BAG_L1    -130.706758 -131.849580  root_mean_squared_error        0.987328       1.130407   28.149422                 0.987328                1.130407          28.149422            1       True          2\n",
      "6       LightGBMXT_BAG_L1    -134.831284 -145.772291  root_mean_squared_error        4.099308       3.920517   42.789851                 4.099308                3.920517          42.789851            1       True          1\n",
      "\t1\t = Optimal   num_stack_levels (Stacked Overfitting Occurred: False)\n",
      "\t170s\t = DyStack   runtime |\t430s\t = Remaining runtime\n",
      "Starting main fit with num_stack_levels=1.\n",
      "\tFor future fit calls on this dataset, you can skip DyStack to save time: `predictor.fit(..., dynamic_stacking=False, num_stack_levels=1)`\n",
      "Beginning AutoGluon training ... Time limit = 430s\n",
      "AutoGluon will save models to \"/home/sagemaker-user/udacity-AWS-ml-engineer-nanodegree/project/AutogluonModels/ag-20251207_154021\"\n",
      "Train Data Rows:    10886\n",
      "Train Data Columns: 11\n",
      "Label Column:       count\n",
      "Problem Type:       regression\n",
      "Preprocessing data ...\n",
      "Using Feature Generators to preprocess the data ...\n",
      "Dropping user-specified ignored columns: ['casual', 'registered']\n",
      "Fitting AutoMLPipelineFeatureGenerator...\n",
      "\tAvailable Memory:                    1122.61 MB\n",
      "\tTrain Data (Original)  Memory Usage: 0.75 MB (0.1% of available memory)\n",
      "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
      "\tStage 1 Generators:\n",
      "\t\tFitting AsTypeFeatureGenerator...\n",
      "\t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n",
      "\tStage 2 Generators:\n",
      "\t\tFitting FillNaFeatureGenerator...\n",
      "\tStage 3 Generators:\n",
      "\t\tFitting IdentityFeatureGenerator...\n",
      "\t\tFitting DatetimeFeatureGenerator...\n",
      "\tStage 4 Generators:\n",
      "\t\tFitting DropUniqueFeatureGenerator...\n",
      "\tStage 5 Generators:\n",
      "\t\tFitting DropDuplicatesFeatureGenerator...\n",
      "\tTypes of features in original data (raw dtype, special dtypes):\n",
      "\t\t('datetime', []) : 1 | ['datetime']\n",
      "\t\t('float', [])    : 3 | ['temp', 'atemp', 'windspeed']\n",
      "\t\t('int', [])      : 5 | ['season', 'holiday', 'workingday', 'weather', 'humidity']\n",
      "\tTypes of features in processed data (raw dtype, special dtypes):\n",
      "\t\t('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
      "\t\t('int', [])                  : 3 | ['season', 'weather', 'humidity']\n",
      "\t\t('int', ['bool'])            : 2 | ['holiday', 'workingday']\n",
      "\t\t('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n",
      "\t0.1s = Fit runtime\n",
      "\t9 features in original data used to generate 13 features in processed data.\n",
      "\tTrain Data (Processed) Memory Usage: 0.93 MB (0.1% of available memory)\n",
      "Data preprocessing and feature engineering runtime = 0.15s ...\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n",
      "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n",
      "\tTo change this, specify the eval_metric parameter of Predictor()\n",
      "Large model count detected (110 configs) ... Only displaying the first 3 models of each family. To see all, set `verbosity=3`.\n",
      "User-specified model hyperparameters to be fit:\n",
      "{\n",
      "\t'NN_TORCH': [{}, {'activation': 'elu', 'dropout_prob': 0.10077639529843717, 'hidden_size': 108, 'learning_rate': 0.002735937344002146, 'num_layers': 4, 'use_batchnorm': True, 'weight_decay': 1.356433327634438e-12, 'ag_args': {'name_suffix': '_r79', 'priority': -2}}, {'activation': 'elu', 'dropout_prob': 0.11897478034205347, 'hidden_size': 213, 'learning_rate': 0.0010474382260641949, 'num_layers': 4, 'use_batchnorm': False, 'weight_decay': 5.594471067786272e-10, 'ag_args': {'name_suffix': '_r22', 'priority': -7}}],\n",
      "\t'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, {'learning_rate': 0.03, 'num_leaves': 128, 'feature_fraction': 0.9, 'min_data_in_leaf': 3, 'ag_args': {'name_suffix': 'Large', 'priority': 0, 'hyperparameter_tune_kwargs': None}}],\n",
      "\t'CAT': [{}, {'depth': 6, 'grow_policy': 'SymmetricTree', 'l2_leaf_reg': 2.1542798306067823, 'learning_rate': 0.06864209415792857, 'max_ctr_complexity': 4, 'one_hot_max_size': 10, 'ag_args': {'name_suffix': '_r177', 'priority': -1}}, {'depth': 8, 'grow_policy': 'Depthwise', 'l2_leaf_reg': 2.7997999596449104, 'learning_rate': 0.031375015734637225, 'max_ctr_complexity': 2, 'one_hot_max_size': 3, 'ag_args': {'name_suffix': '_r9', 'priority': -5}}],\n",
      "\t'XGB': [{}, {'colsample_bytree': 0.6917311125174739, 'enable_categorical': False, 'learning_rate': 0.018063876087523967, 'max_depth': 10, 'min_child_weight': 0.6028633586934382, 'ag_args': {'name_suffix': '_r33', 'priority': -8}}, {'colsample_bytree': 0.6628423832084077, 'enable_categorical': False, 'learning_rate': 0.08775715546881824, 'max_depth': 5, 'min_child_weight': 0.6294123374222513, 'ag_args': {'name_suffix': '_r89', 'priority': -16}}],\n",
      "\t'FASTAI': [{}, {'bs': 256, 'emb_drop': 0.5411770367537934, 'epochs': 43, 'layers': [800, 400], 'lr': 0.01519848858318159, 'ps': 0.23782946566604385, 'ag_args': {'name_suffix': '_r191', 'priority': -4}}, {'bs': 2048, 'emb_drop': 0.05070411322605811, 'epochs': 29, 'layers': [200, 100], 'lr': 0.08974235041576624, 'ps': 0.10393466140748028, 'ag_args': {'name_suffix': '_r102', 'priority': -11}}],\n",
      "\t'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
      "\t'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
      "}\n",
      "AutoGluon will fit 2 stack levels (L1 to L2) ...\n",
      "Fitting 106 L1 models, fit_strategy=\"sequential\" ...\n",
      "Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 286.48s of the 429.82s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.85%)\n",
      "\t-131.4609\t = Validation score   (-root_mean_squared_error)\n",
      "\t55.75s\t = Training   runtime\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1765122259.384285    1998 chttp2_transport.cc:1182] ipv4:169.255.255.2:33613: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {grpc_status:14, http2_error:2, created_time:\"2025-12-07T15:44:19.384267663+00:00\"}\n",
      "\t9.87s\t = Validation runtime\n",
      "Fitting model: LightGBM_BAG_L1 ... Training model for up to 221.51s of the 364.85s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=1.32%)\n",
      "I0000 00:00:1765122293.318950    2091 chttp2_transport.cc:1182] ipv4:169.255.255.2:39819: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {created_time:\"2025-12-07T15:44:53.318945597+00:00\", http2_error:2, grpc_status:14}\n",
      "\t-131.0542\t = Validation score   (-root_mean_squared_error)\n",
      "\t29.13s\t = Training   runtime\n",
      "\t1.4s\t = Validation runtime\n",
      "Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 187.33s of the 330.67s of remaining time.\n",
      "\tWarning: Reducing model 'n_estimators' from 300 -> 150 due to low memory. Expected memory usage reduced from 29.94% -> 15.0% of available memory...\n",
      "\t-117.1797\t = Validation score   (-root_mean_squared_error)\n",
      "\t7.97s\t = Training   runtime\n",
      "\t0.67s\t = Validation runtime\n",
      "Fitting model: CatBoost_BAG_L1 ... Training model for up to 178.29s of the 321.62s of remaining time.\n",
      "\tMemory not enough to fit 8 folds in parallel. Will train 2 folds in parallel instead (Estimated 32.31% memory usage per fold, 64.61%/80.00% total).\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=32.31%)\n",
      "\t-130.6793\t = Validation score   (-root_mean_squared_error)\n",
      "\t146.05s\t = Training   runtime\n",
      "\t0.12s\t = Validation runtime\n",
      "Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 28.48s of the 171.82s of remaining time.\n",
      "\t-124.6007\t = Validation score   (-root_mean_squared_error)\n",
      "\t7.61s\t = Training   runtime\n",
      "\t0.61s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 19.62s of the 162.96s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=0.87%)\n",
      "\t-142.4876\t = Validation score   (-root_mean_squared_error)\n",
      "\t43.47s\t = Training   runtime\n",
      "\t0.37s\t = Validation runtime\n",
      "Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.00s of the 115.41s of remaining time.\n",
      "\tEnsemble Weights: {'RandomForestMSE_BAG_L1': 1.0}\n",
      "\t-117.1797\t = Validation score   (-root_mean_squared_error)\n",
      "\t0.03s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "Fitting 106 L2 models, fit_strategy=\"sequential\" ...\n",
      "Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 115.36s of the 115.32s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=2.24%)\n",
      "\t-116.2372\t = Validation score   (-root_mean_squared_error)\n",
      "I0000 00:00:1765122538.333496    2092 chttp2_transport.cc:1182] ipv4:169.255.255.2:43075: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {grpc_status:14, http2_error:2, created_time:\"2025-12-07T15:48:58.332214988+00:00\"}\n",
      "\t24.52s\t = Training   runtime\n",
      "\t0.22s\t = Validation runtime\n",
      "Fitting model: LightGBM_BAG_L2 ... Training model for up to 85.94s of the 85.90s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=1.68%)\n",
      "\t-116.4951\t = Validation score   (-root_mean_squared_error)\n",
      "\t23.82s\t = Training   runtime\n",
      "\t0.08s\t = Validation runtime\n",
      "Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 57.52s of the 57.48s of remaining time.\n",
      "\tWarning: Reducing model 'n_estimators' from 300 -> 162 due to low memory. Expected memory usage reduced from 27.66% -> 15.0% of available memory...\n",
      "\t-119.3733\t = Validation score   (-root_mean_squared_error)\n",
      "\t22.57s\t = Training   runtime\n",
      "\t0.35s\t = Validation runtime\n",
      "Fitting model: CatBoost_BAG_L2 ... Training model for up to 34.32s of the 34.28s of remaining time.\n",
      "\tMemory not enough to fit 8 folds in parallel. Will train 2 folds in parallel instead (Estimated 35.11% memory usage per fold, 70.21%/80.00% total).\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=35.11%)\n",
      "\t-116.0303\t = Validation score   (-root_mean_squared_error)\n",
      "\t34.54s\t = Training   runtime\n",
      "\t0.05s\t = Validation runtime\n",
      "I0000 00:00:1765122628.278406    2091 chttp2_transport.cc:1182] ipv4:169.255.255.2:43775: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {grpc_status:14, http2_error:2, created_time:\"2025-12-07T15:50:28.278401161+00:00\"}\n",
      "Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.00s of the -4.18s of remaining time.\n",
      "\tEnsemble Weights: {'LightGBMXT_BAG_L2': 0.375, 'CatBoost_BAG_L2': 0.292, 'LightGBM_BAG_L2': 0.208, 'RandomForestMSE_BAG_L1': 0.125}\n",
      "\t-115.7429\t = Validation score   (-root_mean_squared_error)\n",
      "\t0.03s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "AutoGluon training complete, total runtime = 434.22s ... Best model: WeightedEnsemble_L3 | Estimated inference throughput: 110.9 rows/s (1361 batch size)\n",
      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/home/sagemaker-user/udacity-AWS-ml-engineer-nanodegree/project/AutogluonModels/ag-20251207_154021\")\n"
     ]
    }
   ],
   "source": [
    "predictor = TabularPredictor(label=label,\n",
    "problem_type= 'regression',\n",
    "eval_metric=eval_metric,\n",
    "learner_kwargs={'ignored_columns': ignored_columns}).fit(\n",
    "    train_data = train_data,\n",
    "    time_limit=time_limit,\n",
    "    presets=presets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Review AutoGluon's training run with ranking of models that did the best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:50:29.574752Z",
     "iopub.status.busy": "2025-12-07T15:50:29.573900Z",
     "iopub.status.idle": "2025-12-07T15:50:30.378082Z",
     "shell.execute_reply": "2025-12-07T15:50:30.377074Z",
     "shell.execute_reply.started": "2025-12-07T15:50:29.574725Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Summary of fit() ***\n",
      "Estimated performance of each model:\n",
      "                     model   score_val              eval_metric  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0      WeightedEnsemble_L3 -115.742943  root_mean_squared_error      13.395191  372.881253                0.000604           0.034408            3       True         12\n",
      "1          CatBoost_BAG_L2 -116.030347  root_mean_squared_error      13.088166  324.512115                0.047821          34.537133            2       True         11\n",
      "2        LightGBMXT_BAG_L2 -116.237175  root_mean_squared_error      13.263769  314.493837                0.223424          24.518855            2       True          8\n",
      "3          LightGBM_BAG_L2 -116.495144  root_mean_squared_error      13.123342  313.790857                0.082998          23.815875            2       True          9\n",
      "4   RandomForestMSE_BAG_L1 -117.179720  root_mean_squared_error       0.668591    7.966447                0.668591           7.966447            1       True          3\n",
      "5      WeightedEnsemble_L2 -117.179720  root_mean_squared_error       0.669350    7.999247                0.000758           0.032800            2       True          7\n",
      "6   RandomForestMSE_BAG_L2 -119.373345  root_mean_squared_error      13.392768  312.547758                0.352424          22.572776            2       True         10\n",
      "7     ExtraTreesMSE_BAG_L1 -124.600676  root_mean_squared_error       0.613296    7.605242                0.613296           7.605242            1       True          5\n",
      "8          CatBoost_BAG_L1 -130.679314  root_mean_squared_error       0.118717  146.053091                0.118717         146.053091            1       True          4\n",
      "9          LightGBM_BAG_L1 -131.054162  root_mean_squared_error       1.395436   29.128677                1.395436          29.128677            1       True          2\n",
      "10       LightGBMXT_BAG_L1 -131.460909  root_mean_squared_error       9.874825   55.749429                9.874825          55.749429            1       True          1\n",
      "11  NeuralNetFastAI_BAG_L1 -142.487574  root_mean_squared_error       0.369480   43.472096                0.369480          43.472096            1       True          6\n",
      "Number of models trained: 12\n",
      "Types of models trained:\n",
      "{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_CatBoost', 'StackerEnsembleModel_RF', 'StackerEnsembleModel_NNFastAiTabular', 'WeightedEnsembleModel', 'StackerEnsembleModel_XT'}\n",
      "Bagging used: True  (with 8 folds)\n",
      "Multi-layer stack-ensembling used: True  (with 3 levels)\n",
      "Feature Metadata (Processed):\n",
      "(raw dtype, special dtypes):\n",
      "('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
      "('int', [])                  : 3 | ['season', 'weather', 'humidity']\n",
      "('int', ['bool'])            : 2 | ['holiday', 'workingday']\n",
      "('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n",
      "Plot summary of models saved to file: /home/sagemaker-user/udacity-AWS-ml-engineer-nanodegree/project/AutogluonModels/ag-20251207_154021/SummaryOfModels.html\n",
      "*** End of fit() summary ***\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'model_types': {'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',\n",
       "  'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',\n",
       "  'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',\n",
       "  'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',\n",
       "  'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',\n",
       "  'NeuralNetFastAI_BAG_L1': 'StackerEnsembleModel_NNFastAiTabular',\n",
       "  'WeightedEnsemble_L2': 'WeightedEnsembleModel',\n",
       "  'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',\n",
       "  'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',\n",
       "  'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',\n",
       "  'CatBoost_BAG_L2': 'StackerEnsembleModel_CatBoost',\n",
       "  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},\n",
       " 'model_performance': {'LightGBMXT_BAG_L1': -131.46090891834504,\n",
       "  'LightGBM_BAG_L1': -131.054161598899,\n",
       "  'RandomForestMSE_BAG_L1': -117.17972024777579,\n",
       "  'CatBoost_BAG_L1': -130.67931398729877,\n",
       "  'ExtraTreesMSE_BAG_L1': -124.60067564699747,\n",
       "  'NeuralNetFastAI_BAG_L1': -142.48757368331653,\n",
       "  'WeightedEnsemble_L2': -117.17972024777579,\n",
       "  'LightGBMXT_BAG_L2': -116.23717547595763,\n",
       "  'LightGBM_BAG_L2': -116.49514409925132,\n",
       "  'RandomForestMSE_BAG_L2': -119.37334501213404,\n",
       "  'CatBoost_BAG_L2': -116.03034699200356,\n",
       "  'WeightedEnsemble_L3': -115.7429433118332},\n",
       " 'model_best': 'WeightedEnsemble_L3',\n",
       " 'model_paths': {'LightGBMXT_BAG_L1': ['LightGBMXT_BAG_L1'],\n",
       "  'LightGBM_BAG_L1': ['LightGBM_BAG_L1'],\n",
       "  'RandomForestMSE_BAG_L1': ['RandomForestMSE_BAG_L1'],\n",
       "  'CatBoost_BAG_L1': ['CatBoost_BAG_L1'],\n",
       "  'ExtraTreesMSE_BAG_L1': ['ExtraTreesMSE_BAG_L1'],\n",
       "  'NeuralNetFastAI_BAG_L1': ['NeuralNetFastAI_BAG_L1'],\n",
       "  'WeightedEnsemble_L2': ['WeightedEnsemble_L2'],\n",
       "  'LightGBMXT_BAG_L2': ['LightGBMXT_BAG_L2'],\n",
       "  'LightGBM_BAG_L2': ['LightGBM_BAG_L2'],\n",
       "  'RandomForestMSE_BAG_L2': ['RandomForestMSE_BAG_L2'],\n",
       "  'CatBoost_BAG_L2': ['CatBoost_BAG_L2'],\n",
       "  'WeightedEnsemble_L3': ['WeightedEnsemble_L3']},\n",
       " 'model_fit_times': {'LightGBMXT_BAG_L1': 55.74942874908447,\n",
       "  'LightGBM_BAG_L1': 29.128676891326904,\n",
       "  'RandomForestMSE_BAG_L1': 7.9664466381073,\n",
       "  'CatBoost_BAG_L1': 146.05309128761292,\n",
       "  'ExtraTreesMSE_BAG_L1': 7.605242013931274,\n",
       "  'NeuralNetFastAI_BAG_L1': 43.47209644317627,\n",
       "  'WeightedEnsemble_L2': 0.03280019760131836,\n",
       "  'LightGBMXT_BAG_L2': 24.518855333328247,\n",
       "  'LightGBM_BAG_L2': 23.815874576568604,\n",
       "  'RandomForestMSE_BAG_L2': 22.5727756023407,\n",
       "  'CatBoost_BAG_L2': 34.53713274002075,\n",
       "  'WeightedEnsemble_L3': 0.034407854080200195},\n",
       " 'model_pred_times': {'LightGBMXT_BAG_L1': 9.87482500076294,\n",
       "  'LightGBM_BAG_L1': 1.3954360485076904,\n",
       "  'RandomForestMSE_BAG_L1': 0.6685912609100342,\n",
       "  'CatBoost_BAG_L1': 0.11871695518493652,\n",
       "  'ExtraTreesMSE_BAG_L1': 0.6132955551147461,\n",
       "  'NeuralNetFastAI_BAG_L1': 0.3694796562194824,\n",
       "  'WeightedEnsemble_L2': 0.0007584095001220703,\n",
       "  'LightGBMXT_BAG_L2': 0.22342443466186523,\n",
       "  'LightGBM_BAG_L2': 0.08299756050109863,\n",
       "  'RandomForestMSE_BAG_L2': 0.35242366790771484,\n",
       "  'CatBoost_BAG_L2': 0.0478212833404541,\n",
       "  'WeightedEnsemble_L3': 0.0006036758422851562},\n",
       " 'num_bag_folds': 8,\n",
       " 'max_stack_level': 3,\n",
       " 'model_hyperparams': {'LightGBMXT_BAG_L1': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None},\n",
       "  'LightGBM_BAG_L1': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None},\n",
       "  'RandomForestMSE_BAG_L1': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None,\n",
       "   'use_child_oof': True},\n",
       "  'CatBoost_BAG_L1': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None},\n",
       "  'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None,\n",
       "   'use_child_oof': True},\n",
       "  'NeuralNetFastAI_BAG_L1': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None},\n",
       "  'WeightedEnsemble_L2': {'use_orig_features': False,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None},\n",
       "  'LightGBMXT_BAG_L2': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None},\n",
       "  'LightGBM_BAG_L2': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None},\n",
       "  'RandomForestMSE_BAG_L2': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None,\n",
       "   'use_child_oof': True},\n",
       "  'CatBoost_BAG_L2': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None},\n",
       "  'WeightedEnsemble_L3': {'use_orig_features': False,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None}},\n",
       " 'leaderboard':                      model   score_val              eval_metric  \\\n",
       " 0      WeightedEnsemble_L3 -115.742943  root_mean_squared_error   \n",
       " 1          CatBoost_BAG_L2 -116.030347  root_mean_squared_error   \n",
       " 2        LightGBMXT_BAG_L2 -116.237175  root_mean_squared_error   \n",
       " 3          LightGBM_BAG_L2 -116.495144  root_mean_squared_error   \n",
       " 4   RandomForestMSE_BAG_L1 -117.179720  root_mean_squared_error   \n",
       " 5      WeightedEnsemble_L2 -117.179720  root_mean_squared_error   \n",
       " 6   RandomForestMSE_BAG_L2 -119.373345  root_mean_squared_error   \n",
       " 7     ExtraTreesMSE_BAG_L1 -124.600676  root_mean_squared_error   \n",
       " 8          CatBoost_BAG_L1 -130.679314  root_mean_squared_error   \n",
       " 9          LightGBM_BAG_L1 -131.054162  root_mean_squared_error   \n",
       " 10       LightGBMXT_BAG_L1 -131.460909  root_mean_squared_error   \n",
       " 11  NeuralNetFastAI_BAG_L1 -142.487574  root_mean_squared_error   \n",
       " \n",
       "     pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  \\\n",
       " 0       13.395191  372.881253                0.000604           0.034408   \n",
       " 1       13.088166  324.512115                0.047821          34.537133   \n",
       " 2       13.263769  314.493837                0.223424          24.518855   \n",
       " 3       13.123342  313.790857                0.082998          23.815875   \n",
       " 4        0.668591    7.966447                0.668591           7.966447   \n",
       " 5        0.669350    7.999247                0.000758           0.032800   \n",
       " 6       13.392768  312.547758                0.352424          22.572776   \n",
       " 7        0.613296    7.605242                0.613296           7.605242   \n",
       " 8        0.118717  146.053091                0.118717         146.053091   \n",
       " 9        1.395436   29.128677                1.395436          29.128677   \n",
       " 10       9.874825   55.749429                9.874825          55.749429   \n",
       " 11       0.369480   43.472096                0.369480          43.472096   \n",
       " \n",
       "     stack_level  can_infer  fit_order  \n",
       " 0             3       True         12  \n",
       " 1             2       True         11  \n",
       " 2             2       True          8  \n",
       " 3             2       True          9  \n",
       " 4             1       True          3  \n",
       " 5             2       True          7  \n",
       " 6             2       True         10  \n",
       " 7             1       True          5  \n",
       " 8             1       True          4  \n",
       " 9             1       True          2  \n",
       " 10            1       True          1  \n",
       " 11            1       True          6  }"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor.fit_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:50:30.402520Z",
     "iopub.status.busy": "2025-12-07T15:50:30.402108Z",
     "iopub.status.idle": "2025-12-07T15:50:30.418662Z",
     "shell.execute_reply": "2025-12-07T15:50:30.417776Z",
     "shell.execute_reply.started": "2025-12-07T15:50:30.402466Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>score_val</th>\n",
       "      <th>eval_metric</th>\n",
       "      <th>pred_time_val</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>pred_time_val_marginal</th>\n",
       "      <th>fit_time_marginal</th>\n",
       "      <th>stack_level</th>\n",
       "      <th>can_infer</th>\n",
       "      <th>fit_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WeightedEnsemble_L3</td>\n",
       "      <td>-115.742943</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>13.395191</td>\n",
       "      <td>372.881253</td>\n",
       "      <td>0.000604</td>\n",
       "      <td>0.034408</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CatBoost_BAG_L2</td>\n",
       "      <td>-116.030347</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>13.088166</td>\n",
       "      <td>324.512115</td>\n",
       "      <td>0.047821</td>\n",
       "      <td>34.537133</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LightGBMXT_BAG_L2</td>\n",
       "      <td>-116.237175</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>13.263769</td>\n",
       "      <td>314.493837</td>\n",
       "      <td>0.223424</td>\n",
       "      <td>24.518855</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LightGBM_BAG_L2</td>\n",
       "      <td>-116.495144</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>13.123342</td>\n",
       "      <td>313.790857</td>\n",
       "      <td>0.082998</td>\n",
       "      <td>23.815875</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RandomForestMSE_BAG_L1</td>\n",
       "      <td>-117.179720</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>0.668591</td>\n",
       "      <td>7.966447</td>\n",
       "      <td>0.668591</td>\n",
       "      <td>7.966447</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>WeightedEnsemble_L2</td>\n",
       "      <td>-117.179720</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>0.669350</td>\n",
       "      <td>7.999247</td>\n",
       "      <td>0.000758</td>\n",
       "      <td>0.032800</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RandomForestMSE_BAG_L2</td>\n",
       "      <td>-119.373345</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>13.392768</td>\n",
       "      <td>312.547758</td>\n",
       "      <td>0.352424</td>\n",
       "      <td>22.572776</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ExtraTreesMSE_BAG_L1</td>\n",
       "      <td>-124.600676</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>0.613296</td>\n",
       "      <td>7.605242</td>\n",
       "      <td>0.613296</td>\n",
       "      <td>7.605242</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CatBoost_BAG_L1</td>\n",
       "      <td>-130.679314</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>0.118717</td>\n",
       "      <td>146.053091</td>\n",
       "      <td>0.118717</td>\n",
       "      <td>146.053091</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LightGBM_BAG_L1</td>\n",
       "      <td>-131.054162</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>1.395436</td>\n",
       "      <td>29.128677</td>\n",
       "      <td>1.395436</td>\n",
       "      <td>29.128677</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LightGBMXT_BAG_L1</td>\n",
       "      <td>-131.460909</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>9.874825</td>\n",
       "      <td>55.749429</td>\n",
       "      <td>9.874825</td>\n",
       "      <td>55.749429</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NeuralNetFastAI_BAG_L1</td>\n",
       "      <td>-142.487574</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>0.369480</td>\n",
       "      <td>43.472096</td>\n",
       "      <td>0.369480</td>\n",
       "      <td>43.472096</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     model   score_val              eval_metric  \\\n",
       "0      WeightedEnsemble_L3 -115.742943  root_mean_squared_error   \n",
       "1          CatBoost_BAG_L2 -116.030347  root_mean_squared_error   \n",
       "2        LightGBMXT_BAG_L2 -116.237175  root_mean_squared_error   \n",
       "3          LightGBM_BAG_L2 -116.495144  root_mean_squared_error   \n",
       "4   RandomForestMSE_BAG_L1 -117.179720  root_mean_squared_error   \n",
       "5      WeightedEnsemble_L2 -117.179720  root_mean_squared_error   \n",
       "6   RandomForestMSE_BAG_L2 -119.373345  root_mean_squared_error   \n",
       "7     ExtraTreesMSE_BAG_L1 -124.600676  root_mean_squared_error   \n",
       "8          CatBoost_BAG_L1 -130.679314  root_mean_squared_error   \n",
       "9          LightGBM_BAG_L1 -131.054162  root_mean_squared_error   \n",
       "10       LightGBMXT_BAG_L1 -131.460909  root_mean_squared_error   \n",
       "11  NeuralNetFastAI_BAG_L1 -142.487574  root_mean_squared_error   \n",
       "\n",
       "    pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  \\\n",
       "0       13.395191  372.881253                0.000604           0.034408   \n",
       "1       13.088166  324.512115                0.047821          34.537133   \n",
       "2       13.263769  314.493837                0.223424          24.518855   \n",
       "3       13.123342  313.790857                0.082998          23.815875   \n",
       "4        0.668591    7.966447                0.668591           7.966447   \n",
       "5        0.669350    7.999247                0.000758           0.032800   \n",
       "6       13.392768  312.547758                0.352424          22.572776   \n",
       "7        0.613296    7.605242                0.613296           7.605242   \n",
       "8        0.118717  146.053091                0.118717         146.053091   \n",
       "9        1.395436   29.128677                1.395436          29.128677   \n",
       "10       9.874825   55.749429                9.874825          55.749429   \n",
       "11       0.369480   43.472096                0.369480          43.472096   \n",
       "\n",
       "    stack_level  can_infer  fit_order  \n",
       "0             3       True         12  \n",
       "1             2       True         11  \n",
       "2             2       True          8  \n",
       "3             2       True          9  \n",
       "4             1       True          3  \n",
       "5             2       True          7  \n",
       "6             2       True         10  \n",
       "7             1       True          5  \n",
       "8             1       True          4  \n",
       "9             1       True          2  \n",
       "10            1       True          1  \n",
       "11            1       True          6  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leaderboard dataframe\n",
    "leaderboard_df = pd.DataFrame(predictor.leaderboard(silent=True))\n",
    "leaderboard_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:52:37.799724Z",
     "iopub.status.busy": "2025-12-07T15:52:37.799430Z",
     "iopub.status.idle": "2025-12-07T15:52:38.592489Z",
     "shell.execute_reply": "2025-12-07T15:52:38.591642Z",
     "shell.execute_reply.started": "2025-12-07T15:52:37.799702Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming 'leaderboard_df' is your DataFrame\n",
    "models = leaderboard_df['model']\n",
    "scores = leaderboard_df['score_val']\n",
    "\n",
    "# Create a figure and an axis\n",
    "fig, ax = plt.subplots(figsize=(14, 7))\n",
    "\n",
    "# Plot the bars using absolute scores and 'lightsalmon' color\n",
    "# We use abs() to ensure the bars plot upwards from 0\n",
    "ax.bar(\n",
    "    models, \n",
    "    scores.abs(),  \n",
    "    color='lightsalmon'\n",
    ")\n",
    "\n",
    "# Set X-axis labels and rotate them for readability\n",
    "ax.set_xlabel(\"Model\")\n",
    "ax.set_ylabel(\"Absolute RMSE Scores\")\n",
    "ax.set_title(\"Model Performance Comparison (Absolute RMSE)\")\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "ax.legend(['score_val']) \n",
    "\n",
    "plt.tight_layout() # Adjust layout to prevent labels from overlapping\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create predictions from test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:53:19.612000Z",
     "iopub.status.busy": "2025-12-07T15:53:19.611224Z",
     "iopub.status.idle": "2025-12-07T15:53:55.025475Z",
     "shell.execute_reply": "2025-12-07T15:53:55.024684Z",
     "shell.execute_reply.started": "2025-12-07T15:53:19.611971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    81.708847\n",
       "1    81.196259\n",
       "2    81.196259\n",
       "3    95.865479\n",
       "4    95.865479\n",
       "Name: count, dtype: float32"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = predictor.predict(test)\n",
    "predictions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NOTE: Kaggle will reject the submission if we don't set everything to be > 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:54:14.073765Z",
     "iopub.status.busy": "2025-12-07T15:54:14.073479Z",
     "iopub.status.idle": "2025-12-07T15:54:14.086843Z",
     "shell.execute_reply": "2025-12-07T15:54:14.086123Z",
     "shell.execute_reply.started": "2025-12-07T15:54:14.073745Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6493.000000\n",
       "mean      218.833374\n",
       "std       126.564674\n",
       "min        15.593138\n",
       "25%       120.412399\n",
       "50%       196.542572\n",
       "75%       302.706543\n",
       "max       629.248047\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the `predictions` series to see if there are any negative values\n",
    "predictions.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T15:57:14.834935Z",
     "iopub.status.busy": "2025-12-07T15:57:14.834627Z",
     "iopub.status.idle": "2025-12-07T15:57:14.840463Z",
     "shell.execute_reply": "2025-12-07T15:57:14.839380Z",
     "shell.execute_reply.started": "2025-12-07T15:57:14.834912Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total predictions                : 6493\n",
      "Total positive prediction values : 6493\n",
      "Total negative prediction values : 0\n"
     ]
    }
   ],
   "source": [
    "# How many negative values do we have?\n",
    "# Since the minimum value is 15.593138 we can safely assume that there are not negative values \n",
    "# but still if we have to verify\n",
    "total_predictions_count = len(predictions)\n",
    "pred_Ncount = (predictions < 0).sum()\n",
    "pred_Pcount = (predictions >= 0).sum()\n",
    "\n",
    "print(\"Total predictions                :\", total_predictions_count)\n",
    "print(\"Total positive prediction values :\", pred_Pcount)\n",
    "print(\"Total negative prediction values :\", pred_Ncount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:00:13.679842Z",
     "iopub.status.busy": "2025-12-07T16:00:13.679554Z",
     "iopub.status.idle": "2025-12-07T16:00:13.685471Z",
     "shell.execute_reply": "2025-12-07T16:00:13.684861Z",
     "shell.execute_reply.started": "2025-12-07T16:00:13.679819Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of remaining negative predictions: 0\n",
      "All negative values have been successfully clipped to zero.\n"
     ]
    }
   ],
   "source": [
    "# Set them to zero\n",
    "# guardrails incase there are negative values\n",
    "predictions[predictions < 0] = 0\n",
    "\n",
    "# Check for any remaining negative values\n",
    "predictions_neg_count = (predictions < 0).sum()\n",
    "\n",
    "print(f\"Number of remaining negative predictions: {predictions_neg_count}\")\n",
    "print(\"All negative values have been successfully clipped to zero.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set predictions to submission dataframe, save, and submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:00:35.346643Z",
     "iopub.status.busy": "2025-12-07T16:00:35.346362Z",
     "iopub.status.idle": "2025-12-07T16:00:35.373738Z",
     "shell.execute_reply": "2025-12-07T16:00:35.372913Z",
     "shell.execute_reply.started": "2025-12-07T16:00:35.346622Z"
    }
   },
   "outputs": [],
   "source": [
    "submission[\"count\"] = predictions\n",
    "submission.to_csv(\"submission.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:00:55.994847Z",
     "iopub.status.busy": "2025-12-07T16:00:55.994518Z",
     "iopub.status.idle": "2025-12-07T16:00:58.325588Z",
     "shell.execute_reply": "2025-12-07T16:00:58.324582Z",
     "shell.execute_reply.started": "2025-12-07T16:00:55.994781Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 188k/188k [00:00<00:00, 620kB/s]\n",
      "Successfully submitted to Bike Sharing Demand"
     ]
    }
   ],
   "source": [
    "!kaggle competitions submit -c bike-sharing-demand -f submission.csv -m \"first raw submission\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### View submission via the command line or in the web browser under the competition's page - `My Submissions`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:02:43.737039Z",
     "iopub.status.busy": "2025-12-07T16:02:43.736744Z",
     "iopub.status.idle": "2025-12-07T16:02:46.083814Z",
     "shell.execute_reply": "2025-12-07T16:02:46.082903Z",
     "shell.execute_reply.started": "2025-12-07T16:02:43.737015Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: kaggle in /opt/conda/lib/python3.12/site-packages (1.8.2)\n",
      "Requirement already satisfied: black>=24.10.0 in /opt/conda/lib/python3.12/site-packages (from kaggle) (25.1.0)\n",
      "Requirement already satisfied: bleach in /opt/conda/lib/python3.12/site-packages (from kaggle) (6.2.0)\n",
      "Requirement already satisfied: kagglesdk in /opt/conda/lib/python3.12/site-packages (from kaggle) (0.1.13)\n",
      "Requirement already satisfied: mypy>=1.15.0 in /opt/conda/lib/python3.12/site-packages (from kaggle) (1.19.0)\n",
      "Requirement already satisfied: protobuf in /opt/conda/lib/python3.12/site-packages (from kaggle) (5.28.3)\n",
      "Requirement already satisfied: python-dateutil in /opt/conda/lib/python3.12/site-packages (from kaggle) (2.9.0.post0)\n",
      "Requirement already satisfied: python-slugify in /opt/conda/lib/python3.12/site-packages (from kaggle) (8.0.4)\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.12/site-packages (from kaggle) (2.32.4)\n",
      "Requirement already satisfied: setuptools>=21.0.0 in /opt/conda/lib/python3.12/site-packages (from kaggle) (80.9.0)\n",
      "Requirement already satisfied: six>=1.10 in /opt/conda/lib/python3.12/site-packages (from kaggle) (1.17.0)\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.12/site-packages (from kaggle) (4.67.1)\n",
      "Requirement already satisfied: types-requests in /opt/conda/lib/python3.12/site-packages (from kaggle) (2.32.4.20250913)\n",
      "Requirement already satisfied: types-tqdm in /opt/conda/lib/python3.12/site-packages (from kaggle) (4.67.0.20250809)\n",
      "Requirement already satisfied: urllib3>=1.15.1 in /opt/conda/lib/python3.12/site-packages (from kaggle) (2.6.0)\n",
      "Requirement already satisfied: click>=8.0.0 in /opt/conda/lib/python3.12/site-packages (from black>=24.10.0->kaggle) (8.2.1)\n",
      "Requirement already satisfied: mypy-extensions>=0.4.3 in /opt/conda/lib/python3.12/site-packages (from black>=24.10.0->kaggle) (1.1.0)\n",
      "Requirement already satisfied: packaging>=22.0 in /opt/conda/lib/python3.12/site-packages (from black>=24.10.0->kaggle) (24.2)\n",
      "Requirement already satisfied: pathspec>=0.9.0 in /opt/conda/lib/python3.12/site-packages (from black>=24.10.0->kaggle) (0.12.1)\n",
      "Requirement already satisfied: platformdirs>=2 in /opt/conda/lib/python3.12/site-packages (from black>=24.10.0->kaggle) (4.3.8)\n",
      "Requirement already satisfied: typing_extensions>=4.6.0 in /opt/conda/lib/python3.12/site-packages (from mypy>=1.15.0->kaggle) (4.14.1)\n",
      "Requirement already satisfied: librt>=0.6.2 in /opt/conda/lib/python3.12/site-packages (from mypy>=1.15.0->kaggle) (0.7.3)\n",
      "Requirement already satisfied: webencodings in /opt/conda/lib/python3.12/site-packages (from bleach->kaggle) (0.5.1)\n",
      "Requirement already satisfied: text-unidecode>=1.3 in /opt/conda/lib/python3.12/site-packages (from python-slugify->kaggle) (1.3)\n",
      "Requirement already satisfied: charset_normalizer<4,>=2 in /opt/conda/lib/python3.12/site-packages (from requests->kaggle) (3.4.3)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.12/site-packages (from requests->kaggle) (3.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.12/site-packages (from requests->kaggle) (2025.8.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:04:55.812655Z",
     "iopub.status.busy": "2025-12-07T16:04:55.812359Z",
     "iopub.status.idle": "2025-12-07T16:04:56.419129Z",
     "shell.execute_reply": "2025-12-07T16:04:56.418305Z",
     "shell.execute_reply.started": "2025-12-07T16:04:55.812632Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/bin/kaggle\", line 7, in <module>\n",
      "    sys.exit(main())\n",
      "             ^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/kaggle/cli.py\", line 70, in main\n",
      "    out = args.func(**command_args)\n",
      "          ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/kaggle/api/kaggle_api_extended.py\", line 1467, in competition_submissions_cli\n",
      "    submissions = self.competition_submissions(\n",
      "                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "TypeError: KaggleApi.competition_submissions() got an unexpected keyword argument 'page_number'\n"
     ]
    }
   ],
   "source": [
    "\n",
    "!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:08:12.700213Z",
     "iopub.status.busy": "2025-12-07T16:08:12.699769Z",
     "iopub.status.idle": "2025-12-07T16:08:13.666463Z",
     "shell.execute_reply": "2025-12-07T16:08:13.665542Z",
     "shell.execute_reply.started": "2025-12-07T16:08:12.700174Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kaggle API 1.8.2\n"
     ]
    }
   ],
   "source": [
    "!kaggle --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:16:25.041512Z",
     "iopub.status.busy": "2025-12-07T16:16:25.041223Z",
     "iopub.status.idle": "2025-12-07T16:16:25.133446Z",
     "shell.execute_reply": "2025-12-07T16:16:25.132642Z",
     "shell.execute_reply.started": "2025-12-07T16:16:25.041491Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"_date\": \"2025-12-07 16:00:57.423000\",\n",
      "  \"_description\": \"first raw submission\",\n",
      "  \"_error_description\": null,\n",
      "  \"_file_name\": \"submission.csv\",\n",
      "  \"_is_frozen\": true,\n",
      "  \"_private_score\": \"1.42139\",\n",
      "  \"_public_score\": \"1.42139\",\n",
      "  \"_ref\": 48843618,\n",
      "  \"_status\": \"SubmissionStatus.COMPLETE\",\n",
      "  \"_submitted_by\": \"brejeshbalakrishnan\",\n",
      "  \"_submitted_by_ref\": \"brejeshbalakrishnan\",\n",
      "  \"_team_name\": \"Brejesh Balakrishnan\",\n",
      "  \"_total_bytes\": 192955,\n",
      "  \"_url\": \"/submissions/48843618/48843618.raw\"\n",
      "}\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from kaggle.api.kaggle_api_extended import KaggleApi\n",
    "\n",
    "api = KaggleApi()\n",
    "api.authenticate()\n",
    "subs = api.competition_submissions('bike-sharing-demand')  # no page_number\n",
    "import json\n",
    "\n",
    "for s in subs[:3]:\n",
    "    print(json.dumps(vars(s), indent=2, sort_keys=True, default=str))\n",
    "    print(\"-\" * 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initial score of `1.42139`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Exploratory Data Analysis and Creating an additional feature\n",
    "* Any additional feature will do, but a great suggestion would be to separate out the datetime into hour, day, or month parts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:18:57.819288Z",
     "iopub.status.busy": "2025-12-07T16:18:57.818993Z",
     "iopub.status.idle": "2025-12-07T16:18:59.437125Z",
     "shell.execute_reply": "2025-12-07T16:18:59.436283Z",
     "shell.execute_reply.started": "2025-12-07T16:18:57.819264Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'datetime'}>,\n",
       "        <Axes: title={'center': 'season'}>,\n",
       "        <Axes: title={'center': 'holiday'}>],\n",
       "       [<Axes: title={'center': 'workingday'}>,\n",
       "        <Axes: title={'center': 'weather'}>,\n",
       "        <Axes: title={'center': 'temp'}>],\n",
       "       [<Axes: title={'center': 'atemp'}>,\n",
       "        <Axes: title={'center': 'humidity'}>,\n",
       "        <Axes: title={'center': 'windspeed'}>],\n",
       "       [<Axes: title={'center': 'casual'}>,\n",
       "        <Axes: title={'center': 'registered'}>,\n",
       "        <Axes: title={'center': 'count'}>]], dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x800 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram of all features to show the distribution of each one relative to the data. This is part of the exploritory data analysis\n",
    "train.hist(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Heatmap using Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:23:16.404037Z",
     "iopub.status.busy": "2025-12-07T16:23:16.403696Z",
     "iopub.status.idle": "2025-12-07T16:23:17.570599Z",
     "shell.execute_reply": "2025-12-07T16:23:17.569225Z",
     "shell.execute_reply.started": "2025-12-07T16:23:16.404014Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x1800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "plt.figure(figsize=(15, 15), dpi=120)\n",
    "corr_data = train.copy()\n",
    "correlation_matrix = corr_data.corr()\n",
    "figure_plot, axes_heatmap = plt.subplots(figsize=(15, 15), dpi=120)\n",
    "upper_triangle_mask = np.array(correlation_matrix)\n",
    "upper_triangle_mask[np.tril_indices_from(upper_triangle_mask)] = False\n",
    "axes_heatmap = sns.heatmap(correlation_matrix, cmap='GnBu', cbar_kws={\"shrink\": .5}, vmin=-1, vmax=1, center=0,\n",
    "            square=True, mask=upper_triangle_mask, annot=True, linewidths=0.01, annot_kws={\"size\":13})\n",
    "plt.xticks(fontsize=14, rotation=90)\n",
    "plt.yticks(fontsize=14, rotation=0)\n",
    "plt.title(\"HeatMap: Feature Correlation Matrix\", fontsize=20, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "plt.autoscale()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:20:20.798901Z",
     "iopub.status.busy": "2025-12-07T16:20:20.798607Z",
     "iopub.status.idle": "2025-12-07T16:20:20.810889Z",
     "shell.execute_reply": "2025-12-07T16:20:20.809788Z",
     "shell.execute_reply.started": "2025-12-07T16:20:20.798880Z"
    }
   },
   "outputs": [],
   "source": [
    "# create a new feature\n",
    "train = pd.concat([train,pd.DataFrame({\n",
    "                     'year' : train.datetime.dt.year, \n",
    "                     'month': train.datetime.dt.month,\n",
    "                     'day': train.datetime.dt.day,\n",
    "                     'hour': train.datetime.dt.hour})],axis=1)\n",
    "test = pd.concat([test,pd.DataFrame({\n",
    "                     'year' : test.datetime.dt.year, \n",
    "                     'month': test.datetime.dt.month,\n",
    "                     'day': test.datetime.dt.day,\n",
    "                     'hour': test.datetime.dt.hour})],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make category types for these so models know they are not just numbers\n",
    "* AutoGluon originally sees these as ints, but in reality they are int representations of a category.\n",
    "* Setting the dtype to category will classify these as categories in AutoGluon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:24:11.607133Z",
     "iopub.status.busy": "2025-12-07T16:24:11.606838Z",
     "iopub.status.idle": "2025-12-07T16:24:11.625510Z",
     "shell.execute_reply": "2025-12-07T16:24:11.624556Z",
     "shell.execute_reply.started": "2025-12-07T16:24:11.607110Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10886 entries, 0 to 10885\n",
      "Data columns (total 20 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   datetime    10886 non-null  datetime64[ns]\n",
      " 1   season      10886 non-null  category      \n",
      " 2   holiday     10886 non-null  int64         \n",
      " 3   workingday  10886 non-null  int64         \n",
      " 4   weather     10886 non-null  category      \n",
      " 5   temp        10886 non-null  float64       \n",
      " 6   atemp       10886 non-null  float64       \n",
      " 7   humidity    10886 non-null  int64         \n",
      " 8   windspeed   10886 non-null  float64       \n",
      " 9   casual      10886 non-null  int64         \n",
      " 10  registered  10886 non-null  int64         \n",
      " 11  count       10886 non-null  int64         \n",
      " 12  year        10886 non-null  int32         \n",
      " 13  month       10886 non-null  int32         \n",
      " 14  day         10886 non-null  int32         \n",
      " 15  hour        10886 non-null  int32         \n",
      " 16  year        10886 non-null  int32         \n",
      " 17  month       10886 non-null  int32         \n",
      " 18  day         10886 non-null  int32         \n",
      " 19  hour        10886 non-null  int32         \n",
      "dtypes: category(2), datetime64[ns](1), float64(3), int32(8), int64(6)\n",
      "memory usage: 1.2 MB\n"
     ]
    }
   ],
   "source": [
    "# Recheck datatype of features in the train dataset\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:20:35.069020Z",
     "iopub.status.busy": "2025-12-07T16:20:35.061410Z",
     "iopub.status.idle": "2025-12-07T16:20:35.090354Z",
     "shell.execute_reply": "2025-12-07T16:20:35.089402Z",
     "shell.execute_reply.started": "2025-12-07T16:20:35.068977Z"
    }
   },
   "outputs": [],
   "source": [
    "train[\"season\"] = train.season.astype('category')\n",
    "train[\"weather\"] = train.weather.astype('category')\n",
    "test[\"season\"] = test.season.astype('category')\n",
    "test[\"weather\"] = test.weather.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:20:36.784142Z",
     "iopub.status.busy": "2025-12-07T16:20:36.783802Z",
     "iopub.status.idle": "2025-12-07T16:20:36.801387Z",
     "shell.execute_reply": "2025-12-07T16:20:36.800477Z",
     "shell.execute_reply.started": "2025-12-07T16:20:36.784119Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime season  holiday  workingday weather  temp   atemp  \\\n",
       "0 2011-01-01 00:00:00      1        0           0       1  9.84  14.395   \n",
       "1 2011-01-01 01:00:00      1        0           0       1  9.02  13.635   \n",
       "2 2011-01-01 02:00:00      1        0           0       1  9.02  13.635   \n",
       "3 2011-01-01 03:00:00      1        0           0       1  9.84  14.395   \n",
       "4 2011-01-01 04:00:00      1        0           0       1  9.84  14.395   \n",
       "\n",
       "   humidity  windspeed  casual  registered  count  year  month  day  hour  \\\n",
       "0        81        0.0       3          13     16  2011      1    1     0   \n",
       "1        80        0.0       8          32     40  2011      1    1     1   \n",
       "2        80        0.0       5          27     32  2011      1    1     2   \n",
       "3        75        0.0       3          10     13  2011      1    1     3   \n",
       "4        75        0.0       0           1      1  2011      1    1     4   \n",
       "\n",
       "   year  month  day  hour  \n",
       "0  2011      1    1     0  \n",
       "1  2011      1    1     1  \n",
       "2  2011      1    1     2  \n",
       "3  2011      1    1     3  \n",
       "4  2011      1    1     4  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View are new feature\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:20:48.803052Z",
     "iopub.status.busy": "2025-12-07T16:20:48.802775Z",
     "iopub.status.idle": "2025-12-07T16:20:48.818839Z",
     "shell.execute_reply": "2025-12-07T16:20:48.818126Z",
     "shell.execute_reply.started": "2025-12-07T16:20:48.803031Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime season  holiday  workingday weather  temp   atemp  \\\n",
       "0 2011-01-01 00:00:00      1        0           0       1  9.84  14.395   \n",
       "1 2011-01-01 01:00:00      1        0           0       1  9.02  13.635   \n",
       "2 2011-01-01 02:00:00      1        0           0       1  9.02  13.635   \n",
       "3 2011-01-01 03:00:00      1        0           0       1  9.84  14.395   \n",
       "4 2011-01-01 04:00:00      1        0           0       1  9.84  14.395   \n",
       "\n",
       "   humidity  windspeed  casual  registered  count  year  month  day  hour  \\\n",
       "0        81        0.0       3          13     16  2011      1    1     0   \n",
       "1        80        0.0       8          32     40  2011      1    1     1   \n",
       "2        80        0.0       5          27     32  2011      1    1     2   \n",
       "3        75        0.0       3          10     13  2011      1    1     3   \n",
       "4        75        0.0       0           1      1  2011      1    1     4   \n",
       "\n",
       "   year  month  day  hour  \n",
       "0  2011      1    1     0  \n",
       "1  2011      1    1     1  \n",
       "2  2011      1    1     2  \n",
       "3  2011      1    1     3  \n",
       "4  2011      1    1     4  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View histogram of all features again now with the hour feature\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:24:47.850314Z",
     "iopub.status.busy": "2025-12-07T16:24:47.850021Z",
     "iopub.status.idle": "2025-12-07T16:24:50.735598Z",
     "shell.execute_reply": "2025-12-07T16:24:50.733383Z",
     "shell.execute_reply.started": "2025-12-07T16:24:47.850290Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x2000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View histogram of all features again now with the hour feature\n",
    "train.hist(figsize=(15,20))  # Note: 'casual' and 'registered' are ignored during training as they are absent in test data\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Rerun the model with the same settings as before, just with more features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:33:23.387280Z",
     "iopub.status.busy": "2025-12-07T16:33:23.386998Z",
     "iopub.status.idle": "2025-12-07T16:43:47.227603Z",
     "shell.execute_reply": "2025-12-07T16:43:47.226748Z",
     "shell.execute_reply.started": "2025-12-07T16:33:23.387258Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No path specified. Models will be saved in: \"AutogluonModels/ag-20251207_163323\"\n",
      "Verbosity: 2 (Standard Logging)\n",
      "=================== System Info ===================\n",
      "AutoGluon Version:  1.4.0\n",
      "Python Version:     3.12.9\n",
      "Operating System:   Linux\n",
      "Platform Machine:   x86_64\n",
      "Platform Version:   #1 SMP PREEMPT_DYNAMIC Mon Nov  3 18:38:36 UTC 2025\n",
      "CPU Count:          2\n",
      "Memory Avail:       0.44 GB / 3.74 GB (11.8%)\n",
      "Disk Space Avail:   4.16 GB / 4.99 GB (83.4%)\n",
      "\tWARNING: Available disk space is low and there is a risk that AutoGluon will run out of disk during fit, causing an exception. \n",
      "\tWe recommend a minimum available disk space of 10 GB, and large datasets may require more.\n",
      "===================================================\n",
      "Presets specified: ['best_quality']\n",
      "Using hyperparameters preset: hyperparameters='zeroshot'\n",
      "Stack configuration (auto_stack=True): num_stack_levels=0, num_bag_folds=8, num_bag_sets=1\n",
      "Beginning AutoGluon training ... Time limit = 600s\n",
      "AutoGluon will save models to \"/home/sagemaker-user/udacity-AWS-ml-engineer-nanodegree/project/AutogluonModels/ag-20251207_163323\"\n",
      "Train Data Rows:    10886\n",
      "Train Data Columns: 11\n",
      "Label Column:       count\n",
      "Problem Type:       regression\n",
      "Preprocessing data ...\n",
      "Using Feature Generators to preprocess the data ...\n",
      "Dropping user-specified ignored columns: ['casual', 'registered']\n",
      "Fitting AutoMLPipelineFeatureGenerator...\n",
      "\tAvailable Memory:                    446.85 MB\n",
      "\tTrain Data (Original)  Memory Usage: 0.75 MB (0.2% of available memory)\n",
      "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
      "\tStage 1 Generators:\n",
      "\t\tFitting AsTypeFeatureGenerator...\n",
      "\t\t\tNote: Converting 2 features to boolean dtype as they only contain 2 unique values.\n",
      "\tStage 2 Generators:\n",
      "\t\tFitting FillNaFeatureGenerator...\n",
      "\tStage 3 Generators:\n",
      "\t\tFitting IdentityFeatureGenerator...\n",
      "\t\tFitting DatetimeFeatureGenerator...\n",
      "\tStage 4 Generators:\n",
      "\t\tFitting DropUniqueFeatureGenerator...\n",
      "\tStage 5 Generators:\n",
      "\t\tFitting DropDuplicatesFeatureGenerator...\n",
      "\tTypes of features in original data (raw dtype, special dtypes):\n",
      "\t\t('datetime', []) : 1 | ['datetime']\n",
      "\t\t('float', [])    : 3 | ['temp', 'atemp', 'windspeed']\n",
      "\t\t('int', [])      : 5 | ['season', 'holiday', 'workingday', 'weather', 'humidity']\n",
      "\tTypes of features in processed data (raw dtype, special dtypes):\n",
      "\t\t('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
      "\t\t('int', [])                  : 3 | ['season', 'weather', 'humidity']\n",
      "\t\t('int', ['bool'])            : 2 | ['holiday', 'workingday']\n",
      "\t\t('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n",
      "\t0.1s = Fit runtime\n",
      "\t9 features in original data used to generate 13 features in processed data.\n",
      "\tTrain Data (Processed) Memory Usage: 0.93 MB (0.2% of available memory)\n",
      "Data preprocessing and feature engineering runtime = 0.16s ...\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n",
      "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n",
      "\tTo change this, specify the eval_metric parameter of Predictor()\n",
      "Large model count detected (110 configs) ... Only displaying the first 3 models of each family. To see all, set `verbosity=3`.\n",
      "User-specified model hyperparameters to be fit:\n",
      "{\n",
      "\t'NN_TORCH': [{}, {'activation': 'elu', 'dropout_prob': 0.10077639529843717, 'hidden_size': 108, 'learning_rate': 0.002735937344002146, 'num_layers': 4, 'use_batchnorm': True, 'weight_decay': 1.356433327634438e-12, 'ag_args': {'name_suffix': '_r79', 'priority': -2}}, {'activation': 'elu', 'dropout_prob': 0.11897478034205347, 'hidden_size': 213, 'learning_rate': 0.0010474382260641949, 'num_layers': 4, 'use_batchnorm': False, 'weight_decay': 5.594471067786272e-10, 'ag_args': {'name_suffix': '_r22', 'priority': -7}}],\n",
      "\t'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, {'learning_rate': 0.03, 'num_leaves': 128, 'feature_fraction': 0.9, 'min_data_in_leaf': 3, 'ag_args': {'name_suffix': 'Large', 'priority': 0, 'hyperparameter_tune_kwargs': None}}],\n",
      "\t'CAT': [{}, {'depth': 6, 'grow_policy': 'SymmetricTree', 'l2_leaf_reg': 2.1542798306067823, 'learning_rate': 0.06864209415792857, 'max_ctr_complexity': 4, 'one_hot_max_size': 10, 'ag_args': {'name_suffix': '_r177', 'priority': -1}}, {'depth': 8, 'grow_policy': 'Depthwise', 'l2_leaf_reg': 2.7997999596449104, 'learning_rate': 0.031375015734637225, 'max_ctr_complexity': 2, 'one_hot_max_size': 3, 'ag_args': {'name_suffix': '_r9', 'priority': -5}}],\n",
      "\t'XGB': [{}, {'colsample_bytree': 0.6917311125174739, 'enable_categorical': False, 'learning_rate': 0.018063876087523967, 'max_depth': 10, 'min_child_weight': 0.6028633586934382, 'ag_args': {'name_suffix': '_r33', 'priority': -8}}, {'colsample_bytree': 0.6628423832084077, 'enable_categorical': False, 'learning_rate': 0.08775715546881824, 'max_depth': 5, 'min_child_weight': 0.6294123374222513, 'ag_args': {'name_suffix': '_r89', 'priority': -16}}],\n",
      "\t'FASTAI': [{}, {'bs': 256, 'emb_drop': 0.5411770367537934, 'epochs': 43, 'layers': [800, 400], 'lr': 0.01519848858318159, 'ps': 0.23782946566604385, 'ag_args': {'name_suffix': '_r191', 'priority': -4}}, {'bs': 2048, 'emb_drop': 0.05070411322605811, 'epochs': 29, 'layers': [200, 100], 'lr': 0.08974235041576624, 'ps': 0.10393466140748028, 'ag_args': {'name_suffix': '_r102', 'priority': -11}}],\n",
      "\t'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
      "\t'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n",
      "}\n",
      "Fitting 106 L1 models, fit_strategy=\"sequential\" ...\n",
      "Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 599.84s of the 599.83s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=2.52%)\n",
      "I0000 00:00:1765125266.372975    1992 chttp2_transport.cc:1182] ipv4:169.255.255.2:40363: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {grpc_status:14, http2_error:2, created_time:\"2025-12-07T16:34:26.371878096+00:00\"}\n",
      "\t-178.7518\t = Validation score   (-root_mean_squared_error)\n",
      "\t56.97s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: LightGBM_BAG_L1 ... Training model for up to 538.25s of the 538.24s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=3.61%)\n",
      "I0000 00:00:1765125314.260280    2095 chttp2_transport.cc:1182] ipv4:169.255.255.2:37539: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {created_time:\"2025-12-07T16:35:14.259298412+00:00\", http2_error:2, grpc_status:14}\n",
      "I0000 00:00:1765125325.227956    2091 chttp2_transport.cc:1182] ipv4:169.255.255.2:46639: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {created_time:\"2025-12-07T16:35:25.227952727+00:00\", http2_error:2, grpc_status:14}\n",
      "\t-177.9449\t = Validation score   (-root_mean_squared_error)\n",
      "\t53.15s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 479.39s of the 479.39s of remaining time.\n",
      "\tWarning: Reducing model 'n_estimators' from 300 -> 47 due to low memory. Expected memory usage reduced from 94.13% -> 15.0% of available memory...\n",
      "\t-120.1695\t = Validation score   (-root_mean_squared_error)\n",
      "\t2.44s\t = Training   runtime\n",
      "\t0.21s\t = Validation runtime\n",
      "Fitting model: CatBoost_BAG_L1 ... Training model for up to 476.61s of the 476.60s of remaining time.\n",
      "\tMemory not enough to fit 8 folds in parallel. Will train 1 folds in parallel instead (Estimated 135.18% memory usage per fold, 135.18%/80.00% total).\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (1 workers, per: cpus=1, gpus=0, memory=135.18%)\n",
      "\t\tSwitching to pseudo sequential ParallelFoldFittingStrategy to avoid Python memory leakage.\n",
      "\t\tOverrule this behavior by setting fold_fitting_strategy to 'sequential_local' in ag_args_ensemble when when calling `predictor.fit`\n",
      "I0000 00:00:1765125367.860198    1998 chttp2_transport.cc:1182] ipv4:169.255.255.2:38057: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {grpc_status:14, http2_error:2, created_time:\"2025-12-07T16:36:07.859181097+00:00\"}\n",
      "\t-130.4723\t = Validation score   (-root_mean_squared_error)\n",
      "\t313.35s\t = Training   runtime\n",
      "\t0.07s\t = Validation runtime\n",
      "Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 160.48s of the 160.47s of remaining time.\n",
      "\tWarning: Reducing model 'n_estimators' from 300 -> 51 due to low memory. Expected memory usage reduced from 87.48% -> 15.0% of available memory...\n",
      "\t-128.1254\t = Validation score   (-root_mean_squared_error)\n",
      "\t1.04s\t = Training   runtime\n",
      "\t0.13s\t = Validation runtime\n",
      "Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 159.15s of the 159.14s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=3.71%)\n",
      "\tWarning: Exception caused NeuralNetFastAI_BAG_L1 to fail during training... Skipping this model.\n",
      "\t\tTask was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 90602f60115bcb5cd2e8f7ab5746407234db2d2c01000000, name=_ray_fit, pid=10831, memory used=0.33GB) was running was 3.55GB / 3.74GB (0.950351), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 9f9b01aab631de0597732a16ec6d48f388706b3aa9263540d3df6a20) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-9f9b01aab631de0597732a16ec6d48f388706b3aa9263540d3df6a20*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.18\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.36\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "10831\t0.33\tray::_ray_fit\n",
      "624\t0.29\t/opt/conda/bin/python3.12 -m pylsp\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "6735\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "7128\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "10996\t0.05\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/_private/workers/default_worker.py...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Detailed Traceback:\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/tabular/trainer/abstract_trainer.py\", line 2171, in _train_and_save\n",
      "    model = self._train_single(**model_fit_kwargs)\n",
      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/tabular/trainer/abstract_trainer.py\", line 2055, in _train_single\n",
      "    model = model.fit(X=X, y=y, X_val=X_val, y_val=y_val, X_test=X_test, y_test=y_test, total_resources=total_resources, **model_fit_kwargs)\n",
      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 90602f60115bcb5cd2e8f7ab5746407234db2d2c01000000, name=_ray_fit, pid=10831, memory used=0.33GB) was running was 3.55GB / 3.74GB (0.950351), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 9f9b01aab631de0597732a16ec6d48f388706b3aa9263540d3df6a20) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-9f9b01aab631de0597732a16ec6d48f388706b3aa9263540d3df6a20*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.18\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.36\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "10831\t0.33\tray::_ray_fit\n",
      "624\t0.29\t/opt/conda/bin/python3.12 -m pylsp\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "6735\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "7128\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "10996\t0.05\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/_private/workers/default_worker.py...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Fitting model: XGBoost_BAG_L1 ... Training model for up to 145.70s of the 145.69s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=3.87%)\n",
      "\tWarning: Exception caused XGBoost_BAG_L1 to fail during training... Skipping this model.\n",
      "\t\tTask was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: b805e9eb642e131622ba46682b9fc69be49e4d0a01000000, name=_ray_fit, pid=11063, memory used=0.05GB) was running was 3.58GB / 3.74GB (0.958119), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 6d5faf8dcdfb517f8769ca13566abb4e3435af3dc80a78d66f6648a6) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-6d5faf8dcdfb517f8769ca13566abb4e3435af3dc80a78d66f6648a6*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.18\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.36\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.29\t/opt/conda/bin/python3.12 -m pylsp\n",
      "10996\t0.15\tray::IDLE\n",
      "10997\t0.15\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "6735\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "7128\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "11063\t0.05\tray::IDLE\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Detailed Traceback:\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/tabular/trainer/abstract_trainer.py\", line 2171, in _train_and_save\n",
      "    model = self._train_single(**model_fit_kwargs)\n",
      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/tabular/trainer/abstract_trainer.py\", line 2055, in _train_single\n",
      "    model = model.fit(X=X, y=y, X_val=X_val, y_val=y_val, X_test=X_test, y_test=y_test, total_resources=total_resources, **model_fit_kwargs)\n",
      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: b805e9eb642e131622ba46682b9fc69be49e4d0a01000000, name=_ray_fit, pid=11063, memory used=0.05GB) was running was 3.58GB / 3.74GB (0.958119), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 6d5faf8dcdfb517f8769ca13566abb4e3435af3dc80a78d66f6648a6) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-6d5faf8dcdfb517f8769ca13566abb4e3435af3dc80a78d66f6648a6*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.18\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.36\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.29\t/opt/conda/bin/python3.12 -m pylsp\n",
      "10996\t0.15\tray::IDLE\n",
      "10997\t0.15\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "6735\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "7128\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "11063\t0.05\tray::IDLE\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Fitting model: NeuralNetTorch_BAG_L1 ... Training model for up to 136.06s of the 136.05s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=1.51%)\n",
      "\tWarning: Exception caused NeuralNetTorch_BAG_L1 to fail during training... Skipping this model.\n",
      "\t\tTask was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 20a50b7f94591812eec45a8d4edc0edcc44d8bc001000000, name=_ray_fit, pid=11125, memory used=0.33GB) was running was 3.56GB / 3.74GB (0.952189), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 29145e8d14c9d83a72c9d922acbb18f2f689ba05b03ad750dd903a9a) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-29145e8d14c9d83a72c9d922acbb18f2f689ba05b03ad750dd903a9a*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.18\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.36\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "11125\t0.33\tray::_ray_fit\n",
      "624\t0.29\t/opt/conda/bin/python3.12 -m pylsp\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "6735\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "7128\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "11250\t0.05\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/_private/workers/default_worker.py...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Detailed Traceback:\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/tabular/trainer/abstract_trainer.py\", line 2171, in _train_and_save\n",
      "    model = self._train_single(**model_fit_kwargs)\n",
      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/tabular/trainer/abstract_trainer.py\", line 2055, in _train_single\n",
      "    model = model.fit(X=X, y=y, X_val=X_val, y_val=y_val, X_test=X_test, y_test=y_test, total_resources=total_resources, **model_fit_kwargs)\n",
      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 20a50b7f94591812eec45a8d4edc0edcc44d8bc001000000, name=_ray_fit, pid=11125, memory used=0.33GB) was running was 3.56GB / 3.74GB (0.952189), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 29145e8d14c9d83a72c9d922acbb18f2f689ba05b03ad750dd903a9a) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-29145e8d14c9d83a72c9d922acbb18f2f689ba05b03ad750dd903a9a*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.18\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.36\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "11125\t0.33\tray::_ray_fit\n",
      "624\t0.29\t/opt/conda/bin/python3.12 -m pylsp\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "6735\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "7128\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "11250\t0.05\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/_private/workers/default_worker.py...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 124.38s of the 124.37s of remaining time.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=6.98%)\n",
      "I0000 00:00:1765125735.132356    2092 chttp2_transport.cc:1182] ipv4:169.255.255.2:33129: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {created_time:\"2025-12-07T16:42:15.13029387+00:00\", http2_error:2, grpc_status:14}\n",
      "\t-178.9407\t = Validation score   (-root_mean_squared_error)\n",
      "\t53.88s\t = Training   runtime\n",
      "\t0.01s\t = Validation runtime\n",
      "Fitting model: CatBoost_r177_BAG_L1 ... Training model for up to 65.99s of the 65.98s of remaining time.\n",
      "I0000 00:00:1765125738.658521    2092 chttp2_transport.cc:1182] ipv4:169.255.255.2:41593: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {grpc_status:14, http2_error:2, created_time:\"2025-12-07T16:42:18.658517843+00:00\"}\n",
      "\tMemory not enough to fit 8 folds in parallel. Will train 1 folds in parallel instead (Estimated 125.34% memory usage per fold, 125.34%/80.00% total).\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (1 workers, per: cpus=1, gpus=0, memory=125.34%)\n",
      "\t\tSwitching to pseudo sequential ParallelFoldFittingStrategy to avoid Python memory leakage.\n",
      "\t\tOverrule this behavior by setting fold_fitting_strategy to 'sequential_local' in ag_args_ensemble when when calling `predictor.fit`\n",
      "I0000 00:00:1765125771.036664    1992 chttp2_transport.cc:1182] ipv4:169.255.255.2:41899: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {created_time:\"2025-12-07T16:42:51.034292632+00:00\", http2_error:2, grpc_status:14}\n",
      "\t-131.1844\t = Validation score   (-root_mean_squared_error)\n",
      "\t83.42s\t = Training   runtime\n",
      "\t0.04s\t = Validation runtime\n",
      "Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.00s of the -21.44s of remaining time.\n",
      "\tEnsemble Weights: {'RandomForestMSE_BAG_L1': 0.88, 'CatBoost_BAG_L1': 0.08, 'LightGBMXT_BAG_L1': 0.04}\n",
      "\t-119.9188\t = Validation score   (-root_mean_squared_error)\n",
      "\t0.05s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "AutoGluon training complete, total runtime = 621.57s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 12534.2 rows/s (1361 batch size)\n",
      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/home/sagemaker-user/udacity-AWS-ml-engineer-nanodegree/project/AutogluonModels/ag-20251207_163323\")\n"
     ]
    }
   ],
   "source": [
    "predictor_new_features = TabularPredictor(label=label, \n",
    "                                          problem_type= 'regression',\n",
    "                                          eval_metric=eval_metric, \n",
    "                                          learner_kwargs={'ignored_columns': ignored_columns}).fit(\n",
    "                                                                                           train_data = train_data,\n",
    "                                                                                           time_limit=time_limit,\n",
    "                                                                                           presets=presets,\n",
    "                                                                                           dynamic_stacking=False,  # Turn off the heavy memory check\n",
    "                                                                                           num_stack_levels=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:43:47.229754Z",
     "iopub.status.busy": "2025-12-07T16:43:47.229480Z",
     "iopub.status.idle": "2025-12-07T16:43:47.334014Z",
     "shell.execute_reply": "2025-12-07T16:43:47.333198Z",
     "shell.execute_reply.started": "2025-12-07T16:43:47.229732Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Summary of fit() ***\n",
      "Estimated performance of each model:\n",
      "                    model   score_val              eval_metric  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0     WeightedEnsemble_L2 -119.918835  root_mean_squared_error       0.293488  372.804507                0.000870           0.045973            2       True          8\n",
      "1  RandomForestMSE_BAG_L1 -120.169502  root_mean_squared_error       0.210456    2.440768                0.210456           2.440768            1       True          3\n",
      "2    ExtraTreesMSE_BAG_L1 -128.125420  root_mean_squared_error       0.129230    1.044748                0.129230           1.044748            1       True          5\n",
      "3         CatBoost_BAG_L1 -130.472284  root_mean_squared_error       0.070377  313.350754                0.070377         313.350754            1       True          4\n",
      "4    CatBoost_r177_BAG_L1 -131.184374  root_mean_squared_error       0.044790   83.423699                0.044790          83.423699            1       True          7\n",
      "5         LightGBM_BAG_L1 -177.944936  root_mean_squared_error       0.010209   53.146978                0.010209          53.146978            1       True          2\n",
      "6       LightGBMXT_BAG_L1 -178.751833  root_mean_squared_error       0.011785   56.967011                0.011785          56.967011            1       True          1\n",
      "7    LightGBMLarge_BAG_L1 -178.940684  root_mean_squared_error       0.013571   53.875580                0.013571          53.875580            1       True          6\n",
      "Number of models trained: 8\n",
      "Types of models trained:\n",
      "{'StackerEnsembleModel_LGB', 'StackerEnsembleModel_CatBoost', 'StackerEnsembleModel_RF', 'WeightedEnsembleModel', 'StackerEnsembleModel_XT'}\n",
      "Bagging used: True  (with 8 folds)\n",
      "Multi-layer stack-ensembling used: False \n",
      "Feature Metadata (Processed):\n",
      "(raw dtype, special dtypes):\n",
      "('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
      "('int', [])                  : 3 | ['season', 'weather', 'humidity']\n",
      "('int', ['bool'])            : 2 | ['holiday', 'workingday']\n",
      "('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']\n",
      "Plot summary of models saved to file: /home/sagemaker-user/udacity-AWS-ml-engineer-nanodegree/project/AutogluonModels/ag-20251207_163323/SummaryOfModels.html\n",
      "*** End of fit() summary ***\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'model_types': {'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',\n",
       "  'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',\n",
       "  'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',\n",
       "  'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',\n",
       "  'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',\n",
       "  'LightGBMLarge_BAG_L1': 'StackerEnsembleModel_LGB',\n",
       "  'CatBoost_r177_BAG_L1': 'StackerEnsembleModel_CatBoost',\n",
       "  'WeightedEnsemble_L2': 'WeightedEnsembleModel'},\n",
       " 'model_performance': {'LightGBMXT_BAG_L1': -178.751832961977,\n",
       "  'LightGBM_BAG_L1': -177.94493629824913,\n",
       "  'RandomForestMSE_BAG_L1': -120.16950217387324,\n",
       "  'CatBoost_BAG_L1': -130.4722836190554,\n",
       "  'ExtraTreesMSE_BAG_L1': -128.12541992122735,\n",
       "  'LightGBMLarge_BAG_L1': -178.94068408417186,\n",
       "  'CatBoost_r177_BAG_L1': -131.18437390585132,\n",
       "  'WeightedEnsemble_L2': -119.91883528766368},\n",
       " 'model_best': 'WeightedEnsemble_L2',\n",
       " 'model_paths': {'LightGBMXT_BAG_L1': ['LightGBMXT_BAG_L1'],\n",
       "  'LightGBM_BAG_L1': ['LightGBM_BAG_L1'],\n",
       "  'RandomForestMSE_BAG_L1': ['RandomForestMSE_BAG_L1'],\n",
       "  'CatBoost_BAG_L1': ['CatBoost_BAG_L1'],\n",
       "  'ExtraTreesMSE_BAG_L1': ['ExtraTreesMSE_BAG_L1'],\n",
       "  'LightGBMLarge_BAG_L1': ['LightGBMLarge_BAG_L1'],\n",
       "  'CatBoost_r177_BAG_L1': ['CatBoost_r177_BAG_L1'],\n",
       "  'WeightedEnsemble_L2': ['WeightedEnsemble_L2']},\n",
       " 'model_fit_times': {'LightGBMXT_BAG_L1': 56.967010736465454,\n",
       "  'LightGBM_BAG_L1': 53.14697790145874,\n",
       "  'RandomForestMSE_BAG_L1': 2.4407684803009033,\n",
       "  'CatBoost_BAG_L1': 313.35075402259827,\n",
       "  'ExtraTreesMSE_BAG_L1': 1.044748067855835,\n",
       "  'LightGBMLarge_BAG_L1': 53.875580072402954,\n",
       "  'CatBoost_r177_BAG_L1': 83.42369937896729,\n",
       "  'WeightedEnsemble_L2': 0.04597330093383789},\n",
       " 'model_pred_times': {'LightGBMXT_BAG_L1': 0.011784791946411133,\n",
       "  'LightGBM_BAG_L1': 0.010209083557128906,\n",
       "  'RandomForestMSE_BAG_L1': 0.21045565605163574,\n",
       "  'CatBoost_BAG_L1': 0.07037734985351562,\n",
       "  'ExtraTreesMSE_BAG_L1': 0.12922954559326172,\n",
       "  'LightGBMLarge_BAG_L1': 0.013570547103881836,\n",
       "  'CatBoost_r177_BAG_L1': 0.044789791107177734,\n",
       "  'WeightedEnsemble_L2': 0.0008697509765625},\n",
       " 'num_bag_folds': 8,\n",
       " 'max_stack_level': 2,\n",
       " 'model_hyperparams': {'LightGBMXT_BAG_L1': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None},\n",
       "  'LightGBM_BAG_L1': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None},\n",
       "  'RandomForestMSE_BAG_L1': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None,\n",
       "   'use_child_oof': True},\n",
       "  'CatBoost_BAG_L1': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None},\n",
       "  'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None,\n",
       "   'use_child_oof': True},\n",
       "  'LightGBMLarge_BAG_L1': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None},\n",
       "  'CatBoost_r177_BAG_L1': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None},\n",
       "  'WeightedEnsemble_L2': {'use_orig_features': False,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None}},\n",
       " 'leaderboard':                     model   score_val              eval_metric  pred_time_val  \\\n",
       " 0     WeightedEnsemble_L2 -119.918835  root_mean_squared_error       0.293488   \n",
       " 1  RandomForestMSE_BAG_L1 -120.169502  root_mean_squared_error       0.210456   \n",
       " 2    ExtraTreesMSE_BAG_L1 -128.125420  root_mean_squared_error       0.129230   \n",
       " 3         CatBoost_BAG_L1 -130.472284  root_mean_squared_error       0.070377   \n",
       " 4    CatBoost_r177_BAG_L1 -131.184374  root_mean_squared_error       0.044790   \n",
       " 5         LightGBM_BAG_L1 -177.944936  root_mean_squared_error       0.010209   \n",
       " 6       LightGBMXT_BAG_L1 -178.751833  root_mean_squared_error       0.011785   \n",
       " 7    LightGBMLarge_BAG_L1 -178.940684  root_mean_squared_error       0.013571   \n",
       " \n",
       "      fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  \\\n",
       " 0  372.804507                0.000870           0.045973            2   \n",
       " 1    2.440768                0.210456           2.440768            1   \n",
       " 2    1.044748                0.129230           1.044748            1   \n",
       " 3  313.350754                0.070377         313.350754            1   \n",
       " 4   83.423699                0.044790          83.423699            1   \n",
       " 5   53.146978                0.010209          53.146978            1   \n",
       " 6   56.967011                0.011785          56.967011            1   \n",
       " 7   53.875580                0.013571          53.875580            1   \n",
       " \n",
       "    can_infer  fit_order  \n",
       " 0       True          8  \n",
       " 1       True          3  \n",
       " 2       True          5  \n",
       " 3       True          4  \n",
       " 4       True          7  \n",
       " 5       True          2  \n",
       " 6       True          1  \n",
       " 7       True          6  }"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor_new_features.fit_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:43:47.335053Z",
     "iopub.status.busy": "2025-12-07T16:43:47.334809Z",
     "iopub.status.idle": "2025-12-07T16:43:47.356434Z",
     "shell.execute_reply": "2025-12-07T16:43:47.355210Z",
     "shell.execute_reply.started": "2025-12-07T16:43:47.335031Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>score_val</th>\n",
       "      <th>eval_metric</th>\n",
       "      <th>pred_time_val</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>pred_time_val_marginal</th>\n",
       "      <th>fit_time_marginal</th>\n",
       "      <th>stack_level</th>\n",
       "      <th>can_infer</th>\n",
       "      <th>fit_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WeightedEnsemble_L2</td>\n",
       "      <td>-119.918835</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>0.293488</td>\n",
       "      <td>372.804507</td>\n",
       "      <td>0.000870</td>\n",
       "      <td>0.045973</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RandomForestMSE_BAG_L1</td>\n",
       "      <td>-120.169502</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>0.210456</td>\n",
       "      <td>2.440768</td>\n",
       "      <td>0.210456</td>\n",
       "      <td>2.440768</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ExtraTreesMSE_BAG_L1</td>\n",
       "      <td>-128.125420</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>0.129230</td>\n",
       "      <td>1.044748</td>\n",
       "      <td>0.129230</td>\n",
       "      <td>1.044748</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CatBoost_BAG_L1</td>\n",
       "      <td>-130.472284</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>0.070377</td>\n",
       "      <td>313.350754</td>\n",
       "      <td>0.070377</td>\n",
       "      <td>313.350754</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CatBoost_r177_BAG_L1</td>\n",
       "      <td>-131.184374</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>0.044790</td>\n",
       "      <td>83.423699</td>\n",
       "      <td>0.044790</td>\n",
       "      <td>83.423699</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LightGBM_BAG_L1</td>\n",
       "      <td>-177.944936</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>0.010209</td>\n",
       "      <td>53.146978</td>\n",
       "      <td>0.010209</td>\n",
       "      <td>53.146978</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LightGBMXT_BAG_L1</td>\n",
       "      <td>-178.751833</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>0.011785</td>\n",
       "      <td>56.967011</td>\n",
       "      <td>0.011785</td>\n",
       "      <td>56.967011</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LightGBMLarge_BAG_L1</td>\n",
       "      <td>-178.940684</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>0.013571</td>\n",
       "      <td>53.875580</td>\n",
       "      <td>0.013571</td>\n",
       "      <td>53.875580</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    model   score_val              eval_metric  pred_time_val  \\\n",
       "0     WeightedEnsemble_L2 -119.918835  root_mean_squared_error       0.293488   \n",
       "1  RandomForestMSE_BAG_L1 -120.169502  root_mean_squared_error       0.210456   \n",
       "2    ExtraTreesMSE_BAG_L1 -128.125420  root_mean_squared_error       0.129230   \n",
       "3         CatBoost_BAG_L1 -130.472284  root_mean_squared_error       0.070377   \n",
       "4    CatBoost_r177_BAG_L1 -131.184374  root_mean_squared_error       0.044790   \n",
       "5         LightGBM_BAG_L1 -177.944936  root_mean_squared_error       0.010209   \n",
       "6       LightGBMXT_BAG_L1 -178.751833  root_mean_squared_error       0.011785   \n",
       "7    LightGBMLarge_BAG_L1 -178.940684  root_mean_squared_error       0.013571   \n",
       "\n",
       "     fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  \\\n",
       "0  372.804507                0.000870           0.045973            2   \n",
       "1    2.440768                0.210456           2.440768            1   \n",
       "2    1.044748                0.129230           1.044748            1   \n",
       "3  313.350754                0.070377         313.350754            1   \n",
       "4   83.423699                0.044790          83.423699            1   \n",
       "5   53.146978                0.010209          53.146978            1   \n",
       "6   56.967011                0.011785          56.967011            1   \n",
       "7   53.875580                0.013571          53.875580            1   \n",
       "\n",
       "   can_infer  fit_order  \n",
       "0       True          8  \n",
       "1       True          3  \n",
       "2       True          5  \n",
       "3       True          4  \n",
       "4       True          7  \n",
       "5       True          2  \n",
       "6       True          1  \n",
       "7       True          6  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leaderboard dataframe\n",
    "leaderboard_new_features_df = pd.DataFrame(predictor_new_features.leaderboard(silent=True))\n",
    "leaderboard_new_features_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:43:47.368797Z",
     "iopub.status.busy": "2025-12-07T16:43:47.368544Z",
     "iopub.status.idle": "2025-12-07T16:43:47.650503Z",
     "shell.execute_reply": "2025-12-07T16:43:47.649550Z",
     "shell.execute_reply.started": "2025-12-07T16:43:47.368775Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming 'leaderboard_df' is your DataFrame\n",
    "models = leaderboard_new_features_df['model']\n",
    "scores = leaderboard_new_features_df['score_val']\n",
    "\n",
    "# Create a figure and an axis\n",
    "fig, ax = plt.subplots(figsize=(14, 7))\n",
    "\n",
    "# Plot the bars using absolute scores and 'lightsalmon' color\n",
    "# We use abs() to ensure the bars plot upwards from 0\n",
    "ax.bar(\n",
    "    models, \n",
    "    scores.abs(),  \n",
    "    color='lightsalmon'\n",
    ")\n",
    "\n",
    "# Set X-axis labels and rotate them for readability\n",
    "ax.set_xlabel(\"Model\")\n",
    "ax.set_ylabel(\"Absolute RMSE Scores\")\n",
    "ax.set_title(\"New Model Performance Comparison (Absolute RMSE)\")\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "ax.legend(['score_val']) \n",
    "\n",
    "plt.tight_layout() # Adjust layout to prevent labels from overlapping\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:43:47.660432Z",
     "iopub.status.busy": "2025-12-07T16:43:47.657426Z",
     "iopub.status.idle": "2025-12-07T16:43:48.366025Z",
     "shell.execute_reply": "2025-12-07T16:43:48.365187Z",
     "shell.execute_reply.started": "2025-12-07T16:43:47.660401Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    90.126976\n",
       "1    75.239059\n",
       "2    75.239059\n",
       "3    89.350655\n",
       "4    89.350655\n",
       "Name: count, dtype: float32"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_new_features = predictor_new_features.predict(test)\n",
    "predictions_new_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:43:48.368612Z",
     "iopub.status.busy": "2025-12-07T16:43:48.368353Z",
     "iopub.status.idle": "2025-12-07T16:43:48.382563Z",
     "shell.execute_reply": "2025-12-07T16:43:48.381792Z",
     "shell.execute_reply.started": "2025-12-07T16:43:48.368591Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6493.000000\n",
       "mean      217.399445\n",
       "std       126.659805\n",
       "min        17.851311\n",
       "25%       120.187096\n",
       "50%       188.148270\n",
       "75%       292.530701\n",
       "max       686.759705\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remember to set all negative values to zero\n",
    "predictions_new_features.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:43:48.393491Z",
     "iopub.status.busy": "2025-12-07T16:43:48.393229Z",
     "iopub.status.idle": "2025-12-07T16:43:48.403946Z",
     "shell.execute_reply": "2025-12-07T16:43:48.402305Z",
     "shell.execute_reply.started": "2025-12-07T16:43:48.393468Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of negative predictions: 0\n",
      "All negative values in the predictions (if any) are set to zero successfully.\n"
     ]
    }
   ],
   "source": [
    "predictions_new_features[predictions_new_features<0] = 0    # (In case, if negative values exist in the predictions_new_features, set them to 0)\n",
    "\n",
    "# Rechecking, if no predictions are less than 0\n",
    "negative_pred_count = predictions_new_features.apply(lambda x: 1 if x<0 else 0)\n",
    "pred_neg_count = (negative_pred_count==1).sum()\n",
    "print(f\"No. of negative predictions: {pred_neg_count}\")\n",
    "print(\"All negative values in the predictions (if any) are set to zero successfully.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:47:58.325624Z",
     "iopub.status.busy": "2025-12-07T16:47:58.325234Z",
     "iopub.status.idle": "2025-12-07T16:47:58.352201Z",
     "shell.execute_reply": "2025-12-07T16:47:58.351202Z",
     "shell.execute_reply.started": "2025-12-07T16:47:58.325588Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    90.126976\n",
       "1    75.239059\n",
       "2    75.239059\n",
       "3    89.350655\n",
       "4    89.350655\n",
       "Name: count, dtype: float32"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_new_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:48:51.319902Z",
     "iopub.status.busy": "2025-12-07T16:48:51.319616Z",
     "iopub.status.idle": "2025-12-07T16:48:51.350328Z",
     "shell.execute_reply": "2025-12-07T16:48:51.349582Z",
     "shell.execute_reply.started": "2025-12-07T16:48:51.319879Z"
    }
   },
   "outputs": [],
   "source": [
    "submission_new_features = pd.read_csv('sampleSubmission.csv', parse_dates=[\"datetime\"])\n",
    "submission_new_features[\"count\"] = predictions_new_features\n",
    "submission_new_features.to_csv(\"submission_new_features.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:48:55.832152Z",
     "iopub.status.busy": "2025-12-07T16:48:55.831815Z",
     "iopub.status.idle": "2025-12-07T16:48:57.552464Z",
     "shell.execute_reply": "2025-12-07T16:48:57.551465Z",
     "shell.execute_reply.started": "2025-12-07T16:48:55.832075Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 188k/188k [00:00<00:00, 669kB/s]\n",
      "Successfully submitted to Bike Sharing Demand"
     ]
    }
   ],
   "source": [
    "!kaggle competitions submit -c bike-sharing-demand -f submission_new_features.csv -m \"new features\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:48:59.381378Z",
     "iopub.status.busy": "2025-12-07T16:48:59.381082Z",
     "iopub.status.idle": "2025-12-07T16:48:59.463760Z",
     "shell.execute_reply": "2025-12-07T16:48:59.463090Z",
     "shell.execute_reply.started": "2025-12-07T16:48:59.381355Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"_date\": \"2025-12-07 16:48:56.703000\",\n",
      "  \"_description\": \"new features\",\n",
      "  \"_error_description\": null,\n",
      "  \"_file_name\": \"submission_new_features.csv\",\n",
      "  \"_is_frozen\": true,\n",
      "  \"_private_score\": \"1.41560\",\n",
      "  \"_public_score\": \"1.41560\",\n",
      "  \"_ref\": 48844886,\n",
      "  \"_status\": \"SubmissionStatus.COMPLETE\",\n",
      "  \"_submitted_by\": \"brejeshbalakrishnan\",\n",
      "  \"_submitted_by_ref\": \"brejeshbalakrishnan\",\n",
      "  \"_team_name\": \"Brejesh Balakrishnan\",\n",
      "  \"_total_bytes\": 192883,\n",
      "  \"_url\": \"/submissions/48844886/48844886.raw\"\n",
      "}\n",
      "----------------------------------------\n",
      "{\n",
      "  \"_date\": \"2025-12-07 16:45:44.443000\",\n",
      "  \"_description\": \"new features\",\n",
      "  \"_error_description\": null,\n",
      "  \"_file_name\": \"submission_new_features.csv\",\n",
      "  \"_is_frozen\": true,\n",
      "  \"_private_score\": \"1.41560\",\n",
      "  \"_public_score\": \"1.41560\",\n",
      "  \"_ref\": 48844797,\n",
      "  \"_status\": \"SubmissionStatus.COMPLETE\",\n",
      "  \"_submitted_by\": \"brejeshbalakrishnan\",\n",
      "  \"_submitted_by_ref\": \"brejeshbalakrishnan\",\n",
      "  \"_team_name\": \"Brejesh Balakrishnan\",\n",
      "  \"_total_bytes\": 192883,\n",
      "  \"_url\": \"/submissions/48844797/48844797.raw\"\n",
      "}\n",
      "----------------------------------------\n",
      "{\n",
      "  \"_date\": \"2025-12-07 16:44:56.420000\",\n",
      "  \"_description\": \"new features\",\n",
      "  \"_error_description\": null,\n",
      "  \"_file_name\": \"submission_new_features.csv\",\n",
      "  \"_is_frozen\": true,\n",
      "  \"_private_score\": \"1.41560\",\n",
      "  \"_public_score\": \"1.41560\",\n",
      "  \"_ref\": 48844776,\n",
      "  \"_status\": \"SubmissionStatus.COMPLETE\",\n",
      "  \"_submitted_by\": \"brejeshbalakrishnan\",\n",
      "  \"_submitted_by_ref\": \"brejeshbalakrishnan\",\n",
      "  \"_team_name\": \"Brejesh Balakrishnan\",\n",
      "  \"_total_bytes\": 192883,\n",
      "  \"_url\": \"/submissions/48844776/48844776.raw\"\n",
      "}\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from kaggle.api.kaggle_api_extended import KaggleApi\n",
    "\n",
    "api = KaggleApi()\n",
    "api.authenticate()\n",
    "subs = api.competition_submissions('bike-sharing-demand')  # no page_number\n",
    "import json\n",
    "\n",
    "for s in subs[0:3]:\n",
    "    print(json.dumps(vars(s), indent=2, sort_keys=True, default=str))\n",
    "    print(\"-\" * 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### New Score of `1.41560`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6: Hyper parameter optimization\n",
    "* There are many options for hyper parameter optimization.\n",
    "* Options are to change the AutoGluon higher level parameters or the individual model hyperparameters.\n",
    "* The hyperparameters of the models themselves that are in AutoGluon. Those need the `hyperparameter` and `hyperparameter_tune_kwargs` arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:39:23.619018Z",
     "iopub.status.busy": "2025-12-07T17:39:23.618747Z",
     "iopub.status.idle": "2025-12-07T17:39:23.626094Z",
     "shell.execute_reply": "2025-12-07T17:39:23.625398Z",
     "shell.execute_reply.started": "2025-12-07T17:39:23.618996Z"
    }
   },
   "outputs": [],
   "source": [
    "import autogluon.core as ag\n",
    "from autogluon.common import space \n",
    "\n",
    "\n",
    "# Define the dictionary for the GBMLarge preset\n",
    "gbm_large_preset = {\n",
    "    \"learning_rate\": 0.03,\n",
    "    \"num_leaves\": 128,\n",
    "    \"feature_fraction\": 0.9,\n",
    "    \"min_data_in_leaf\": 3,\n",
    "    # Priority 0 ensures it doesn't interfere with your custom HPO trial priority\n",
    "    \"ag_args\": {\"name_suffix\": \"Large\", \"priority\": 0, \"hyperparameter_tune_kwargs\": None}, \n",
    "}\n",
    "\n",
    "# Exclude ['NN_TORCH'] family of models\n",
    "excluded_model_types = ['NN_TORCH']\n",
    "\n",
    "# For GBM\n",
    "gbm_options = [\n",
    "    { # Custom HPO Trial Configuration\n",
    "        'extra_trees': True,\n",
    "        'num_boost_round': space.Int(lower=100, upper=800, default=100),\n",
    "        'num_leaves': space.Int(lower=26, upper=66, default=36),\n",
    "        'ag_args': {'name_suffix': 'XT'}\n",
    "    }, \n",
    "    {}, # Default Configuration (to train a baseline GBM)\n",
    "    gbm_large_preset # ⬅️ FIX: Use the dictionary instead of the string 'GBMLarge'\n",
    "]\n",
    "\n",
    "# XT Models\n",
    "xt_options = {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression']}}\n",
    "\n",
    "# XGB Models\n",
    "xgb_options = [{'objective': 'reg:squarederror', \n",
    "                'eval_metric': 'rmse',\n",
    "                'max_depth': space.Int(lower=5, upper=8, default=6), \n",
    "                'n_estimators': space.Int(lower=100, upper=500, default=100), \n",
    "                'eta': .3,\n",
    "                'subsample': 1,\n",
    "                'colsample_bytree': 1}]\n",
    "\n",
    "hyperparameters = {\n",
    "    'GBM': gbm_options,\n",
    "    'XT': xt_options,\n",
    "    'XGB': xgb_options\n",
    "}\n",
    "\n",
    "num_trials = 20\n",
    "search_strategy = 'auto'\n",
    "scheduler = 'local'\n",
    "\n",
    "hyperparameter_tune_kwargs = {\n",
    "                             'num_trials': num_trials,\n",
    "                             'scheduler' : scheduler,\n",
    "                             'searcher': search_strategy,\n",
    "                             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:01:15.887139Z",
     "iopub.status.busy": "2025-12-07T17:01:15.886850Z",
     "iopub.status.idle": "2025-12-07T17:04:05.512443Z",
     "shell.execute_reply": "2025-12-07T17:04:05.511520Z",
     "shell.execute_reply.started": "2025-12-07T17:01:15.887119Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No path specified. Models will be saved in: \"AutogluonModels/ag-20251207_170115\"\n",
      "Verbosity: 2 (Standard Logging)\n",
      "=================== System Info ===================\n",
      "AutoGluon Version:  1.4.0\n",
      "Python Version:     3.12.9\n",
      "Operating System:   Linux\n",
      "Platform Machine:   x86_64\n",
      "Platform Version:   #1 SMP PREEMPT_DYNAMIC Mon Nov  3 18:38:36 UTC 2025\n",
      "CPU Count:          2\n",
      "Memory Avail:       0.44 GB / 3.74 GB (11.6%)\n",
      "Disk Space Avail:   4.01 GB / 4.99 GB (80.3%)\n",
      "\tWARNING: Available disk space is low and there is a risk that AutoGluon will run out of disk during fit, causing an exception. \n",
      "\tWe recommend a minimum available disk space of 10 GB, and large datasets may require more.\n",
      "===================================================\n",
      "Presets specified: ['best_quality']\n",
      "Warning: hyperparameter tuning is currently experimental and may cause the process to hang.\n",
      "Stack configuration (auto_stack=True): num_stack_levels=0, num_bag_folds=8, num_bag_sets=1\n",
      "Beginning AutoGluon training ... Time limit = 600s\n",
      "AutoGluon will save models to \"/home/sagemaker-user/udacity-AWS-ml-engineer-nanodegree/project/AutogluonModels/ag-20251207_170115\"\n",
      "Train Data Rows:    10886\n",
      "Train Data Columns: 19\n",
      "Label Column:       count\n",
      "Problem Type:       regression\n",
      "Preprocessing data ...\n",
      "Using Feature Generators to preprocess the data ...\n",
      "Dropping user-specified ignored columns: ['casual', 'registered']\n",
      "Fitting AutoMLPipelineFeatureGenerator...\n",
      "\tAvailable Memory:                    446.57 MB\n",
      "\tTrain Data (Original)  Memory Usage: 0.93 MB (0.2% of available memory)\n",
      "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
      "\tStage 1 Generators:\n",
      "\t\tFitting AsTypeFeatureGenerator...\n",
      "\t\t\tNote: Converting 4 features to boolean dtype as they only contain 2 unique values.\n",
      "\tStage 2 Generators:\n",
      "\t\tFitting FillNaFeatureGenerator...\n",
      "\tStage 3 Generators:\n",
      "\t\tFitting IdentityFeatureGenerator...\n",
      "\t\tFitting CategoryFeatureGenerator...\n",
      "\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n",
      "\t\tFitting DatetimeFeatureGenerator...\n",
      "\tStage 4 Generators:\n",
      "\t\tFitting DropUniqueFeatureGenerator...\n",
      "\tStage 5 Generators:\n",
      "\t\tFitting DropDuplicatesFeatureGenerator...\n",
      "\tUnused Original Features (Count: 4): ['year_1', 'month_1', 'day_1', 'hour_1']\n",
      "\t\tThese features were not used to generate any of the output features. Add a feature generator compatible with these features to utilize them.\n",
      "\t\tFeatures can also be unused if they carry very little information, such as being categorical but having almost entirely unique values or being duplicates of other features.\n",
      "\t\tThese features do not need to be present at inference time.\n",
      "\t\t('int', []) : 4 | ['year_1', 'month_1', 'day_1', 'hour_1']\n",
      "\tTypes of features in original data (raw dtype, special dtypes):\n",
      "\t\t('category', []) : 2 | ['season', 'weather']\n",
      "\t\t('datetime', []) : 1 | ['datetime']\n",
      "\t\t('float', [])    : 3 | ['temp', 'atemp', 'windspeed']\n",
      "\t\t('int', [])      : 7 | ['holiday', 'workingday', 'humidity', 'year', 'month', ...]\n",
      "\tTypes of features in processed data (raw dtype, special dtypes):\n",
      "\t\t('category', [])             : 2 | ['season', 'weather']\n",
      "\t\t('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
      "\t\t('int', [])                  : 4 | ['humidity', 'month', 'day', 'hour']\n",
      "\t\t('int', ['bool'])            : 3 | ['holiday', 'workingday', 'year']\n",
      "\t\t('int', ['datetime_as_int']) : 3 | ['datetime', 'datetime.year', 'datetime.dayofweek']\n",
      "\t1.1s = Fit runtime\n",
      "\t13 features in original data used to generate 15 features in processed data.\n",
      "\tTrain Data (Processed) Memory Usage: 0.76 MB (0.2% of available memory)\n",
      "Data preprocessing and feature engineering runtime = 1.17s ...\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'\n",
      "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n",
      "\tTo change this, specify the eval_metric parameter of Predictor()\n",
      "User-specified model hyperparameters to be fit:\n",
      "{\n",
      "\t'GBM': [{'extra_trees': True, 'num_boost_round': Int: lower=100, upper=800, 'num_leaves': Int: lower=26, upper=66, 'ag_args': {'name_suffix': 'XT'}}, {}, {'learning_rate': 0.03, 'num_leaves': 128, 'feature_fraction': 0.9, 'min_data_in_leaf': 3, 'ag_args': {'name_suffix': 'Large', 'priority': 0, 'hyperparameter_tune_kwargs': None}}],\n",
      "\t'XT': [{'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression']}}],\n",
      "\t'XGB': [{'objective': 'reg:squarederror', 'eval_metric': 'rmse', 'max_depth': Int: lower=5, upper=8, 'n_estimators': Int: lower=100, upper=500, 'eta': 0.3, 'subsample': 1, 'colsample_bytree': 1}],\n",
      "}\n",
      "Excluded models: [] (Specified by `excluded_model_types`)\n",
      "Fitting 5 L1 models, fit_strategy=\"sequential\" ...\n",
      "Hyperparameter tuning model: LightGBMXT_BAG_L1 ... Tuning model for up to 107.79s of the 598.82s of remaining time.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0cd5f8c392246bc92a2456c2784e0e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=1.86%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: f96840cb3d6fd8909fd4785627ebb8257532591101000000, name=_ray_fit, pid=13208, memory used=0.10GB) was running was 3.56GB / 3.74GB (0.952415), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 162c8db4a139c7645fc43649d4f71f74fd5e4510496ca1cf6cfd9769) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-162c8db4a139c7645fc43649d4f71f74fd5e4510496ca1cf6cfd9769*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.31\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13115\t0.20\tray::IDLE\n",
      "13208\t0.10\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: f96840cb3d6fd8909fd4785627ebb8257532591101000000, name=_ray_fit, pid=13208, memory used=0.10GB) was running was 3.56GB / 3.74GB (0.952415), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 162c8db4a139c7645fc43649d4f71f74fd5e4510496ca1cf6cfd9769) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-162c8db4a139c7645fc43649d4f71f74fd5e4510496ca1cf6cfd9769*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.31\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13115\t0.20\tray::IDLE\n",
      "13208\t0.10\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=3.30%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: baf4bd970f09778d4b2f22fda87979e8a33237d401000000, name=_ray_fit, pid=13270, memory used=0.06GB) was running was 3.55GB / 3.74GB (0.950095), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: c865c1cfc7d5c844a90ef2457c900d334b2c89a5ed03682eaae38d8f) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-c865c1cfc7d5c844a90ef2457c900d334b2c89a5ed03682eaae38d8f*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.31\t/opt/conda/bin/python3.12 -m pylsp\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "13270\t0.06\tray::IDLE\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "1987\t0.04\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/runtime_env/agent/main...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: baf4bd970f09778d4b2f22fda87979e8a33237d401000000, name=_ray_fit, pid=13270, memory used=0.06GB) was running was 3.55GB / 3.74GB (0.950095), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: c865c1cfc7d5c844a90ef2457c900d334b2c89a5ed03682eaae38d8f) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-c865c1cfc7d5c844a90ef2457c900d334b2c89a5ed03682eaae38d8f*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.31\t/opt/conda/bin/python3.12 -m pylsp\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "13270\t0.06\tray::IDLE\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "1987\t0.04\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/runtime_env/agent/main...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=2.79%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: ea2a44543ce4cde8161c83c82f746efb081318c501000000, name=_ray_fit, pid=13544, memory used=0.10GB) was running was 3.55GB / 3.74GB (0.950255), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: cde73ceb2d209cce5e52ade0bee249c4ba54623f249dbce0734f8579) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-cde73ceb2d209cce5e52ade0bee249c4ba54623f249dbce0734f8579*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.31\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13452\t0.20\tray::IDLE\n",
      "13544\t0.10\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: ea2a44543ce4cde8161c83c82f746efb081318c501000000, name=_ray_fit, pid=13544, memory used=0.10GB) was running was 3.55GB / 3.74GB (0.950255), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: cde73ceb2d209cce5e52ade0bee249c4ba54623f249dbce0734f8579) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-cde73ceb2d209cce5e52ade0bee249c4ba54623f249dbce0734f8579*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.31\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13452\t0.20\tray::IDLE\n",
      "13544\t0.10\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=4.72%)\n",
      "I0000 00:00:1765126919.605570    2092 chttp2_transport.cc:1182] ipv4:169.255.255.2:46179: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {grpc_status:14, http2_error:2, created_time:\"2025-12-07T17:01:59.603646721+00:00\"}\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: f543e1cda8136342cdc177460a98154b1f5379b701000000, name=_ray_fit, pid=13924, memory used=0.08GB) was running was 3.56GB / 3.74GB (0.953041), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 4b649fa2fc0a255c48f33c97f3ea5890f55a5c2035f22b7c8cdfd037) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-4b649fa2fc0a255c48f33c97f3ea5890f55a5c2035f22b7c8cdfd037*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.23\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.20\tray::IDLE\n",
      "13924\t0.08\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: f543e1cda8136342cdc177460a98154b1f5379b701000000, name=_ray_fit, pid=13924, memory used=0.08GB) was running was 3.56GB / 3.74GB (0.953041), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 4b649fa2fc0a255c48f33c97f3ea5890f55a5c2035f22b7c8cdfd037) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-4b649fa2fc0a255c48f33c97f3ea5890f55a5c2035f22b7c8cdfd037*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.23\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.20\tray::IDLE\n",
      "13924\t0.08\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=3.68%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 2c00214cb26afc0b566a1f9016d2068aabdaaf8301000000, name=_ray_fit, pid=13958, memory used=0.04GB) was running was 3.56GB / 3.74GB (0.953663), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 58522bf3f6f2fe8b63b4c069b333291d9a793058eee9348444721fb2) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-58522bf3f6f2fe8b63b4c069b333291d9a793058eee9348444721fb2*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.31\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.20\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "12803\t0.07\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 2c00214cb26afc0b566a1f9016d2068aabdaaf8301000000, name=_ray_fit, pid=13958, memory used=0.04GB) was running was 3.56GB / 3.74GB (0.953663), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 58522bf3f6f2fe8b63b4c069b333291d9a793058eee9348444721fb2) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-58522bf3f6f2fe8b63b4c069b333291d9a793058eee9348444721fb2*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.31\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.20\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "12803\t0.07\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=5.24%)\n",
      "2025-12-07 17:02:18,070\tERROR worker.py:420 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 6da13763188e78b9fc8b21584f7ccec4ab4d33eb01000000, name=_ray_fit, pid=13989, memory used=0.04GB) was running was 3.58GB / 3.74GB (0.958581), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 1598fb897efaaa1ce9c0d9291dceae3e4a29c3a9b8a016a8d48d0d2f) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-1598fb897efaaa1ce9c0d9291dceae3e4a29c3a9b8a016a8d48d0d2f*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.07\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 6da13763188e78b9fc8b21584f7ccec4ab4d33eb01000000, name=_ray_fit, pid=13989, memory used=0.04GB) was running was 3.58GB / 3.74GB (0.958581), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 1598fb897efaaa1ce9c0d9291dceae3e4a29c3a9b8a016a8d48d0d2f) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-1598fb897efaaa1ce9c0d9291dceae3e4a29c3a9b8a016a8d48d0d2f*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.07\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=8.56%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 3cf7ed0ccb3af6bb248e5a337ac0737809492ca201000000, name=_ray_fit, pid=14048, memory used=0.05GB) was running was 3.59GB / 3.74GB (0.961027), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 74e9854105fb669f0c5597270d629553f49709783e6d9ec027abb370) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-74e9854105fb669f0c5597270d629553f49709783e6d9ec027abb370*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 3cf7ed0ccb3af6bb248e5a337ac0737809492ca201000000, name=_ray_fit, pid=14048, memory used=0.05GB) was running was 3.59GB / 3.74GB (0.961027), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 74e9854105fb669f0c5597270d629553f49709783e6d9ec027abb370) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-74e9854105fb669f0c5597270d629553f49709783e6d9ec027abb370*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=7.14%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: be2d002b0d3bb21bdbf80513b3a6263e8146360501000000, name=_ray_fit, pid=14077, memory used=0.04GB) was running was 3.56GB / 3.74GB (0.95232), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: f3a8a6ec52e099c6fc7019bdfef40d3725b1439cc875e18427507582) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-f3a8a6ec52e099c6fc7019bdfef40d3725b1439cc875e18427507582*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: be2d002b0d3bb21bdbf80513b3a6263e8146360501000000, name=_ray_fit, pid=14077, memory used=0.04GB) was running was 3.56GB / 3.74GB (0.95232), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: f3a8a6ec52e099c6fc7019bdfef40d3725b1439cc875e18427507582) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-f3a8a6ec52e099c6fc7019bdfef40d3725b1439cc875e18427507582*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=6.38%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 29614d2821b4392bde7b47c221344a98c593847201000000, name=_ray_fit, pid=14136, memory used=0.04GB) was running was 3.55GB / 3.74GB (0.950045), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: f2b65c2a2a7a088fce3fd48d2af38f70d37f377bda4d2745239b3c25) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-f2b65c2a2a7a088fce3fd48d2af38f70d37f377bda4d2745239b3c25*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 29614d2821b4392bde7b47c221344a98c593847201000000, name=_ray_fit, pid=14136, memory used=0.04GB) was running was 3.55GB / 3.74GB (0.950045), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: f2b65c2a2a7a088fce3fd48d2af38f70d37f377bda4d2745239b3c25) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-f2b65c2a2a7a088fce3fd48d2af38f70d37f377bda4d2745239b3c25*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=6.73%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 7cedf3fd8113014bfa35bcb3658e95a40f7935c101000000, name=_ray_fit, pid=14166, memory used=0.05GB) was running was 3.58GB / 3.74GB (0.957762), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 2111be0966467f0e8e42285908dd8abfef8efecaf80ad8ae3a8b12fd) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-2111be0966467f0e8e42285908dd8abfef8efecaf80ad8ae3a8b12fd*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "14166\t0.05\tray::IDLE\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 7cedf3fd8113014bfa35bcb3658e95a40f7935c101000000, name=_ray_fit, pid=14166, memory used=0.05GB) was running was 3.58GB / 3.74GB (0.957762), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 2111be0966467f0e8e42285908dd8abfef8efecaf80ad8ae3a8b12fd) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-2111be0966467f0e8e42285908dd8abfef8efecaf80ad8ae3a8b12fd*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "14166\t0.05\tray::IDLE\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=5.08%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: d6df3005209c43583ccf869128a06c12ce8303a201000000, name=_ray_fit, pid=14224, memory used=0.05GB) was running was 3.56GB / 3.74GB (0.952126), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 2a43c44c4231cbeed3518699decf924d09ead975efdb0cc6c75e607b) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-2a43c44c4231cbeed3518699decf924d09ead975efdb0cc6c75e607b*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: d6df3005209c43583ccf869128a06c12ce8303a201000000, name=_ray_fit, pid=14224, memory used=0.05GB) was running was 3.56GB / 3.74GB (0.952126), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 2a43c44c4231cbeed3518699decf924d09ead975efdb0cc6c75e607b) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-2a43c44c4231cbeed3518699decf924d09ead975efdb0cc6c75e607b*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=4.46%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: ad2289da246b800f1d593667af1b1152b74eba9501000000, name=_ray_fit, pid=14253, memory used=0.04GB) was running was 3.57GB / 3.74GB (0.956101), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 045b1640374ffa3aef45f84b8852050a04ad521d7e6114f61cf61e3a) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-045b1640374ffa3aef45f84b8852050a04ad521d7e6114f61cf61e3a*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: ad2289da246b800f1d593667af1b1152b74eba9501000000, name=_ray_fit, pid=14253, memory used=0.04GB) was running was 3.57GB / 3.74GB (0.956101), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 045b1640374ffa3aef45f84b8852050a04ad521d7e6114f61cf61e3a) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-045b1640374ffa3aef45f84b8852050a04ad521d7e6114f61cf61e3a*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=5.75%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 50ad1bddadea3893f5f018e73afda6c3c712599801000000, name=_ray_fit, pid=14282, memory used=0.05GB) was running was 3.58GB / 3.74GB (0.9574), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 3852fab722b5713e22f51ab5a27168f174aed7b1c3faa8b314cdefb7) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-3852fab722b5713e22f51ab5a27168f174aed7b1c3faa8b314cdefb7*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "14282\t0.05\tray::IDLE\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 50ad1bddadea3893f5f018e73afda6c3c712599801000000, name=_ray_fit, pid=14282, memory used=0.05GB) was running was 3.58GB / 3.74GB (0.9574), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 3852fab722b5713e22f51ab5a27168f174aed7b1c3faa8b314cdefb7) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-3852fab722b5713e22f51ab5a27168f174aed7b1c3faa8b314cdefb7*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "14282\t0.05\tray::IDLE\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=6.55%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: bfb820386d512ab02e6e462218a7650b2d91279001000000, name=_ray_fit, pid=14340, memory used=0.04GB) was running was 3.58GB / 3.74GB (0.958351), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: ab2705b6e7584291019493a5e284455113f6257db932609054f348ba) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-ab2705b6e7584291019493a5e284455113f6257db932609054f348ba*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.20\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: bfb820386d512ab02e6e462218a7650b2d91279001000000, name=_ray_fit, pid=14340, memory used=0.04GB) was running was 3.58GB / 3.74GB (0.958351), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: ab2705b6e7584291019493a5e284455113f6257db932609054f348ba) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-ab2705b6e7584291019493a5e284455113f6257db932609054f348ba*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.20\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "2025-12-07 17:02:25,507\tERROR worker.py:420 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=4.03%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 2424c0b697a0f3581eacc58dfe3e675f27c58c6101000000, name=_ray_fit, pid=14369, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.95539), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 6e92d7332d1f557b39e3cf70c2eb5f1a8528eb926264a16f69c256f0) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-6e92d7332d1f557b39e3cf70c2eb5f1a8528eb926264a16f69c256f0*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 2424c0b697a0f3581eacc58dfe3e675f27c58c6101000000, name=_ray_fit, pid=14369, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.95539), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 6e92d7332d1f557b39e3cf70c2eb5f1a8528eb926264a16f69c256f0) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-6e92d7332d1f557b39e3cf70c2eb5f1a8528eb926264a16f69c256f0*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "13800\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=5.81%)\n",
      "I0000 00:00:1765126954.565338    2097 chttp2_transport.cc:1182] ipv4:169.255.255.2:32965: Got goaway [2] err=UNAVAILABLE:GOAWAY received; Error code: 2; Debug Text: Cancelling all calls {grpc_status:14, http2_error:2, created_time:\"2025-12-07T17:02:34.563449214+00:00\"}\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: f39fd9e626d2d0f8802c9704ed53ac25e1c9edc501000000, name=_ray_fit, pid=14552, memory used=0.07GB) was running was 3.56GB / 3.74GB (0.952085), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 45f51d7c2c9da2165a2325955602c7f1f28783cc40930c4a77bdafd5) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-45f51d7c2c9da2165a2325955602c7f1f28783cc40930c4a77bdafd5*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14398\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "14552\t0.07\tray::IDLE\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: f39fd9e626d2d0f8802c9704ed53ac25e1c9edc501000000, name=_ray_fit, pid=14552, memory used=0.07GB) was running was 3.56GB / 3.74GB (0.952085), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 45f51d7c2c9da2165a2325955602c7f1f28783cc40930c4a77bdafd5) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-45f51d7c2c9da2165a2325955602c7f1f28783cc40930c4a77bdafd5*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14398\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "14552\t0.07\tray::IDLE\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=4.88%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: d506145da3fcfe256c730381429ed86100fea68d01000000, name=_ray_fit, pid=14583, memory used=0.05GB) was running was 3.59GB / 3.74GB (0.959871), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 00c81a6454f5c1d4495688dc7058f25ffa4cd2f44b74a7ae948127ac) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-00c81a6454f5c1d4495688dc7058f25ffa4cd2f44b74a7ae948127ac*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14398\t0.22\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: d506145da3fcfe256c730381429ed86100fea68d01000000, name=_ray_fit, pid=14583, memory used=0.05GB) was running was 3.59GB / 3.74GB (0.959871), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 00c81a6454f5c1d4495688dc7058f25ffa4cd2f44b74a7ae948127ac) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-00c81a6454f5c1d4495688dc7058f25ffa4cd2f44b74a7ae948127ac*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14398\t0.22\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=8.24%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 9949fec29432f493f996b44f7b8a324f661d597101000000, name=_ray_fit, pid=14617, memory used=0.05GB) was running was 3.59GB / 3.74GB (0.960073), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 1c6cb003282659ae001b7bac20eacf78fccb4809243a58671cc0575f) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-1c6cb003282659ae001b7bac20eacf78fccb4809243a58671cc0575f*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14398\t0.22\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 9949fec29432f493f996b44f7b8a324f661d597101000000, name=_ray_fit, pid=14617, memory used=0.05GB) was running was 3.59GB / 3.74GB (0.960073), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 1c6cb003282659ae001b7bac20eacf78fccb4809243a58671cc0575f) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-1c6cb003282659ae001b7bac20eacf78fccb4809243a58671cc0575f*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14398\t0.22\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=6.53%)\n",
      "2025-12-07 17:02:37,684\tERROR worker.py:420 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 923d042df5fdf8ddfec1fe91a272667611b283c501000000, name=_ray_fit, pid=14676, memory used=0.05GB) was running was 3.58GB / 3.74GB (0.95781), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 05c2552f560312286f5ecaa5f89aadaee8bf5686c1502fd0febe1964) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-05c2552f560312286f5ecaa5f89aadaee8bf5686c1502fd0febe1964*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14398\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 923d042df5fdf8ddfec1fe91a272667611b283c501000000, name=_ray_fit, pid=14676, memory used=0.05GB) was running was 3.58GB / 3.74GB (0.95781), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 05c2552f560312286f5ecaa5f89aadaee8bf5686c1502fd0febe1964) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-05c2552f560312286f5ecaa5f89aadaee8bf5686c1502fd0febe1964*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14398\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=7.61%)\n",
      "2025-12-07 17:02:38,881\tERROR worker.py:420 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 55d05025db8c81f5d7c2b31cd2b955a7aef837a801000000, name=_ray_fit, pid=14735, memory used=0.04GB) was running was 3.59GB / 3.74GB (0.960003), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 0b517f346d132ee7a0055534cdbfe351183c4c2625fd24b6d914887f) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-0b517f346d132ee7a0055534cdbfe351183c4c2625fd24b6d914887f*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14398\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 55d05025db8c81f5d7c2b31cd2b955a7aef837a801000000, name=_ray_fit, pid=14735, memory used=0.04GB) was running was 3.59GB / 3.74GB (0.960003), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 0b517f346d132ee7a0055534cdbfe351183c4c2625fd24b6d914887f) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-0b517f346d132ee7a0055534cdbfe351183c4c2625fd24b6d914887f*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14398\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "No model was trained during hyperparameter tuning LightGBMXT_BAG_L1... Skipping this model.\n",
      "Hyperparameter tuning model: LightGBM_BAG_L1 ... Tuning model for up to 107.79s of the 516.68s of remaining time.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ee4db60062c45faae8336eec3c9da0b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=6.56%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: a7a6a3bd625f4deae69b599bced43b0a2e44e46a01000000, name=_ray_fit, pid=14794, memory used=0.05GB) was running was 3.56GB / 3.74GB (0.95309), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 7f26abd71ad1b57de8f08bd18590e4c9132b8729a83faea802d39cd0) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-7f26abd71ad1b57de8f08bd18590e4c9132b8729a83faea802d39cd0*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14398\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "14794\t0.05\tray::IDLE\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: a7a6a3bd625f4deae69b599bced43b0a2e44e46a01000000, name=_ray_fit, pid=14794, memory used=0.05GB) was running was 3.56GB / 3.74GB (0.95309), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 7f26abd71ad1b57de8f08bd18590e4c9132b8729a83faea802d39cd0) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-7f26abd71ad1b57de8f08bd18590e4c9132b8729a83faea802d39cd0*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14398\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "14794\t0.05\tray::IDLE\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tMemory not enough to fit 8 folds in parallel. Will train 4 folds in parallel instead (Estimated 12.63% memory usage per fold, 50.53%/80.00% total).\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=12.63%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 3c0467fc761469fe3fb4984cd18475592571ca7d01000000, name=_ray_fit, pid=14866, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.956149), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: dfba38a405851ad74962b37555c47698741d3b121f34f88631dcadb8) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-dfba38a405851ad74962b37555c47698741d3b121f34f88631dcadb8*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14398\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 3c0467fc761469fe3fb4984cd18475592571ca7d01000000, name=_ray_fit, pid=14866, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.956149), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: dfba38a405851ad74962b37555c47698741d3b121f34f88631dcadb8) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-dfba38a405851ad74962b37555c47698741d3b121f34f88631dcadb8*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14398\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=8.27%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 43ddc39a5feca6b76feb7559a91c7707cfbc632c01000000, name=_ray_fit, pid=15054, memory used=0.07GB) was running was 3.56GB / 3.74GB (0.953629), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 47ad83faa31e10b30ab6f82c226bbe85d6df2cf45723ebdefa736d0e) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-47ad83faa31e10b30ab6f82c226bbe85d6df2cf45723ebdefa736d0e*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.20\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "15054\t0.07\tray::IDLE\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 43ddc39a5feca6b76feb7559a91c7707cfbc632c01000000, name=_ray_fit, pid=15054, memory used=0.07GB) was running was 3.56GB / 3.74GB (0.953629), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 47ad83faa31e10b30ab6f82c226bbe85d6df2cf45723ebdefa736d0e) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-47ad83faa31e10b30ab6f82c226bbe85d6df2cf45723ebdefa736d0e*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.20\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "15054\t0.07\tray::IDLE\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=9.16%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: eb5746a5f060773204234aa28bac41a031eee3ae01000000, name=_ray_fit, pid=15084, memory used=0.04GB) was running was 3.57GB / 3.74GB (0.953883), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 7256fe4ff93b1c692b7475f71df3aeef3a3f00ce6cce7e57e126955c) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-7256fe4ff93b1c692b7475f71df3aeef3a3f00ce6cce7e57e126955c*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: eb5746a5f060773204234aa28bac41a031eee3ae01000000, name=_ray_fit, pid=15084, memory used=0.04GB) was running was 3.57GB / 3.74GB (0.953883), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 7256fe4ff93b1c692b7475f71df3aeef3a3f00ce6cce7e57e126955c) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-7256fe4ff93b1c692b7475f71df3aeef3a3f00ce6cce7e57e126955c*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=8.96%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 8e25ee6a0edcd3f7fb24abc7e0ed5db6c799b71001000000, name=_ray_fit, pid=15086, memory used=0.04GB) was running was 3.58GB / 3.74GB (0.957595), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 2c8ba68506c37911ec5cbbb255a9bb00d34d2d6273a57cb1819222ec) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-2c8ba68506c37911ec5cbbb255a9bb00d34d2d6273a57cb1819222ec*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.22\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 8e25ee6a0edcd3f7fb24abc7e0ed5db6c799b71001000000, name=_ray_fit, pid=15086, memory used=0.04GB) was running was 3.58GB / 3.74GB (0.957595), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 2c8ba68506c37911ec5cbbb255a9bb00d34d2d6273a57cb1819222ec) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-2c8ba68506c37911ec5cbbb255a9bb00d34d2d6273a57cb1819222ec*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.22\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tMemory not enough to fit 8 folds in parallel. Will train 4 folds in parallel instead (Estimated 12.24% memory usage per fold, 48.98%/80.00% total).\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=12.24%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: da61158fc04f7671cb30486ece64c2e593dedd9401000000, name=_ray_fit, pid=15116, memory used=0.04GB) was running was 3.58GB / 3.74GB (0.957665), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: c2e883db1fe509d3736b56c47ef8a8be6ae7815bd344124e56611ef9) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-c2e883db1fe509d3736b56c47ef8a8be6ae7815bd344124e56611ef9*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.22\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: da61158fc04f7671cb30486ece64c2e593dedd9401000000, name=_ray_fit, pid=15116, memory used=0.04GB) was running was 3.58GB / 3.74GB (0.957665), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: c2e883db1fe509d3736b56c47ef8a8be6ae7815bd344124e56611ef9) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-c2e883db1fe509d3736b56c47ef8a8be6ae7815bd344124e56611ef9*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.22\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tMemory not enough to fit 8 folds in parallel. Will train 4 folds in parallel instead (Estimated 10.34% memory usage per fold, 41.34%/80.00% total).\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=10.34%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 44d37a0e0b927da6ce01c0851c7d6c5b0328a0bc01000000, name=_ray_fit, pid=15146, memory used=0.04GB) was running was 3.56GB / 3.74GB (0.952625), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 72f1b2ed55e8e8967c1cdf149e306115a7edb0eea698477959077056) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-72f1b2ed55e8e8967c1cdf149e306115a7edb0eea698477959077056*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 44d37a0e0b927da6ce01c0851c7d6c5b0328a0bc01000000, name=_ray_fit, pid=15146, memory used=0.04GB) was running was 3.56GB / 3.74GB (0.952625), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 72f1b2ed55e8e8967c1cdf149e306115a7edb0eea698477959077056) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-72f1b2ed55e8e8967c1cdf149e306115a7edb0eea698477959077056*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=6.95%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 06ab8eaedb43701a2dce97fb2407f7e35ba640dd01000000, name=_ray_fit, pid=15175, memory used=0.04GB) was running was 3.59GB / 3.74GB (0.959839), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 947b6fab12b0a37ce425ab999823bc1d3a3e8820d10d9fa20f01a522) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-947b6fab12b0a37ce425ab999823bc1d3a3e8820d10d9fa20f01a522*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 06ab8eaedb43701a2dce97fb2407f7e35ba640dd01000000, name=_ray_fit, pid=15175, memory used=0.04GB) was running was 3.59GB / 3.74GB (0.959839), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 947b6fab12b0a37ce425ab999823bc1d3a3e8820d10d9fa20f01a522) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-947b6fab12b0a37ce425ab999823bc1d3a3e8820d10d9fa20f01a522*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tMemory not enough to fit 8 folds in parallel. Will train 4 folds in parallel instead (Estimated 10.24% memory usage per fold, 40.97%/80.00% total).\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=10.24%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 1919a1f030d2f871c01d528c9be84b36139ca2b801000000, name=_ray_fit, pid=15204, memory used=0.05GB) was running was 3.58GB / 3.74GB (0.95696), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 44891fe1cf2b9bd1aa4c1a53dbd9ce837d847e9b7ad1de00a5418aae) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-44891fe1cf2b9bd1aa4c1a53dbd9ce837d847e9b7ad1de00a5418aae*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.20\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "15204\t0.05\tray::IDLE\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 1919a1f030d2f871c01d528c9be84b36139ca2b801000000, name=_ray_fit, pid=15204, memory used=0.05GB) was running was 3.58GB / 3.74GB (0.95696), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 44891fe1cf2b9bd1aa4c1a53dbd9ce837d847e9b7ad1de00a5418aae) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-44891fe1cf2b9bd1aa4c1a53dbd9ce837d847e9b7ad1de00a5418aae*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.20\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "15204\t0.05\tray::IDLE\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tMemory not enough to fit 8 folds in parallel. Will train 4 folds in parallel instead (Estimated 11.88% memory usage per fold, 47.51%/80.00% total).\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=11.88%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: ab35ba6b21eda4017829ba7545fc8c40e5a4ebf801000000, name=_ray_fit, pid=15262, memory used=0.05GB) was running was 3.59GB / 3.74GB (0.959579), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 66fc7f59788ee2821650a82ce7cefd589a7b9bd3a32450ce320b9dae) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-66fc7f59788ee2821650a82ce7cefd589a7b9bd3a32450ce320b9dae*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: ab35ba6b21eda4017829ba7545fc8c40e5a4ebf801000000, name=_ray_fit, pid=15262, memory used=0.05GB) was running was 3.59GB / 3.74GB (0.959579), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 66fc7f59788ee2821650a82ce7cefd589a7b9bd3a32450ce320b9dae) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-66fc7f59788ee2821650a82ce7cefd589a7b9bd3a32450ce320b9dae*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=9.84%)\n",
      "2025-12-07 17:02:56,789\tERROR worker.py:420 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: aec5c40c3fb70475e2aa7a2bf7c4bcf5d727ebce01000000, name=_ray_fit, pid=15328, memory used=0.04GB) was running was 3.56GB / 3.74GB (0.953674), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: da6fd280558607a73056f5301e44b5437ed04eea90f4e1f558134875) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-da6fd280558607a73056f5301e44b5437ed04eea90f4e1f558134875*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: aec5c40c3fb70475e2aa7a2bf7c4bcf5d727ebce01000000, name=_ray_fit, pid=15328, memory used=0.04GB) was running was 3.56GB / 3.74GB (0.953674), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: da6fd280558607a73056f5301e44b5437ed04eea90f4e1f558134875) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-da6fd280558607a73056f5301e44b5437ed04eea90f4e1f558134875*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=9.85%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: bf02d4682b5d91048981b9512b1e7029bb72d50701000000, name=_ray_fit, pid=15358, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.955542), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 2abfe921f41a8ace8893ee74c9ba63720f208c83ddf1fd766fb9c0b9) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-2abfe921f41a8ace8893ee74c9ba63720f208c83ddf1fd766fb9c0b9*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.20\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: bf02d4682b5d91048981b9512b1e7029bb72d50701000000, name=_ray_fit, pid=15358, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.955542), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 2abfe921f41a8ace8893ee74c9ba63720f208c83ddf1fd766fb9c0b9) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-2abfe921f41a8ace8893ee74c9ba63720f208c83ddf1fd766fb9c0b9*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.20\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tMemory not enough to fit 8 folds in parallel. Will train 4 folds in parallel instead (Estimated 12.14% memory usage per fold, 48.57%/80.00% total).\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=12.14%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: fde41656c7201ee071ec15d056b1949ddbd23a6f01000000, name=_ray_fit, pid=15388, memory used=0.04GB) was running was 3.55GB / 3.74GB (0.951068), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 21c14136055f76fb363dba02a9623b2840e09acdb01d1485ca16dd94) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-21c14136055f76fb363dba02a9623b2840e09acdb01d1485ca16dd94*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: fde41656c7201ee071ec15d056b1949ddbd23a6f01000000, name=_ray_fit, pid=15388, memory used=0.04GB) was running was 3.55GB / 3.74GB (0.951068), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 21c14136055f76fb363dba02a9623b2840e09acdb01d1485ca16dd94) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-21c14136055f76fb363dba02a9623b2840e09acdb01d1485ca16dd94*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.38\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "14882\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=2.30%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: d8e797810bfdee97474e35f9d1ed1c8e196a955801000000, name=_ray_fit, pid=15581, memory used=0.06GB) was running was 3.56GB / 3.74GB (0.951709), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 4010346f91f1c572401ab4fe3ceb6fb266a0ab0411e42bd7ab8e9b69) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-4010346f91f1c572401ab4fe3ceb6fb266a0ab0411e42bd7ab8e9b69*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15418\t0.18\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "15581\t0.06\tray::IDLE\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: d8e797810bfdee97474e35f9d1ed1c8e196a955801000000, name=_ray_fit, pid=15581, memory used=0.06GB) was running was 3.56GB / 3.74GB (0.951709), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 4010346f91f1c572401ab4fe3ceb6fb266a0ab0411e42bd7ab8e9b69) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-4010346f91f1c572401ab4fe3ceb6fb266a0ab0411e42bd7ab8e9b69*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15418\t0.18\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "15581\t0.06\tray::IDLE\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=5.37%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 5987e34c51bd55c7d20e4a56cc4bf768cace45d001000000, name=_ray_fit, pid=15612, memory used=0.04GB) was running was 3.58GB / 3.74GB (0.958318), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 3c62f39d8f918e06531a39fbc3fa4f98952e1dbc94465b5f8765188a) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-3c62f39d8f918e06531a39fbc3fa4f98952e1dbc94465b5f8765188a*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15418\t0.22\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 5987e34c51bd55c7d20e4a56cc4bf768cace45d001000000, name=_ray_fit, pid=15612, memory used=0.04GB) was running was 3.58GB / 3.74GB (0.958318), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 3c62f39d8f918e06531a39fbc3fa4f98952e1dbc94465b5f8765188a) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-3c62f39d8f918e06531a39fbc3fa4f98952e1dbc94465b5f8765188a*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.23\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15418\t0.22\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tMemory not enough to fit 8 folds in parallel. Will train 4 folds in parallel instead (Estimated 11.05% memory usage per fold, 44.18%/80.00% total).\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=11.05%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: d30b6a6c6a864409e1712c60ed4b906cf6354c9401000000, name=_ray_fit, pid=15670, memory used=0.04GB) was running was 3.57GB / 3.74GB (0.954142), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: d810131bfc244c5ccaae869eb1b7645479be4107ba9e224561355424) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-d810131bfc244c5ccaae869eb1b7645479be4107ba9e224561355424*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15418\t0.22\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: d30b6a6c6a864409e1712c60ed4b906cf6354c9401000000, name=_ray_fit, pid=15670, memory used=0.04GB) was running was 3.57GB / 3.74GB (0.954142), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: d810131bfc244c5ccaae869eb1b7645479be4107ba9e224561355424) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-d810131bfc244c5ccaae869eb1b7645479be4107ba9e224561355424*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15418\t0.22\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tMemory not enough to fit 8 folds in parallel. Will train 4 folds in parallel instead (Estimated 12.53% memory usage per fold, 50.13%/80.00% total).\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=12.53%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 113fe470dec0861b79afc18fb147d3a1d642b9fa01000000, name=_ray_fit, pid=15714, memory used=0.05GB) was running was 3.60GB / 3.74GB (0.964198), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 14ac9bddd7098cac84bd7d38e41241c80072a185caa4acdf531879a6) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-14ac9bddd7098cac84bd7d38e41241c80072a185caa4acdf531879a6*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15418\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 113fe470dec0861b79afc18fb147d3a1d642b9fa01000000, name=_ray_fit, pid=15714, memory used=0.05GB) was running was 3.60GB / 3.74GB (0.964198), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 14ac9bddd7098cac84bd7d38e41241c80072a185caa4acdf531879a6) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-14ac9bddd7098cac84bd7d38e41241c80072a185caa4acdf531879a6*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15418\t0.21\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=4.25%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: d9f619f928235d626eaf3c7ca89cab7561e93b0c01000000, name=_ray_fit, pid=15758, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.954754), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: a8274448d0b522f06e89df6d67634b713836ea22ef158d43af775326) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-a8274448d0b522f06e89df6d67634b713836ea22ef158d43af775326*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15418\t0.20\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "15758\t0.05\tray::IDLE\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: d9f619f928235d626eaf3c7ca89cab7561e93b0c01000000, name=_ray_fit, pid=15758, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.954754), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: a8274448d0b522f06e89df6d67634b713836ea22ef158d43af775326) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-a8274448d0b522f06e89df6d67634b713836ea22ef158d43af775326*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15418\t0.20\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "15758\t0.05\tray::IDLE\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=5.23%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 5402d9b8881d4a45a18e2766002783221bb03e8801000000, name=_ray_fit, pid=15816, memory used=0.05GB) was running was 3.59GB / 3.74GB (0.959431), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 4c44f47df43cc1f6156e076d4a10c750133d0b3f8d8cfb5d90eedafa) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-4c44f47df43cc1f6156e076d4a10c750133d0b3f8d8cfb5d90eedafa*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15418\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "15816\t0.05\tray::IDLE\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 5402d9b8881d4a45a18e2766002783221bb03e8801000000, name=_ray_fit, pid=15816, memory used=0.05GB) was running was 3.59GB / 3.74GB (0.959431), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 4c44f47df43cc1f6156e076d4a10c750133d0b3f8d8cfb5d90eedafa) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-4c44f47df43cc1f6156e076d4a10c750133d0b3f8d8cfb5d90eedafa*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15418\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "15816\t0.05\tray::IDLE\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=6.62%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 38bb145408a79b276a601bdb2961ca83b39f978401000000, name=_ray_fit, pid=15845, memory used=0.04GB) was running was 3.59GB / 3.74GB (0.961485), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 813a8efd0bfded8ffe7aa7f65969b386e620355c608c94daa212ad78) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-813a8efd0bfded8ffe7aa7f65969b386e620355c608c94daa212ad78*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15418\t0.20\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 38bb145408a79b276a601bdb2961ca83b39f978401000000, name=_ray_fit, pid=15845, memory used=0.04GB) was running was 3.59GB / 3.74GB (0.961485), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 813a8efd0bfded8ffe7aa7f65969b386e620355c608c94daa212ad78) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-813a8efd0bfded8ffe7aa7f65969b386e620355c608c94daa212ad78*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.24\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15418\t0.20\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "No model was trained during hyperparameter tuning LightGBM_BAG_L1... Skipping this model.\n",
      "Hyperparameter tuning model: ExtraTreesMSE_BAG_L1 ... Tuning model for up to 107.79s of the 481.2s of remaining time.\n",
      "\tNo hyperparameter search space specified for ExtraTreesMSE_BAG_L1. Skipping HPO. Will train one model based on the provided hyperparameters.\n",
      "\tWarning: Reducing model 'n_estimators' from 300 -> 61 due to low memory. Expected memory usage reduced from 72.86% -> 15.0% of available memory...\n",
      "Fitted model: ExtraTreesMSE_BAG_L1 ...\n",
      "\t-39.9538\t = Validation score   (-root_mean_squared_error)\n",
      "\t2.41s\t = Training   runtime\n",
      "\t0.13s\t = Validation runtime\n",
      "Hyperparameter tuning model: XGBoost_BAG_L1 ... Tuning model for up to 107.79s of the 478.55s of remaining time.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2aef6a804fb643f2b1457012dc90d109",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=3.51%)\n",
      "\u001b[36mray::_ray_fit()\u001b[39m (pid=15903, ip=169.255.255.2)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 446, in _ray_fit\n",
      "    fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, time_limit=time_limit_fold, **resources, **kwargs_fold)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/tabular/models/xgboost/xgboost_model.py\", line 171, in _fit\n",
      "    metric_name=eval_metric_name,  # forces stopping_metric rather than arbitrary last metric\n",
      "                ^^^^^^^^^^^^^^^^\n",
      "UnboundLocalError: cannot access local variable 'eval_metric_name' where it is not associated with a value\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 929, in get_objects\n",
      "    raise value.as_instanceof_cause()\n",
      "ray.exceptions.RayTaskError(UnboundLocalError): \u001b[36mray::_ray_fit()\u001b[39m (pid=15903, ip=169.255.255.2)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 446, in _ray_fit\n",
      "    fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, time_limit=time_limit_fold, **resources, **kwargs_fold)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/tabular/models/xgboost/xgboost_model.py\", line 171, in _fit\n",
      "    metric_name=eval_metric_name,  # forces stopping_metric rather than arbitrary last metric\n",
      "                ^^^^^^^^^^^^^^^^\n",
      "UnboundLocalError: cannot access local variable 'eval_metric_name' where it is not associated with a value\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=5.04%)\n",
      "2025-12-07 17:03:24,287\tERROR worker.py:420 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: d0629e63c028afc88f89dd8baf5c794fd524299b01000000, name=_ray_fit, pid=16073, memory used=0.04GB) was running was 3.59GB / 3.74GB (0.959208), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: d1b6e0de01ca4b31c9265dae707850fbebaa616823755fe39fbd7128) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-d1b6e0de01ca4b31c9265dae707850fbebaa616823755fe39fbd7128*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15903\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: d0629e63c028afc88f89dd8baf5c794fd524299b01000000, name=_ray_fit, pid=16073, memory used=0.04GB) was running was 3.59GB / 3.74GB (0.959208), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: d1b6e0de01ca4b31c9265dae707850fbebaa616823755fe39fbd7128) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-d1b6e0de01ca4b31c9265dae707850fbebaa616823755fe39fbd7128*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15903\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tMemory not enough to fit 8 folds in parallel. Will train 4 folds in parallel instead (Estimated 17.91% memory usage per fold, 71.62%/80.00% total).\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=17.91%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 01f6bd9f8adfee53dbf1d8a60f42657f62e9c29d01000000, name=_ray_fit, pid=16132, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.956288), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: e4490fb9e47a54350d40c997c76cc045bbf162273cc1555003ed468d) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-e4490fb9e47a54350d40c997c76cc045bbf162273cc1555003ed468d*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15903\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "16132\t0.05\tray::IDLE\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 01f6bd9f8adfee53dbf1d8a60f42657f62e9c29d01000000, name=_ray_fit, pid=16132, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.956288), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: e4490fb9e47a54350d40c997c76cc045bbf162273cc1555003ed468d) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-e4490fb9e47a54350d40c997c76cc045bbf162273cc1555003ed468d*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15903\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "16132\t0.05\tray::IDLE\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=7.02%)\n",
      "2025-12-07 17:03:26,954\tERROR worker.py:420 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: f4f3909d581e5cc91c8f2c332977beca5ac4f5c501000000, name=_ray_fit, pid=16164, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.95603), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 7955240a2e69ae9016d44e758dd5c3ab910fb48284a32f15878df990) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-7955240a2e69ae9016d44e758dd5c3ab910fb48284a32f15878df990*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15903\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: f4f3909d581e5cc91c8f2c332977beca5ac4f5c501000000, name=_ray_fit, pid=16164, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.95603), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 7955240a2e69ae9016d44e758dd5c3ab910fb48284a32f15878df990) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-7955240a2e69ae9016d44e758dd5c3ab910fb48284a32f15878df990*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15903\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=7.71%)\n",
      "2025-12-07 17:03:28,228\tERROR worker.py:420 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: e08b00316050880ff1d1eceb180c6269a982a07801000000, name=_ray_fit, pid=16225, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.953935), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: cea27a6d42b9c4dfe15c846a442ec8143a650beee28903fa4ef199fc) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-cea27a6d42b9c4dfe15c846a442ec8143a650beee28903fa4ef199fc*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15903\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "16225\t0.05\tray::IDLE\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: e08b00316050880ff1d1eceb180c6269a982a07801000000, name=_ray_fit, pid=16225, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.953935), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: cea27a6d42b9c4dfe15c846a442ec8143a650beee28903fa4ef199fc) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-cea27a6d42b9c4dfe15c846a442ec8143a650beee28903fa4ef199fc*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15903\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "16225\t0.05\tray::IDLE\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=7.38%)\n",
      "2025-12-07 17:03:29,546\tERROR worker.py:420 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: f925d533c2ef7f4e443419117d4f2f5682af2c9f01000000, name=_ray_fit, pid=16284, memory used=0.04GB) was running was 3.59GB / 3.74GB (0.959133), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 64a4e1a2c4979952c3e66af40434c238ef040140d552669e86792e5d) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-64a4e1a2c4979952c3e66af40434c238ef040140d552669e86792e5d*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15903\t0.16\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: f925d533c2ef7f4e443419117d4f2f5682af2c9f01000000, name=_ray_fit, pid=16284, memory used=0.04GB) was running was 3.59GB / 3.74GB (0.959133), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 64a4e1a2c4979952c3e66af40434c238ef040140d552669e86792e5d) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-64a4e1a2c4979952c3e66af40434c238ef040140d552669e86792e5d*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15903\t0.16\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=6.86%)\n",
      "2025-12-07 17:03:30,508\tERROR worker.py:420 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 25cd36ce2140794377bb2a412f71d69bf40378f501000000, name=_ray_fit, pid=16343, memory used=0.04GB) was running was 3.57GB / 3.74GB (0.955475), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 1ddda301bb59113a16511761e41aafa7c36a8f1a5985912a2fd62189) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-1ddda301bb59113a16511761e41aafa7c36a8f1a5985912a2fd62189*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15903\t0.17\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 25cd36ce2140794377bb2a412f71d69bf40378f501000000, name=_ray_fit, pid=16343, memory used=0.04GB) was running was 3.57GB / 3.74GB (0.955475), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 1ddda301bb59113a16511761e41aafa7c36a8f1a5985912a2fd62189) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-1ddda301bb59113a16511761e41aafa7c36a8f1a5985912a2fd62189*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "15903\t0.17\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tMemory not enough to fit 8 folds in parallel. Will train 4 folds in parallel instead (Estimated 13.56% memory usage per fold, 54.25%/80.00% total).\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=13.56%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: cb088ffea501fb3cf5d66d09e810c29c60fecad601000000, name=_ray_fit, pid=16372, memory used=0.05GB) was running was 3.56GB / 3.74GB (0.952489), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: daaaafdc23bfe09c5a2f32bb7e2e4d7faeb0095cfadc2d24e1098eae) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-daaaafdc23bfe09c5a2f32bb7e2e4d7faeb0095cfadc2d24e1098eae*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "16372\t0.05\tray::IDLE\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: cb088ffea501fb3cf5d66d09e810c29c60fecad601000000, name=_ray_fit, pid=16372, memory used=0.05GB) was running was 3.56GB / 3.74GB (0.952489), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: daaaafdc23bfe09c5a2f32bb7e2e4d7faeb0095cfadc2d24e1098eae) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-daaaafdc23bfe09c5a2f32bb7e2e4d7faeb0095cfadc2d24e1098eae*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "16372\t0.05\tray::IDLE\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "2025-12-07 17:03:31,791\tERROR worker.py:420 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=3.50%)\n",
      "\u001b[36mray::_ray_fit()\u001b[39m (pid=16431, ip=169.255.255.2)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 446, in _ray_fit\n",
      "    fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, time_limit=time_limit_fold, **resources, **kwargs_fold)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/tabular/models/xgboost/xgboost_model.py\", line 171, in _fit\n",
      "    metric_name=eval_metric_name,  # forces stopping_metric rather than arbitrary last metric\n",
      "                ^^^^^^^^^^^^^^^^\n",
      "UnboundLocalError: cannot access local variable 'eval_metric_name' where it is not associated with a value\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 929, in get_objects\n",
      "    raise value.as_instanceof_cause()\n",
      "ray.exceptions.RayTaskError(UnboundLocalError): \u001b[36mray::_ray_fit()\u001b[39m (pid=16431, ip=169.255.255.2)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 446, in _ray_fit\n",
      "    fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, time_limit=time_limit_fold, **resources, **kwargs_fold)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/tabular/models/xgboost/xgboost_model.py\", line 171, in _fit\n",
      "    metric_name=eval_metric_name,  # forces stopping_metric rather than arbitrary last metric\n",
      "                ^^^^^^^^^^^^^^^^\n",
      "UnboundLocalError: cannot access local variable 'eval_metric_name' where it is not associated with a value\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=7.80%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 3fbd4cd97b370fb3f4a63f92316a53e0dd59589901000000, name=_ray_fit, pid=16587, memory used=0.05GB) was running was 3.56GB / 3.74GB (0.951935), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: dab471e945a936cfe7d8acecddda1c93270a2f804853aacd0dfd53bf) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-dab471e945a936cfe7d8acecddda1c93270a2f804853aacd0dfd53bf*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16431\t0.18\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "16587\t0.05\tray::IDLE\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 3fbd4cd97b370fb3f4a63f92316a53e0dd59589901000000, name=_ray_fit, pid=16587, memory used=0.05GB) was running was 3.56GB / 3.74GB (0.951935), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: dab471e945a936cfe7d8acecddda1c93270a2f804853aacd0dfd53bf) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-dab471e945a936cfe7d8acecddda1c93270a2f804853aacd0dfd53bf*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16431\t0.18\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "16587\t0.05\tray::IDLE\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=6.01%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: ce78ead395b0e554d4b2f31c625755402bf1361201000000, name=_ray_fit, pid=16644, memory used=0.05GB) was running was 3.56GB / 3.74GB (0.952443), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 701275ad1608f87eb2eac8b0825828c40036c7f99884c716c36ba8f4) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-701275ad1608f87eb2eac8b0825828c40036c7f99884c716c36ba8f4*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16431\t0.18\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "16644\t0.05\tray::IDLE\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: ce78ead395b0e554d4b2f31c625755402bf1361201000000, name=_ray_fit, pid=16644, memory used=0.05GB) was running was 3.56GB / 3.74GB (0.952443), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 701275ad1608f87eb2eac8b0825828c40036c7f99884c716c36ba8f4) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-701275ad1608f87eb2eac8b0825828c40036c7f99884c716c36ba8f4*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16431\t0.18\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "16644\t0.05\tray::IDLE\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=9.43%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 2535b75370fd2329353c70f61bd2bd87e30f90ee01000000, name=_ray_fit, pid=16674, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.95471), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: eee00d67f3da7f7ac89e7be2bdd0948572c809d3785ef0fa1a3b84b4) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-eee00d67f3da7f7ac89e7be2bdd0948572c809d3785ef0fa1a3b84b4*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16431\t0.17\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 2535b75370fd2329353c70f61bd2bd87e30f90ee01000000, name=_ray_fit, pid=16674, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.95471), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: eee00d67f3da7f7ac89e7be2bdd0948572c809d3785ef0fa1a3b84b4) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-eee00d67f3da7f7ac89e7be2bdd0948572c809d3785ef0fa1a3b84b4*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16431\t0.17\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=9.16%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: f7001d3cb936c7908d1cbcbb88ee8ebc8689bf1801000000, name=_ray_fit, pid=16703, memory used=0.05GB) was running was 3.56GB / 3.74GB (0.951895), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: e58f22db235b28d773f9fa22eb2497dab6fdcb14834408e1cc062140) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-e58f22db235b28d773f9fa22eb2497dab6fdcb14834408e1cc062140*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16431\t0.18\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: f7001d3cb936c7908d1cbcbb88ee8ebc8689bf1801000000, name=_ray_fit, pid=16703, memory used=0.05GB) was running was 3.56GB / 3.74GB (0.951895), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: e58f22db235b28d773f9fa22eb2497dab6fdcb14834408e1cc062140) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-e58f22db235b28d773f9fa22eb2497dab6fdcb14834408e1cc062140*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16431\t0.18\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=6.16%)\n",
      "\u001b[36mray::_ray_fit()\u001b[39m (pid=16733, ip=169.255.255.2)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 446, in _ray_fit\n",
      "    fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, time_limit=time_limit_fold, **resources, **kwargs_fold)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/tabular/models/xgboost/xgboost_model.py\", line 171, in _fit\n",
      "    metric_name=eval_metric_name,  # forces stopping_metric rather than arbitrary last metric\n",
      "                ^^^^^^^^^^^^^^^^\n",
      "UnboundLocalError: cannot access local variable 'eval_metric_name' where it is not associated with a value\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 929, in get_objects\n",
      "    raise value.as_instanceof_cause()\n",
      "ray.exceptions.RayTaskError(UnboundLocalError): \u001b[36mray::_ray_fit()\u001b[39m (pid=16733, ip=169.255.255.2)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 446, in _ray_fit\n",
      "    fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, time_limit=time_limit_fold, **resources, **kwargs_fold)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/tabular/models/xgboost/xgboost_model.py\", line 171, in _fit\n",
      "    metric_name=eval_metric_name,  # forces stopping_metric rather than arbitrary last metric\n",
      "                ^^^^^^^^^^^^^^^^\n",
      "UnboundLocalError: cannot access local variable 'eval_metric_name' where it is not associated with a value\n",
      "2025-12-07 17:03:48,347\tERROR worker.py:420 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=4.75%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 55a1b352e4094e8ec43326ee322b6c94935ebf4701000000, name=_ray_fit, pid=16888, memory used=0.04GB) was running was 3.56GB / 3.74GB (0.953485), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: b7bfe74f648eeebab68ac500c142599211dc5699f9e3ce6644536dcc) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-b7bfe74f648eeebab68ac500c142599211dc5699f9e3ce6644536dcc*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16733\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 55a1b352e4094e8ec43326ee322b6c94935ebf4701000000, name=_ray_fit, pid=16888, memory used=0.04GB) was running was 3.56GB / 3.74GB (0.953485), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: b7bfe74f648eeebab68ac500c142599211dc5699f9e3ce6644536dcc) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-b7bfe74f648eeebab68ac500c142599211dc5699f9e3ce6644536dcc*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16733\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=6.76%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: f83dd6cdcf4ffd1c356b710ba0c134b6a1aba65e01000000, name=_ray_fit, pid=16917, memory used=0.04GB) was running was 3.56GB / 3.74GB (0.953573), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 1cbcdbd339f5d4dbc9a13275ed19f7b9bea36afd57e3a6c296b2e031) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-1cbcdbd339f5d4dbc9a13275ed19f7b9bea36afd57e3a6c296b2e031*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16733\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: f83dd6cdcf4ffd1c356b710ba0c134b6a1aba65e01000000, name=_ray_fit, pid=16917, memory used=0.04GB) was running was 3.56GB / 3.74GB (0.953573), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 1cbcdbd339f5d4dbc9a13275ed19f7b9bea36afd57e3a6c296b2e031) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-1cbcdbd339f5d4dbc9a13275ed19f7b9bea36afd57e3a6c296b2e031*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16733\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=6.62%)\n",
      "2025-12-07 17:03:51,299\tERROR worker.py:420 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: ccf354814338d5e961a4bb99808d2b189a53ceaa01000000, name=_ray_fit, pid=16975, memory used=0.05GB) was running was 3.56GB / 3.74GB (0.953634), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 59b145d44d628e474cf03a26c81779768a33ee81c80c401b0f2a6d09) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-59b145d44d628e474cf03a26c81779768a33ee81c80c401b0f2a6d09*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16733\t0.18\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: ccf354814338d5e961a4bb99808d2b189a53ceaa01000000, name=_ray_fit, pid=16975, memory used=0.05GB) was running was 3.56GB / 3.74GB (0.953634), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 59b145d44d628e474cf03a26c81779768a33ee81c80c401b0f2a6d09) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-59b145d44d628e474cf03a26c81779768a33ee81c80c401b0f2a6d09*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16733\t0.18\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=8.41%)\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 6831a44014faa7555be886d1dcb212b373b0dbd301000000, name=_ray_fit, pid=17035, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.954824), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: d158e86c9cd261ee3682455360ae46123bfe6e78ea4ffe39aca630dd) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-d158e86c9cd261ee3682455360ae46123bfe6e78ea4ffe39aca630dd*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16733\t0.18\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 6831a44014faa7555be886d1dcb212b373b0dbd301000000, name=_ray_fit, pid=17035, memory used=0.05GB) was running was 3.57GB / 3.74GB (0.954824), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: d158e86c9cd261ee3682455360ae46123bfe6e78ea4ffe39aca630dd) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-d158e86c9cd261ee3682455360ae46123bfe6e78ea4ffe39aca630dd*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16733\t0.18\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tMemory not enough to fit 8 folds in parallel. Will train 4 folds in parallel instead (Estimated 18.22% memory usage per fold, 72.90%/80.00% total).\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=18.22%)\n",
      "2025-12-07 17:03:53,695\tERROR worker.py:420 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.\n",
      "Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 0e8f7f1b4fb7b4d63bc141e5b0438c4157b0816101000000, name=_ray_fit, pid=17094, memory used=0.05GB) was running was 3.56GB / 3.74GB (0.952727), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 233075896316e286c246e949ce37ca1504b37ca24ce5813d99b5ef65) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-233075896316e286c246e949ce37ca1504b37ca24ce5813d99b5ef65*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16733\t0.18\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: 0e8f7f1b4fb7b4d63bc141e5b0438c4157b0816101000000, name=_ray_fit, pid=17094, memory used=0.05GB) was running was 3.56GB / 3.74GB (0.952727), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 233075896316e286c246e949ce37ca1504b37ca24ce5813d99b5ef65) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-233075896316e286c246e949ce37ca1504b37ca24ce5813d99b5ef65*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "16733\t0.18\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=9.51%)\n",
      "\u001b[36mray::_ray_fit()\u001b[39m (pid=17123, ip=169.255.255.2)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 446, in _ray_fit\n",
      "    fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, time_limit=time_limit_fold, **resources, **kwargs_fold)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/tabular/models/xgboost/xgboost_model.py\", line 171, in _fit\n",
      "    metric_name=eval_metric_name,  # forces stopping_metric rather than arbitrary last metric\n",
      "                ^^^^^^^^^^^^^^^^\n",
      "UnboundLocalError: cannot access local variable 'eval_metric_name' where it is not associated with a value\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 37, in model_trial\n",
      "    model = fit_and_save_model(\n",
      "            ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/model_trial.py\", line 96, in fit_and_save_model\n",
      "    model.fit(**fit_args, time_limit=time_left)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 929, in get_objects\n",
      "    raise value.as_instanceof_cause()\n",
      "ray.exceptions.RayTaskError(UnboundLocalError): \u001b[36mray::_ray_fit()\u001b[39m (pid=17123, ip=169.255.255.2)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 446, in _ray_fit\n",
      "    fold_model.fit(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, time_limit=time_limit_fold, **resources, **kwargs_fold)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/tabular/models/xgboost/xgboost_model.py\", line 171, in _fit\n",
      "    metric_name=eval_metric_name,  # forces stopping_metric rather than arbitrary last metric\n",
      "                ^^^^^^^^^^^^^^^^\n",
      "UnboundLocalError: cannot access local variable 'eval_metric_name' where it is not associated with a value\n",
      "No model was trained during hyperparameter tuning XGBoost_BAG_L1... Skipping this model.\n",
      "Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 107.79s of the 434.83s of remaining time.\n",
      "\tMemory not enough to fit 8 folds in parallel. Will train 4 folds in parallel instead (Estimated 12.64% memory usage per fold, 50.54%/80.00% total).\n",
      "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy (2 workers, per: cpus=1, gpus=0, memory=12.64%)\n",
      "\tWarning: Exception caused LightGBMLarge_BAG_L1 to fail during training... Skipping this model.\n",
      "\t\tTask was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: e441517bc2c51dbbbeb1ee01af62857f0be0d38801000000, name=_ray_fit, pid=17284, memory used=0.04GB) was running was 3.57GB / 3.74GB (0.954301), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 7c3f4ed071d491838e78324d106d75c128eb99d73ee007b9083041ea) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-7c3f4ed071d491838e78324d106d75c128eb99d73ee007b9083041ea*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "17123\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Detailed Traceback:\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/tabular/trainer/abstract_trainer.py\", line 2171, in _train_and_save\n",
      "    model = self._train_single(**model_fit_kwargs)\n",
      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/tabular/trainer/abstract_trainer.py\", line 2055, in _train_single\n",
      "    model = model.fit(X=X, y=y, X_val=X_val, y_val=y_val, X_test=X_test, y_test=y_test, total_resources=total_resources, **model_fit_kwargs)\n",
      "            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/abstract/abstract_model.py\", line 1068, in fit\n",
      "    out = self._fit(**kwargs)\n",
      "          ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py\", line 270, in _fit\n",
      "    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 389, in _fit\n",
      "    self._fit_folds(\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py\", line 868, in _fit_folds\n",
      "    fold_fitting_strategy.after_all_folds_scheduled()\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 723, in after_all_folds_scheduled\n",
      "    self._run_parallel(X, y, X_pseudo, y_pseudo, model_base_ref, time_limit_fold, head_node_id)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 664, in _run_parallel\n",
      "    self._process_fold_results(finished, unfinished, fold_ctx)\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 620, in _process_fold_results\n",
      "    raise processed_exception\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py\", line 583, in _process_fold_results\n",
      "    fold_model, pred_proba, time_start_fit, time_end_fit, predict_time, predict_1_time, predict_n_size, fit_num_cpus, fit_num_gpus = self.ray.get(finished)\n",
      "                                                                                                                                     ^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/auto_init_hook.py\", line 21, in auto_init_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/client_mode_hook.py\", line 103, in wrapper\n",
      "    return func(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 2782, in get\n",
      "    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)\n",
      "                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/conda/lib/python3.12/site-packages/ray/_private/worker.py\", line 931, in get_objects\n",
      "    raise value\n",
      "ray.exceptions.OutOfMemoryError: Task was killed due to the node running low on memory.\n",
      "Memory on the node (IP: 169.255.255.2, ID: a44417bdf770811fdb553856390337ca80ba1b487a957487fa0ae87d) where the task (task ID: e441517bc2c51dbbbeb1ee01af62857f0be0d38801000000, name=_ray_fit, pid=17284, memory used=0.04GB) was running was 3.57GB / 3.74GB (0.954301), which exceeds the memory usage threshold of 0.95. Ray killed this worker (ID: 7c3f4ed071d491838e78324d106d75c128eb99d73ee007b9083041ea) because it was the most recently scheduled task; to see more information about memory usage on this node, use `ray logs raylet.out -ip 169.255.255.2`. To see the logs of the worker, use `ray logs worker-7c3f4ed071d491838e78324d106d75c128eb99d73ee007b9083041ea*out -ip 169.255.255.2. Top 10 memory users:\n",
      "PID\tMEM(GB)\tCOMMAND\n",
      "609\t1.25\t/opt/conda/bin/python -m ipykernel_launcher -f /home/sagemaker-user/.local/share/jupyter/runtime/ker...\n",
      "82\t0.39\t/opt/conda/bin/python3.12 /opt/conda/bin/jupyter-lab --ip 0.0.0.0 --port 8888 --ServerApp.base_url=/...\n",
      "624\t0.32\t/opt/conda/bin/python3.12 -m pylsp\n",
      "17123\t0.19\tray::IDLE\n",
      "1864\t0.08\t/opt/conda/bin/python /opt/conda/lib/python3.12/site-packages/ray/dashboard/dashboard.py --host=127....\n",
      "1985\t0.07\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/dashboard/agent.py --node-ip-ad...\n",
      "12449\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "12803\t0.06\t/opt/conda/bin/python3.12 /opt/conda/lib/python3.12/site-packages/jedi/inference/compiled/subprocess...\n",
      "1863\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/autoscaler/_private/monitor.py ...\n",
      "1941\t0.05\t/opt/conda/bin/python -u /opt/conda/lib/python3.12/site-packages/ray/_private/log_monitor.py --sessi...\n",
      "Refer to the documentation on how to address the out of memory issue: https://docs.ray.io/en/latest/ray-core/scheduling/ray-oom-prevention.html. Consider provisioning more memory on this node or reducing task parallelism by requesting more CPUs per task. To adjust the kill threshold, set the environment variable `RAY_memory_usage_threshold` when starting Ray. To disable worker killing, set the environment variable `RAY_memory_monitor_refresh_ms` to zero.\n",
      "Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.00s of the 432.34s of remaining time.\n",
      "\tEnsemble Weights: {'ExtraTreesMSE_BAG_L1': 1.0}\n",
      "\t-39.9538\t = Validation score   (-root_mean_squared_error)\n",
      "\t0.01s\t = Training   runtime\n",
      "\t0.0s\t = Validation runtime\n",
      "AutoGluon training complete, total runtime = 167.69s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 85341.6 rows/s (10886 batch size)\n",
      "Automatically performing refit_full as a post-fit operation (due to `.fit(..., refit_full=True)`\n",
      "Refitting models via `predictor.refit_full` using all of the data (combined train and validation)...\n",
      "\tModels trained in this way will have the suffix \"_FULL\" and have NaN validation score.\n",
      "\tThis process is not bound by time_limit, but should take less time than the original `predictor.fit` call.\n",
      "\tTo learn more, refer to the `.refit_full` method docstring which explains how \"_FULL\" models differ from normal models.\n",
      "Fitting model: ExtraTreesMSE_BAG_L1_FULL | Skipping fit via cloning parent ...\n",
      "\t2.18s\t = Training   runtime\n",
      "\t0.13s\t = Validation runtime\n",
      "Refit complete, total runtime = 0.3s ... Best model: \"WeightedEnsemble_L2\"\n",
      "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"/home/sagemaker-user/udacity-AWS-ml-engineer-nanodegree/project/AutogluonModels/ag-20251207_170115\")\n"
     ]
    }
   ],
   "source": [
    "predictor_new_hpo = TabularPredictor(label='count', problem_type='regression', eval_metric='root_mean_squared_error',\n",
    "                                    learner_kwargs={'ignored_columns': ignored_columns}).fit(\n",
    "                                                                         train_data=train, \n",
    "                                                                         time_limit=time_limit,\n",
    "                                                                         presets=presets, \n",
    "                                                                         hyperparameters=hyperparameters, \n",
    "                                                                         hyperparameter_tune_kwargs=hyperparameter_tune_kwargs,\n",
    "                                                                         excluded_model_types=excluded_model_types,\n",
    "                                                                         refit_full='best',\n",
    "                                                                                           dynamic_stacking=False,  # Turn off the heavy memory check\n",
    "                                                                                           num_stack_levels=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:04:05.513869Z",
     "iopub.status.busy": "2025-12-07T17:04:05.513510Z",
     "iopub.status.idle": "2025-12-07T17:04:05.625126Z",
     "shell.execute_reply": "2025-12-07T17:04:05.624467Z",
     "shell.execute_reply.started": "2025-12-07T17:04:05.513846Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Summary of fit() ***\n",
      "Estimated performance of each model:\n",
      "                       model  score_val              eval_metric  pred_time_val  fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
      "0       ExtraTreesMSE_BAG_L1 -39.953761  root_mean_squared_error       0.126752  2.411394                0.126752           2.411394            1       True          1\n",
      "1        WeightedEnsemble_L2 -39.953761  root_mean_squared_error       0.127558  2.418852                0.000806           0.007458            2       True          2\n",
      "2  ExtraTreesMSE_BAG_L1_FULL        NaN  root_mean_squared_error       0.126752  2.180187                0.126752           2.180187            1       True          3\n",
      "Number of models trained: 3\n",
      "Types of models trained:\n",
      "{'WeightedEnsembleModel', 'StackerEnsembleModel_XT'}\n",
      "Bagging used: True  (with 8 folds)\n",
      "Multi-layer stack-ensembling used: False \n",
      "Feature Metadata (Processed):\n",
      "(raw dtype, special dtypes):\n",
      "('category', [])             : 2 | ['season', 'weather']\n",
      "('float', [])                : 3 | ['temp', 'atemp', 'windspeed']\n",
      "('int', [])                  : 4 | ['humidity', 'month', 'day', 'hour']\n",
      "('int', ['bool'])            : 3 | ['holiday', 'workingday', 'year']\n",
      "('int', ['datetime_as_int']) : 3 | ['datetime', 'datetime.year', 'datetime.dayofweek']\n",
      "Plot summary of models saved to file: /home/sagemaker-user/udacity-AWS-ml-engineer-nanodegree/project/AutogluonModels/ag-20251207_170115/SummaryOfModels.html\n",
      "*** End of fit() summary ***\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'model_types': {'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',\n",
       "  'WeightedEnsemble_L2': 'WeightedEnsembleModel',\n",
       "  'ExtraTreesMSE_BAG_L1_FULL': 'StackerEnsembleModel_XT'},\n",
       " 'model_performance': {'ExtraTreesMSE_BAG_L1': -39.953761435872615,\n",
       "  'WeightedEnsemble_L2': -39.953761435872615,\n",
       "  'ExtraTreesMSE_BAG_L1_FULL': None},\n",
       " 'model_best': 'WeightedEnsemble_L2',\n",
       " 'model_paths': {'ExtraTreesMSE_BAG_L1': ['ExtraTreesMSE_BAG_L1'],\n",
       "  'WeightedEnsemble_L2': ['WeightedEnsemble_L2'],\n",
       "  'ExtraTreesMSE_BAG_L1_FULL': ['ExtraTreesMSE_BAG_L1_FULL']},\n",
       " 'model_fit_times': {'ExtraTreesMSE_BAG_L1': 2.4113941192626953,\n",
       "  'WeightedEnsemble_L2': 0.0074579715728759766,\n",
       "  'ExtraTreesMSE_BAG_L1_FULL': 2.180187463760376},\n",
       " 'model_pred_times': {'ExtraTreesMSE_BAG_L1': 0.12675237655639648,\n",
       "  'WeightedEnsemble_L2': 0.0008056163787841797,\n",
       "  'ExtraTreesMSE_BAG_L1_FULL': 0.12675237655639648},\n",
       " 'num_bag_folds': 8,\n",
       " 'max_stack_level': 2,\n",
       " 'model_hyperparams': {'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None,\n",
       "   'use_child_oof': True},\n",
       "  'WeightedEnsemble_L2': {'use_orig_features': False,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None},\n",
       "  'ExtraTreesMSE_BAG_L1_FULL': {'use_orig_features': True,\n",
       "   'valid_stacker': True,\n",
       "   'max_base_models': 0,\n",
       "   'max_base_models_per_type': 'auto',\n",
       "   'save_bag_folds': True,\n",
       "   'stratify': 'auto',\n",
       "   'bin': 'auto',\n",
       "   'n_bins': None,\n",
       "   'use_child_oof': True}},\n",
       " 'leaderboard':                        model  score_val              eval_metric  \\\n",
       " 0       ExtraTreesMSE_BAG_L1 -39.953761  root_mean_squared_error   \n",
       " 1        WeightedEnsemble_L2 -39.953761  root_mean_squared_error   \n",
       " 2  ExtraTreesMSE_BAG_L1_FULL        NaN  root_mean_squared_error   \n",
       " \n",
       "    pred_time_val  fit_time  pred_time_val_marginal  fit_time_marginal  \\\n",
       " 0       0.126752  2.411394                0.126752           2.411394   \n",
       " 1       0.127558  2.418852                0.000806           0.007458   \n",
       " 2       0.126752  2.180187                0.126752           2.180187   \n",
       " \n",
       "    stack_level  can_infer  fit_order  \n",
       " 0            1       True          1  \n",
       " 1            2       True          2  \n",
       " 2            1       True          3  }"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor_new_hpo.fit_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:57:36.293270Z",
     "iopub.status.busy": "2025-12-07T16:57:36.292997Z",
     "iopub.status.idle": "2025-12-07T16:57:36.301313Z",
     "shell.execute_reply": "2025-12-07T16:57:36.300357Z",
     "shell.execute_reply.started": "2025-12-07T16:57:36.293248Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "❌ ERROR: The following columns are duplicated in your training data:\n",
      "['year', 'month', 'day', 'hour']\n"
     ]
    }
   ],
   "source": [
    "# Assuming your DataFrame is named 'train'\n",
    "duplicated_columns = train.columns[train.columns.duplicated()]\n",
    "\n",
    "if not duplicated_columns.empty:\n",
    "    print(\"❌ ERROR: The following columns are duplicated in your training data:\")\n",
    "    print(duplicated_columns.tolist())\n",
    "else:\n",
    "    print(\"✅ No duplicated columns found. (The error must stem from another manipulation.)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T16:58:47.120473Z",
     "iopub.status.busy": "2025-12-07T16:58:47.120059Z",
     "iopub.status.idle": "2025-12-07T16:58:47.126142Z",
     "shell.execute_reply": "2025-12-07T16:58:47.125070Z",
     "shell.execute_reply.started": "2025-12-07T16:58:47.120443Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated columns renamed successfully. New columns: ['datetime', 'season', 'holiday', 'workingday', 'weather', 'temp', 'atemp', 'humidity', 'windspeed', 'casual', 'registered', 'count', 'year', 'month', 'day', 'hour', 'year_1', 'month_1', 'day_1', 'hour_1']\n"
     ]
    }
   ],
   "source": [
    "cols = train.columns\n",
    "seen_names = {}\n",
    "new_cols = []\n",
    "\n",
    "# Iterate through the columns and append a counter suffix if the name is already seen\n",
    "for col in cols:\n",
    "    if col in seen_names:\n",
    "        seen_names[col] += 1\n",
    "        new_cols.append(f\"{col}_{seen_names[col]}\")\n",
    "    else:\n",
    "        seen_names[col] = 0\n",
    "        new_cols.append(col)\n",
    "\n",
    "train.columns = new_cols\n",
    "print(\"Duplicated columns renamed successfully. New columns:\", train.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:27:22.311236Z",
     "iopub.status.busy": "2025-12-07T17:27:22.310943Z",
     "iopub.status.idle": "2025-12-07T17:27:22.316500Z",
     "shell.execute_reply": "2025-12-07T17:27:22.315886Z",
     "shell.execute_reply.started": "2025-12-07T17:27:22.311203Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated columns renamed successfully. New columns: ['datetime', 'season', 'holiday', 'workingday', 'weather', 'temp', 'atemp', 'humidity', 'windspeed', 'year', 'month', 'day', 'hour', 'year_1', 'month_1', 'day_1', 'hour_1']\n"
     ]
    }
   ],
   "source": [
    "cols = test.columns\n",
    "seen_names = {}\n",
    "new_cols = []\n",
    "\n",
    "# Iterate through the columns and append a counter suffix if the name is already seen\n",
    "for col in cols:\n",
    "    if col in seen_names:\n",
    "        seen_names[col] += 1\n",
    "        new_cols.append(f\"{col}_{seen_names[col]}\")\n",
    "    else:\n",
    "        seen_names[col] = 0\n",
    "        new_cols.append(col)\n",
    "\n",
    "test.columns = new_cols\n",
    "print(\"Duplicated columns renamed successfully. New columns:\", test.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:04:05.871159Z",
     "iopub.status.busy": "2025-12-07T17:04:05.870869Z",
     "iopub.status.idle": "2025-12-07T17:04:05.893741Z",
     "shell.execute_reply": "2025-12-07T17:04:05.893081Z",
     "shell.execute_reply.started": "2025-12-07T17:04:05.871135Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>score_val</th>\n",
       "      <th>eval_metric</th>\n",
       "      <th>pred_time_val</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>pred_time_val_marginal</th>\n",
       "      <th>fit_time_marginal</th>\n",
       "      <th>stack_level</th>\n",
       "      <th>can_infer</th>\n",
       "      <th>fit_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ExtraTreesMSE_BAG_L1</td>\n",
       "      <td>-39.953761</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>0.126752</td>\n",
       "      <td>2.411394</td>\n",
       "      <td>0.126752</td>\n",
       "      <td>2.411394</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WeightedEnsemble_L2</td>\n",
       "      <td>-39.953761</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>0.127558</td>\n",
       "      <td>2.418852</td>\n",
       "      <td>0.000806</td>\n",
       "      <td>0.007458</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ExtraTreesMSE_BAG_L1_FULL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>root_mean_squared_error</td>\n",
       "      <td>0.126752</td>\n",
       "      <td>2.180187</td>\n",
       "      <td>0.126752</td>\n",
       "      <td>2.180187</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       model  score_val              eval_metric  \\\n",
       "0       ExtraTreesMSE_BAG_L1 -39.953761  root_mean_squared_error   \n",
       "1        WeightedEnsemble_L2 -39.953761  root_mean_squared_error   \n",
       "2  ExtraTreesMSE_BAG_L1_FULL        NaN  root_mean_squared_error   \n",
       "\n",
       "   pred_time_val  fit_time  pred_time_val_marginal  fit_time_marginal  \\\n",
       "0       0.126752  2.411394                0.126752           2.411394   \n",
       "1       0.127558  2.418852                0.000806           0.007458   \n",
       "2       0.126752  2.180187                0.126752           2.180187   \n",
       "\n",
       "   stack_level  can_infer  fit_order  \n",
       "0            1       True          1  \n",
       "1            2       True          2  \n",
       "2            1       True          3  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leaderboard dataframe\n",
    "leaderboard_new_hpo1_df = pd.DataFrame(predictor_new_hpo.leaderboard(silent=True))\n",
    "leaderboard_new_hpo1_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:19:13.098824Z",
     "iopub.status.busy": "2025-12-07T17:19:13.098539Z",
     "iopub.status.idle": "2025-12-07T17:19:13.296870Z",
     "shell.execute_reply": "2025-12-07T17:19:13.296024Z",
     "shell.execute_reply.started": "2025-12-07T17:19:13.098803Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming 'leaderboard_df' is your DataFrame\n",
    "models = leaderboard_new_hpo1_df['model']\n",
    "scores = leaderboard_new_hpo1_df['score_val']\n",
    "\n",
    "# Create a figure and an axis\n",
    "fig, ax = plt.subplots(figsize=(14, 7))\n",
    "\n",
    "# Plot the bars using absolute scores and 'lightsalmon' color\n",
    "# We use abs() to ensure the bars plot upwards from 0\n",
    "ax.bar(\n",
    "    models, \n",
    "    scores.abs(),  \n",
    "    color='lightsalmon'\n",
    ")\n",
    "\n",
    "# Set X-axis labels and rotate them for readability\n",
    "ax.set_xlabel(\"Model\")\n",
    "ax.set_ylabel(\"Absolute RMSE Scores\")\n",
    "ax.set_title(\"New Hyperparameter Performance Comparison (Absolute RMSE)\")\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "ax.legend(['score_val']) \n",
    "\n",
    "plt.tight_layout() # Adjust layout to prevent labels from overlapping\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:17:24.221501Z",
     "iopub.status.busy": "2025-12-07T17:17:24.221212Z",
     "iopub.status.idle": "2025-12-07T17:17:24.226620Z",
     "shell.execute_reply": "2025-12-07T17:17:24.225765Z",
     "shell.execute_reply.started": "2025-12-07T17:17:24.221480Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Successfully Retrieved Expected Input Features (Original Columns):\n",
      "['holiday', 'workingday', 'temp', 'atemp', 'humidity', 'windspeed', 'year', 'month', 'day', 'hour', 'season', 'weather', 'datetime', 'datetime.year', 'datetime.dayofweek']\n"
     ]
    }
   ],
   "source": [
    "from autogluon.tabular import TabularPredictor\n",
    "\n",
    "# after fitting predictor_new_hpo\n",
    "feature_metadata = predictor_new_hpo.feature_metadata\n",
    "original_features = feature_metadata.get_features()\n",
    "\n",
    "print(\"✅ Successfully Retrieved Expected Input Features (Original Columns):\")\n",
    "print(original_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:29:18.835650Z",
     "iopub.status.busy": "2025-12-07T17:29:18.835360Z",
     "iopub.status.idle": "2025-12-07T17:29:18.841195Z",
     "shell.execute_reply": "2025-12-07T17:29:18.840457Z",
     "shell.execute_reply.started": "2025-12-07T17:29:18.835630Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Columns:  ['datetime', 'season', 'holiday', 'workingday', 'weather', 'temp', 'atemp', 'humidity', 'windspeed', 'casual', 'registered', 'count', 'year', 'month', 'day', 'hour', 'year_1', 'month_1', 'day_1', 'hour_1']\n",
      "Test Columns:  ['datetime', 'season', 'holiday', 'workingday', 'weather', 'temp', 'atemp', 'humidity', 'windspeed', 'year', 'month', 'day', 'hour', 'year_1', 'month_1', 'day_1', 'hour_1']\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Columns: \",train.columns.tolist())\n",
    "print(\"Test Columns: \",test.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:30:39.848914Z",
     "iopub.status.busy": "2025-12-07T17:30:39.848600Z",
     "iopub.status.idle": "2025-12-07T17:30:39.868886Z",
     "shell.execute_reply": "2025-12-07T17:30:39.867781Z",
     "shell.execute_reply.started": "2025-12-07T17:30:39.848888Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data final columns: 15\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 1. Ensure 'datetime' is of datetime type (as done in the previous step)\n",
    "test['datetime'] = pd.to_datetime(test['datetime'])\n",
    "\n",
    "# 2. Manually engineer the features AG is expecting as raw input\n",
    "test['datetime.year'] = test['datetime'].dt.year\n",
    "test['datetime.dayofweek'] = test['datetime'].dt.dayofweek\n",
    "\n",
    "# 3. Define the final features list (the 15 columns AG expects)\n",
    "expected_features_to_keep = [\n",
    "    'holiday', 'workingday', 'temp', 'atemp', 'humidity', \n",
    "    'windspeed', 'year', 'month', 'day', 'hour', \n",
    "    'season', 'weather', 'datetime', 'datetime.year', 'datetime.dayofweek'\n",
    "]\n",
    "\n",
    "# 4. Filter the test data to include ONLY the 15 expected columns, in order\n",
    "test_final = test[expected_features_to_keep]\n",
    "\n",
    "print(f\"Test data final columns: {len(test_final.columns)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:30:50.708150Z",
     "iopub.status.busy": "2025-12-07T17:30:50.707795Z",
     "iopub.status.idle": "2025-12-07T17:30:51.006388Z",
     "shell.execute_reply": "2025-12-07T17:30:51.005587Z",
     "shell.execute_reply.started": "2025-12-07T17:30:50.708060Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Prediction successful! 🎉\n",
      "0    11.983606\n",
      "1     5.639344\n",
      "2     3.721312\n",
      "3     3.442623\n",
      "4     2.573771\n",
      "Name: count, dtype: float32\n"
     ]
    }
   ],
   "source": [
    "predictions_new_hpo = predictor_new_hpo.predict(test_final) \n",
    "\n",
    "print(\"\\nPrediction successful! 🎉\")\n",
    "print(predictions_new_hpo.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:31:11.382500Z",
     "iopub.status.busy": "2025-12-07T17:31:11.382208Z",
     "iopub.status.idle": "2025-12-07T17:31:11.390409Z",
     "shell.execute_reply": "2025-12-07T17:31:11.387677Z",
     "shell.execute_reply.started": "2025-12-07T17:31:11.382475Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of remaining negative predictions: 0\n",
      "All negative values have been successfully clipped to zero.\n"
     ]
    }
   ],
   "source": [
    "# Remember to set all negative values to zero\n",
    "# guardrails incase there are negative values\n",
    "predictions_new_hpo[predictions_new_hpo < 0] = 0\n",
    "\n",
    "# Check for any remaining negative values\n",
    "predictions_neg_count = (predictions_new_hpo < 0).sum()\n",
    "\n",
    "print(f\"Number of remaining negative predictions: {predictions_neg_count}\")\n",
    "print(\"All negative values have been successfully clipped to zero.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:31:47.444677Z",
     "iopub.status.busy": "2025-12-07T17:31:47.444397Z",
     "iopub.status.idle": "2025-12-07T17:31:47.486858Z",
     "shell.execute_reply": "2025-12-07T17:31:47.486040Z",
     "shell.execute_reply.started": "2025-12-07T17:31:47.444657Z"
    }
   },
   "outputs": [],
   "source": [
    "# Same submitting predictions\n",
    "submission_new_hpo = pd.read_csv('sampleSubmission.csv', parse_dates = ['datetime'])\n",
    "submission_new_hpo[\"count\"] = predictions_new_hpo\n",
    "submission_new_hpo.to_csv(\"submission_new_hpo.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:31:50.902534Z",
     "iopub.status.busy": "2025-12-07T17:31:50.902239Z",
     "iopub.status.idle": "2025-12-07T17:31:53.265986Z",
     "shell.execute_reply": "2025-12-07T17:31:53.264959Z",
     "shell.execute_reply.started": "2025-12-07T17:31:50.902510Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 188k/188k [00:00<00:00, 688kB/s]\n",
      "Successfully submitted to Bike Sharing Demand"
     ]
    }
   ],
   "source": [
    "!kaggle competitions submit -c bike-sharing-demand -f submission_new_hpo.csv -m \"new features with hyperparameters\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:32:13.701575Z",
     "iopub.status.busy": "2025-12-07T17:32:13.701193Z",
     "iopub.status.idle": "2025-12-07T17:32:13.800565Z",
     "shell.execute_reply": "2025-12-07T17:32:13.799768Z",
     "shell.execute_reply.started": "2025-12-07T17:32:13.701549Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"_date\": \"2025-12-07 17:31:52.367000\",\n",
      "  \"_description\": \"new features with hyperparameters\",\n",
      "  \"_error_description\": null,\n",
      "  \"_file_name\": \"submission_new_hpo.csv\",\n",
      "  \"_is_frozen\": true,\n",
      "  \"_private_score\": \"0.49145\",\n",
      "  \"_public_score\": \"0.49145\",\n",
      "  \"_ref\": 48845897,\n",
      "  \"_status\": \"SubmissionStatus.COMPLETE\",\n",
      "  \"_submitted_by\": \"brejeshbalakrishnan\",\n",
      "  \"_submitted_by_ref\": \"brejeshbalakrishnan\",\n",
      "  \"_team_name\": \"Brejesh Balakrishnan\",\n",
      "  \"_total_bytes\": 192435,\n",
      "  \"_url\": \"/submissions/48845897/48845897.raw\"\n",
      "}\n",
      "----------------------------------------\n",
      "{\n",
      "  \"_date\": \"2025-12-07 16:48:56.703000\",\n",
      "  \"_description\": \"new features\",\n",
      "  \"_error_description\": null,\n",
      "  \"_file_name\": \"submission_new_features.csv\",\n",
      "  \"_is_frozen\": true,\n",
      "  \"_private_score\": \"1.41560\",\n",
      "  \"_public_score\": \"1.41560\",\n",
      "  \"_ref\": 48844886,\n",
      "  \"_status\": \"SubmissionStatus.COMPLETE\",\n",
      "  \"_submitted_by\": \"brejeshbalakrishnan\",\n",
      "  \"_submitted_by_ref\": \"brejeshbalakrishnan\",\n",
      "  \"_team_name\": \"Brejesh Balakrishnan\",\n",
      "  \"_total_bytes\": 192883,\n",
      "  \"_url\": \"/submissions/48844886/48844886.raw\"\n",
      "}\n",
      "----------------------------------------\n",
      "{\n",
      "  \"_date\": \"2025-12-07 16:45:44.443000\",\n",
      "  \"_description\": \"new features\",\n",
      "  \"_error_description\": null,\n",
      "  \"_file_name\": \"submission_new_features.csv\",\n",
      "  \"_is_frozen\": true,\n",
      "  \"_private_score\": \"1.41560\",\n",
      "  \"_public_score\": \"1.41560\",\n",
      "  \"_ref\": 48844797,\n",
      "  \"_status\": \"SubmissionStatus.COMPLETE\",\n",
      "  \"_submitted_by\": \"brejeshbalakrishnan\",\n",
      "  \"_submitted_by_ref\": \"brejeshbalakrishnan\",\n",
      "  \"_team_name\": \"Brejesh Balakrishnan\",\n",
      "  \"_total_bytes\": 192883,\n",
      "  \"_url\": \"/submissions/48844797/48844797.raw\"\n",
      "}\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from kaggle.api.kaggle_api_extended import KaggleApi\n",
    "\n",
    "api = KaggleApi()\n",
    "api.authenticate()\n",
    "subs = api.competition_submissions('bike-sharing-demand')  # no page_number\n",
    "import json\n",
    "\n",
    "for s in subs[0:3]:\n",
    "    print(json.dumps(vars(s), indent=2, sort_keys=True, default=str))\n",
    "    print(\"-\" * 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### New Score of `0.49145`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7: Write a Report\n",
    "### Refer to the markdown file for the full report\n",
    "### Creating plots and table for report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:33:47.974008Z",
     "iopub.status.busy": "2025-12-07T17:33:47.973698Z",
     "iopub.status.idle": "2025-12-07T17:33:48.285906Z",
     "shell.execute_reply": "2025-12-07T17:33:48.285038Z",
     "shell.execute_reply.started": "2025-12-07T17:33:47.973986Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Taking the top model score from each training run and creating a line plot to show improvement\n",
    "# You can create these in the notebook and save them to PNG or use some other tool (e.g. google sheets, excel)\n",
    "fig = pd.DataFrame(\n",
    "    {\n",
    "        \"model\": [\"initial\", \"add_features\", \"hpo\"],\n",
    "        \"score\": [predictor.leaderboard().loc[0,'score_val'], \n",
    "            predictor_new_features.leaderboard().loc[0,'score_val'], \n",
    "            predictor_new_hpo.leaderboard().loc[0,'score_val']]\n",
    "    }\n",
    ").plot(x=\"model\", y=\"score\", figsize=(8, 6)).get_figure()\n",
    "fig.savefig('model_train_score.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:35:29.866807Z",
     "iopub.status.busy": "2025-12-07T17:35:29.866506Z",
     "iopub.status.idle": "2025-12-07T17:35:30.082426Z",
     "shell.execute_reply": "2025-12-07T17:35:30.081751Z",
     "shell.execute_reply.started": "2025-12-07T17:35:29.866784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take the 3 kaggle scores and creating a line plot to show improvement\n",
    "fig = pd.DataFrame(\n",
    "    {\n",
    "        \"test_eval\": [\"initial\", \"add_features\", \"hpo\"],\n",
    "        \"score\": [1.42139, 1.4156, 0.49145]\n",
    "    }\n",
    ").plot(x=\"test_eval\", y=\"score\", figsize=(8, 6)).get_figure()\n",
    "fig.savefig('model_test_score.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-07T17:39:42.084696Z",
     "iopub.status.busy": "2025-12-07T17:39:42.084413Z",
     "iopub.status.idle": "2025-12-07T17:39:42.096743Z",
     "shell.execute_reply": "2025-12-07T17:39:42.095797Z",
     "shell.execute_reply.started": "2025-12-07T17:39:42.084675Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>XT</th>\n",
       "      <th>GBM</th>\n",
       "      <th>XGB</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>initial</td>\n",
       "      <td>default</td>\n",
       "      <td>default</td>\n",
       "      <td>default</td>\n",
       "      <td>1.42139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>add_features</td>\n",
       "      <td>default</td>\n",
       "      <td>default</td>\n",
       "      <td>default</td>\n",
       "      <td>1.41560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hpo</td>\n",
       "      <td>{'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression']}}</td>\n",
       "      <td>[{'extra_trees': True, 'num_boost_round': Int: lower=100, upper=800, 'num_leaves': Int: lower=26, upper=66, 'ag_args': {'name_suffix': 'XT'}}, {}, {'learning_rate': 0.03, 'num_leaves': 128, 'feature_fraction': 0.9, 'min_data_in_leaf': 3, 'ag_args': {'name_suffix': 'Large', 'priority': 0, 'hyperparameter_tune_kwargs': None}}]</td>\n",
       "      <td>[{'objective': 'reg:squarederror', 'eval_metric': 'rmse', 'max_depth': Int: lower=5, upper=8, 'n_estimators': Int: lower=100, upper=500, 'eta': 0.3, 'subsample': 1, 'colsample_bytree': 1}]</td>\n",
       "      <td>0.49145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          model  \\\n",
       "0       initial   \n",
       "1  add_features   \n",
       "2           hpo   \n",
       "\n",
       "                                                                                                   XT  \\\n",
       "0                                                                                             default   \n",
       "1                                                                                             default   \n",
       "2  {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression']}}   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                      GBM  \\\n",
       "0                                                                                                                                                                                                                                                                                                                                 default   \n",
       "1                                                                                                                                                                                                                                                                                                                                 default   \n",
       "2  [{'extra_trees': True, 'num_boost_round': Int: lower=100, upper=800, 'num_leaves': Int: lower=26, upper=66, 'ag_args': {'name_suffix': 'XT'}}, {}, {'learning_rate': 0.03, 'num_leaves': 128, 'feature_fraction': 0.9, 'min_data_in_leaf': 3, 'ag_args': {'name_suffix': 'Large', 'priority': 0, 'hyperparameter_tune_kwargs': None}}]   \n",
       "\n",
       "                                                                                                                                                                                            XGB  \\\n",
       "0                                                                                                                                                                                       default   \n",
       "1                                                                                                                                                                                       default   \n",
       "2  [{'objective': 'reg:squarederror', 'eval_metric': 'rmse', 'max_depth': Int: lower=5, upper=8, 'n_estimators': Int: lower=100, upper=500, 'eta': 0.3, 'subsample': 1, 'colsample_bytree': 1}]   \n",
       "\n",
       "     score  \n",
       "0  1.42139  \n",
       "1  1.41560  \n",
       "2  0.49145  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The 3 hyperparameters we tuned with the kaggle score as the result\n",
    "pd.DataFrame({\n",
    "    \"model\": [\"initial\", \"add_features\", \"hpo\"],\n",
    "    \"XT\": ['default', 'default', xt_options ],\n",
    "    \"GBM\": ['default', 'default', gbm_options],\n",
    "    \"XGB\": ['default', 'default', xgb_options ],\n",
    "    \"score\": [1.42139, 1.4156, 0.49145]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "The best model was **ExtraTreesMSE_BAG_L1** obtained during the hyperparameter tuning with a RMSE score of **39.953761** and the best kaggle score of **0.49145**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}