CoCalc -- DoubleDescent_Overfitting.ipynb~

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"Guiding Future STEM Leaders through Innovative Research Training" ~ thinkingbeyond.education
Path: ThinkingBeyond Activities / BeyondAI-2024-Mentee-Projects / shaurya / DoubleDescent_Overfitting.ipynb~
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Image: ubuntu2204
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a8100ea4-bae7-42fd-8537-df63ad47a1bd",
   "metadata": {},
   "source": [
    "# Downloading and importing important librarires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6824a5c2-17f4-4d3a-8c86-e231e7763e6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: torchvision==0.20.1 in c:\\users\\saura\\anaconda3\\lib\\site-packages (0.20.1)\n",
      "Requirement already satisfied: numpy in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torchvision==0.20.1) (1.26.4)\n",
      "Requirement already satisfied: torch==2.5.1 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torchvision==0.20.1) (2.5.1)\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torchvision==0.20.1) (10.3.0)\n",
      "Requirement already satisfied: filelock in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchvision==0.20.1) (3.13.1)\n",
      "Requirement already satisfied: typing-extensions>=4.8.0 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchvision==0.20.1) (4.9.0)\n",
      "Requirement already satisfied: networkx in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchvision==0.20.1) (3.1)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchvision==0.20.1) (3.1.3)\n",
      "Requirement already satisfied: fsspec in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchvision==0.20.1) (2023.10.0)\n",
      "Requirement already satisfied: sympy==1.13.1 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchvision==0.20.1) (1.13.1)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from sympy==1.13.1->torch==2.5.1->torchvision==0.20.1) (1.3.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from jinja2->torch==2.5.1->torchvision==0.20.1) (2.1.3)\n",
      "Requirement already satisfied: torchaudio==2.5.1 in c:\\users\\saura\\anaconda3\\lib\\site-packages (2.5.1)\n",
      "Requirement already satisfied: torch==2.5.1 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torchaudio==2.5.1) (2.5.1)\n",
      "Requirement already satisfied: filelock in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchaudio==2.5.1) (3.13.1)\n",
      "Requirement already satisfied: typing-extensions>=4.8.0 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchaudio==2.5.1) (4.9.0)\n",
      "Requirement already satisfied: networkx in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchaudio==2.5.1) (3.1)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchaudio==2.5.1) (3.1.3)\n",
      "Requirement already satisfied: fsspec in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchaudio==2.5.1) (2023.10.0)\n",
      "Requirement already satisfied: sympy==1.13.1 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchaudio==2.5.1) (1.13.1)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from sympy==1.13.1->torch==2.5.1->torchaudio==2.5.1) (1.3.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from jinja2->torch==2.5.1->torchaudio==2.5.1) (2.1.3)\n",
      "Requirement already satisfied: torchdata==0.10.1 in c:\\users\\saura\\anaconda3\\lib\\site-packages (0.10.1)\n",
      "Requirement already satisfied: urllib3>=1.25 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torchdata==0.10.1) (2.0.7)\n",
      "Requirement already satisfied: requests in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torchdata==0.10.1) (2.32.3)\n",
      "Requirement already satisfied: torch>=2 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torchdata==0.10.1) (2.5.1)\n",
      "Requirement already satisfied: filelock in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch>=2->torchdata==0.10.1) (3.13.1)\n",
      "Requirement already satisfied: typing-extensions>=4.8.0 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch>=2->torchdata==0.10.1) (4.9.0)\n",
      "Requirement already satisfied: networkx in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch>=2->torchdata==0.10.1) (3.1)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch>=2->torchdata==0.10.1) (3.1.3)\n",
      "Requirement already satisfied: fsspec in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch>=2->torchdata==0.10.1) (2023.10.0)\n",
      "Requirement already satisfied: sympy==1.13.1 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from torch>=2->torchdata==0.10.1) (1.13.1)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from sympy==1.13.1->torch>=2->torchdata==0.10.1) (1.3.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from requests->torchdata==0.10.1) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from requests->torchdata==0.10.1) (3.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from requests->torchdata==0.10.1) (2024.2.2)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\saura\\anaconda3\\lib\\site-packages (from jinja2->torch>=2->torchdata==0.10.1) (2.1.3)\n",
      "Requirement already satisfied: numpy==1.26.4 in c:\\users\\saura\\anaconda3\\lib\\site-packages (1.26.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install torchvision==0.20.1\n",
    "!pip install torchaudio==2.5.1\n",
    "!pip install torchdata==0.10.1\n",
    "!pip install numpy==1.26.4\n",
    "\n",
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import torchaudio\n",
    "import torchdata\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from torchvision import datasets, transforms\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "os.makedirs(\"./data\", exist_ok=True)\n",
    "\n",
    "\n",
    "import random\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5381c170-aece-406d-bd93-ebfef4905493",
   "metadata": {},
   "source": [
    "# Preparing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4cb87ba9-8f4a-4655-90eb-b220223c9f9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Preparation\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))\n",
    "])\n",
    "\n",
    "training_data = datasets.MNIST(\n",
    "    root=\"./data\",\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=transform\n",
    ")\n",
    "\n",
    "test_data = datasets.MNIST(\n",
    "    root=\"./data\",\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=transform\n",
    ")\n",
    "\n",
    "train_loader = DataLoader(training_data, batch_size=64, shuffle=True)\n",
    "test_loader = DataLoader(test_data, batch_size=64, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd19932a-e158-480b-9d69-c4694ba7dccc",
   "metadata": {},
   "source": [
    "# Flexible Neural Network class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "439ed1bc-86f4-4d1c-ad3b-81348c4f0371",
   "metadata": {},
   "outputs": [],
   "source": [
    "class FlexibleNN(nn.Module):\n",
    "    def __init__(self, hidden_layer_sizes):\n",
    "        super(FlexibleNN, self).__init__()\n",
    "        self.flatten = nn.Flatten()\n",
    "        layers = []\n",
    "        input_size = 28 * 28\n",
    "        for hidden_size in hidden_layer_sizes:\n",
    "            layers.append(nn.Linear(input_size, hidden_size))\n",
    "            layers.append(nn.ReLU())\n",
    "            input_size = hidden_size\n",
    "        layers.append(nn.Linear(input_size, 10))  # Output layer\n",
    "        self.network = nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.flatten(x)\n",
    "        return self.network(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb13492c-b253-4a78-bc9c-5cb2f9f83a27",
   "metadata": {},
   "source": [
    "# Data training and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "067c0940-cdc0-469a-b144-bc767785d52e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train and Evaluate Function\n",
    "def train_and_evaluate(hidden_layer_sizes):\n",
    "    model = FlexibleNN(hidden_layer_sizes)\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
    "\n",
    "    train_losses = []\n",
    "    test_losses = []\n",
    "\n",
    "    for epoch in range(10):  # Train for a few epochs\n",
    "        model.train()\n",
    "        train_loss = 0\n",
    "        for images, labels in train_loader:\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            train_loss += loss.item()\n",
    "        train_losses.append(train_loss / len(train_loader))\n",
    "\n",
    "        model.eval()\n",
    "        test_loss = 0\n",
    "        with torch.no_grad():\n",
    "            for images, labels in test_loader:\n",
    "                outputs = model(images)\n",
    "                loss = criterion(outputs, labels)\n",
    "                test_loss += loss.item()\n",
    "        test_losses.append(test_loss / len(test_loader))\n",
    "\n",
    "    return train_losses, test_losses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34ae3d07-b1a6-4dd2-b856-59c917589831",
   "metadata": {},
   "source": [
    "# Different Model Complexities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "71c17b53-2880-417f-86ca-4f39aefd7417",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#  Experiment with Different Model Complexities\n",
    "hidden_units = [16, 32, 64, 128, 256, 512, 1024, 2048]\n",
    "\n",
    "train_loss_all = []\n",
    "test_loss_all = []\n",
    "\n",
    "for hidden_size in hidden_units:\n",
    "    hidden_layer_sizes = [hidden_size, hidden_size, 16]\n",
    "    train_losses, test_losses = train_and_evaluate(hidden_layer_sizes)\n",
    "    train_loss_all.append(train_losses[-1])\n",
    "    test_loss_all.append(test_losses[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1ab0c21-7373-42f8-8108-d0ec1c3160bc",
   "metadata": {},
   "source": [
    "# Visualizing the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0410479f-45d6-45b6-a187-e91ccc7baa59",
   "metadata": {},
   "outputs": [
    {
     "data": {
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Sbj92bNLK5oGBabd1dadPS4cOSbduJT0iIqRcuZJWSX3gAalOHSkgwDm5xcVJO3Yk5XfjhhQTk/ReNW0qNWxonzFCQqStW5OK9UJDpaAgqXDhpFXuK1a0zxh3hYUlfXYvXkxaXTY8POk5xcZK2bJJ2bNL+fJJxYtLFSpI+fPbd3wrTp6Udu2Szp9P+kx4eCStrPvss2n3TUxMWv3/4kXp0qWk9y46OulhGJKvb9JnrGBBqWxZqXz5pDsPOMOhQ0mfscuXk3LLn1+qVi3p32a/k125knSXhNOnk57j3b+dxo2lPHkck7dhSEeOSCdO/P+/3ejopPHy5pWKFpVq1076e3FncXHS7t3ShQv//3lK//95li2b9LfqrO/PN24kXcx08mTS33d8fNIc0q+f4957V+Ps9+j69aQ5/NKlpH/7+0slS0r16iX9HWR2uXNLs2bJKFNGHvHxqbe9cMF+4968mfS+X78u3b6dtC0NCEh6z/PlS7q4r0AB+433b/v2Jc2Bly5JUVFJr0P58lKDBs7bZ7GXq1elPXuSXuNbt5L2TQIDk/6m8uSRKldOupDSWaKikrZ5R48mvfd+fknbzkqVkt53T9ZNAgAAAAAAWUtwcLDdY168eFGbNm3Svn37dOzYMYWGhiosLEzR0dEyUliI68qVK8n+/9y5c3bPKTUNGjSQjw3nI8qUKaMjR47c+393X9ne2Sg0BwAniYiNV2h0XIaOGRodp8g7CQr0ZfoHAABI0ezZ5tp9/725IvN/+vBDac2apMLT1Fy7Js2YIQ0enPznr74qTZyYet+9e6UHH7SW1wsvSN98k3qbQ4eSinzSYhjSsmXSrFnS+vVJRVqp8fZOKjZ/9FHp+eeTCoStCg6W1q5NO6+7Ll+W3n8/6TVO6aDSsGH/LTRPq4ixadOk9/au9euTxvjrr6Ti0JRUrSo984z04otJheBW3LghbdqU9Ni1K+n9uXjRWoxSpaS2baXevaVGjaz1laTp06Wnn069zbRpSUWxUlKB+LRp0qRJ0sGD/22bM+d/C80TEpIK/jZtSio8O3AgqfgsOtp8nv7+0kMPSV26SE8+mTSOVVbe/8REaepU6eOPk3L9F29JrfLn14nOnaU2be5fBL96tfTuu0n/TUz87+89PaWHH5beftv8isOpiY+X5s2T5syRNm5M+oylxs8vqeiye/ekz4G/f9pjjBsnvfWWtbxmzEh6pKZv36TPoxkhIdKUKdLvvycVEKf1WcqTJ+n17d9feuQR6wXNZ86kXbT57/xXrZImTEj6b0rvffPmyQvNrf4t/pst74sVqY2dkox+j1KyZEnS3UrWrEmah1JSt640YoTUs6fzLkbICMWLJxX4btuWertr19I3zt690rffJr3mR46kfWeZihWl1q2loUOt74+lJCoqaR9r5kzp1KmU2/j6Sp07S6+/nnxfq2TJ1C9SLFEiaS5ITb9+ac91p08njWXVli1J+63r1iVdQJSWwoWTti9PPZW0wr0tn2+r+027d0sffCAtWpR0gVxK8uWTBg6UXnvNtm05AAAAAACAG6qV0h2VbTR//nx99dVXWrt2rRJTOvZsUkhIiN1yMuPfK5OblfNfx5AoNE8floAAACeJS0jjpJmD3Im3fWcBAAAg07t+Pe02bdpIHTpYj+3hIX32mbm233//3589+2zahTuzZlnL6c4dae7c1Ns8/LC5IvMff5SqVEl6bebMSbvIXEoqbt2yRfrf/5KK2f73v/sXGNnDokVJq3V+9lnKRebpFR0tDRiQVPS4bNn9i8ylpKLpESOSCvh27rQ2TuPGScX5H3yQtLK+1SJzKalg7euvk2I1bpxU5Ocoly8njTFgQMpF5vczb17S6/Pii0kXgezZY63IXEpqv2qVNGRI0grE776b+vuSHufOJRW1P/tsikXmd2W/fl0Pfv+9vJo2/e97FxOTdAFC8+bS33+nXGgsJf187dqkYr2hQ+/fLi2JidLnn0ulS0tPPCH99lvaReZ381yzJumCmBIlku7WkI4D0w4XGiq99JJUrJj0yitJuZv5LN26lTRvdOyYtBr90qWOyzEuLumz06KFtHKla7+ejuAK79G1a9Jjj0mdOiX9/d2vyFyStm+XHn886WInW+ZgN2KYKW62esHUXVu3Sq1aSTVqJG2TDh9Ou8hcSipG/+yzpDt09OyZvvfgr7+S9nPGjbt/kbmUtH8yZ07SdmnMGNf/G121KulCsocekn74wVyRuZS0/zZnTtKFG9WqJW2LHSUmJmn7XLt20pip7QPeuJF0EU7FikmF6QAAAAAAAFlAATvc2e/SpUtq0aKFunfvrtWrV6eryFzK+ILtPDbe4TPbv45ZxsVl7GKwmQ2F5gDgJNm8nLPilY83Uz8AAEC6DBpke9+HHpKqV0+73d69SYXA/1S2bFJBW2p+/DH1wrh/+/33pCK91Px7lel/Cw9PKrbr0yepQMxW4eFJq4A3aGC+GMqKGTOkrl2TVst1hNDQpALRqVOt9TtwIOl9Xb/eIWmZsnGjVL++9Ouv9o994ULS537zZvvHtioiQho9Oul9slqwnpajR5M+u2mt+vsPHtu2Jb33dwu7IyKScps27f+xd99xUlX3/8ffM7O90Ys0pQgIIh0bIqAI2DBRjMaG9YsxxkQTo7GAGjWm2KMxvygae2wRo4JIEQFFioKI0pEOSttepvz+uO66sztz750+d3k9H499wN4595yzO3Pfe3f2c8+NbOzHHjPuTBCpXbt+XBF469bI96/13XdGYfBpp0m7d0ffT6J89pk0cKD08MPG9zhaX31lFDP/5jdGUXg81dQYxc2hLjI6FKTDc7Rrl3E8vvVWZPvNny8NG2YUPjdVdv7wFOkfvAIB42Kp4cONCyui5fMZF8wNGCDNmBH5/q+8YhRUR3K7X59P+uMfpfPPj38WxIPPJ912m3TqqcYdQWLx1VfG13nFFcaq7/H03XfGxVKPP27v4oJatcdqJBeuAQAAAAAAOFRRUVFM+2/fvl0jR47UnDlz4jQjyZuoxXzCaFgwjtSg2hAAUqQgO0PNcpP7w7BZbqbyszxJHRMAAKBJad5cGjcutj5+/nN77WbPbrzNquh7587Q+4VjtQJ6ixbSeeeFf/z7740Cu1desT+mlS++kIYOlVavjl+fH31krKadqNVHq6uNItFoi6lLS6Xx41O7QmdVlXTRRbEXpdVXWWkU8H37bfz6jIf5843jMF6vh/37jedv587I912/XrrySmMu550X/ff/n/+Upk+3337DBqOwN5K8sDJ7tnHBwq5d8eszVm+9ZRSyNrxwJxYPPyydc05kF/VYue666Ipkm4J0eI5KSozC1WgvltqxwyjqtXMnDwdy2XluBg+236HfL11wgXTLLfG7w8T330unn25ccGfX9OnGz71o5/DGG9bnZcnm9RoXW9x3X2TF21amTTPucBPLhSD1HThgHDMRXJwVpLhYuuyyxN2hBAAAAAAAIE1kZGTEtP+kSZO0bt26RtsHDBigW2+9VW+99ZaWL1+uXbt2qbi4WNXV1QoEAkEfU6ZMiWkOaBpieyUCACy5XC516NCh7v/1tx/dsUgL1+9N2lz6dWwWNAcAaChcZgFAuooot4qKwhdNV1QYq3tbOe44KSsrwlk2MGKEvXZLlhjF0fX99KdS69Y/roAcyr//bawsbGX/fuuv+dJLpZyc0I9VVkoTJiRmFdcDB4yCsU8/ldq3j72/iy9ObDFSPFbrLiszvt/LlsX+GotWZaV0ySXG6twxvnkpSbr3XmNF83T03/8aq9xffnnsfa1cGdv+06cbq+3PnBlbP3/4g3HBg5W9e43C+EQUhH/7rXTmmcbFHfn58e8/EosXG0WkiVhx+L33jOLwf/wj9r5mz07f4yTR0uU5uvFGI/disX1701yRfuNG4wIwM263cdzb9dvfGquQx1sgYKy83amTsVK2mV27frzIJxbPPhvb/vH2i19I77+fmL6XL5d+9jPjZ5YnxkUsVqyIfT7Llhl3Yjn//Nj7QpPC+1oAnIbcAuAkZBacqEvLPH3wG5t/k0mRZxZs0itLYrjjZIQuHNZZl5/YNWnjRapLy7y49ncor4b97rvv6sMGdxNs27at/v3vf2vs2LG2+6mI9x1i4UgUmgNAgnk8Hg0dOjTkY/07NU9qoXn/zs2SNhYAZzLLLABIRxHlVufO0muvhX5s9mx7heb9+9ufXDj9+kkul/VKk6EKuLOyjGLkBx8Mv99bbxkrThYUmPf/6qvGStxmzFbq/O1v7a++3KyZ1L27lJtrFMmvW2dd3PXtt9LkyUZBcKySXcTpckk9ehgXBezbZ+/rlaRVq4xVSKdOjW7cFi2MwvzcXONDMi6i+O47owjSzhw2bpReesl4ncUqUd/3zEypY0epsFDKyzOOi+pqY4XT7duNf+247z7j64y1YK6hvDypZ09jnqtWGc+BlVCv8/btpSOOMArDQ6w40shXX0kLFhirQ5u57DJ7/UnGa/iII4zv8a5dxuvDyrJl0m23GatKp0pJibGitd03wA8/3Ph+BwJG9uzebb3PU08Zd7g455xYZnroFpmny3M0d670r3/Zm4NkFDF36mT8nF23zrgbRFO1b590ySVyWa0MP3Gi1KWLvT5ff1166CF7bbOzpSOPNM4hSkuNiwEqK833qa6WLrzQuFtEnskfJX/1K/OL9urzeKRevYx57Nwpbd5sb79ke+45+xc71D9POXhQWrvW3gV5770n3X+/dPvtsc01lC5dpA4djHOWDRvs7fPUUxSaoxHe1wLgNOQWACchs+BEOZke9WxXmOppmJp04hFJLTSfdELXtP+exIvL5VJ+qhdESaGXX3456HOPx6N33nlHw4YNi6ifffv2xXNacCgKzZuY7777TkuWLNGGDRtUXFyszMxMtWrVSn369NGQIUPS7iodn8+ndevWafXq1dqzZ48OHDggt9utFi1aqFWrVhowYIC6deuW6mkCCXP2gA56Yp7NP57EY7z+HZM2FgAAgKNstfkmXs+esY+Vn28U0mzfbt5uy5bQ26+5xrzQvLxceuMNo5jUzPPPmz9+4olSnz6hH1u/3ijusXLmmcYqy8cfH7z9+++lxx83ipXMit3ffltauNCYSzx17CiNHCkddpjk8xnP/9q10pdfxt731VdLd95pFCLW2rXLKGp+/HHrCwwee0z6/e9/LBQPp3NnY9X3YcOkIUOMQn6zN0zLy43v5cMPG4ViZv7f/4tPoXlDOTnGKrNHHmlcCLFzp/E6X7w4/D4ZGcZzNWKE8bX262c8b2arJm3caKxyev/9RqFkOOvXS/PmSaecEu1XFCwzU7rrLun663+80KO01Lgzwauv2u+nUyfpn/80CmRrv86FC6WzzjLuRGBm5kzzQvOPPrJ3Uc1ll0k33WR8v+vbulX685+lv//d/LX85JPSDTdIXeutjDNpkvFc1tq1yygINTN2rHTLLeZtQt314MEHrVdsz8szjrWrrjIyub5ly6Rbb5VmzTLv49ZbjeclnhcruN3S4MHSoEFSy5bGhQbbtklLl0p79sRvnFoNnxc7du82vm+lpebtjjhCOvXU0I+ly3P0wAPm+9caPdqYc/0LzkpKjCL1O+4w7krhNJWVRgbWV1VlPL+LFxu5tddicYS2be0Xjnu9xjmBlb59pbvvNs4h6t/ho6rKmNPNN5tfaLBzpzGn224L/fiGDcZ5kpWMDGOsG2+UWrX6cfs33xivy+nTrftIlupqyc4tjN1u6Ze/NOZf/5jat8/I9Xvvtb544i9/MVZOb9kytjnXGjdO+utfjee91rJlRjatWmW+7/z5xsUqVudMAAAAAACksd7tizTsiJb6bHPii3mHdW2pXu0PjSJzSLMavHc6bty4iIvMJWmjnQVo0ORRaN5EvP7663rkkUe0cOFCBcL8oa+wsFDnn3++br75ZvWMR3FEFPx+vxYtWqQPP/xQc+fO1eLFi1Vl8eZ1mzZtdPbZZ+u6667TwIEDkzRT6euvv9bAgQNDzm/atGmaNGlS0uaCposTRgAAgDRh92r8oqL4jNesmXWhebg59eplFN3Onx9+3+efNy8037jRejVys9XM777beuXLP/zBKFgKpXVrY9XuoUONAkCzgtX775f+9z/zsezq3NkotP7pT0M/vmVL+AJ/O/74x9CFbe3bS48+aqzI+9vfmvexb5/04otGYWU406dHftFDXp40Zozx8ZOfmK8U/9lnRgFiTk5kY4Tjdhtf9223hT6GKitDF7+PHm0U1rZoEdl43boZ440ebRThm72+PvoofoXmTz0lXX558LaCAunpp6X337e30nqzZtKcOUYxfn0nnmgU95m9LiRpyRLzx++4w3oOTz0V/vjv3Nm4GKJPH6PQMJzqaulvfzMurqh1xBHGRy07qwK3bx95EfSBA8bYZgoLjZWsBw8O/fjgwdKMGdJFF0mvvBK+n2++Me4icd55kc0xnLPOMgpku3dv/FggYBQFH3ZYfMaq1fB5sbJnj3TdddZF5l26GN/j+hfd1EqX52jtWumDD8znIRkror/+euNi9cJC6Te/kQYOlE47Taqpse4rnezeLY0aFf3+7doZF7fYfU3++9/Wd1M49VTjZ1yowuHsbOMiqBEjpGOPNb/w4q9/NYrEQy268thj9u7w8dxz0s9/3nh7797Gz9CrrpKeeca6n2R4+mljpX8rTz0V+udIy5bGz4dBg6Szzzb//hQXG8dvuHO8SFxyifF9bnjx2ODBxkUkffqYX2Dl9UpffNH4gkYAAAAAABxm8shu+uzZxNcNXXtyiPcd0SRVV1drT4P3z0466aSI+/H5fPrss8/iNS04mDvVE0Bstm/frpEjR2rixIlasGBB2CJzSSopKdHTTz+tfv366Z577jFtG2+bNm3S9ddfr06dOumkk07SXXfdpfnz51sWmUvGKu1PP/20Bg0apHPOOUe7rFY7igOfz6fLLrvM1vwAK16vV2+//bbefvtteUMU40wemZxV+zlhBGCHVWYBQLqJW25VVNhrF69C80IbFwCazcmsCFwyivO2bQv/uNVq5s2bSxMnhn6spsa8SFkyinv/+EfzNpJ0xhlG0bOZOXPsPz9munUziuvDFZlLRmGk2WrQZoYOtV6p9aab7K3O/vbb5o/HevH46aebP15dbV2wHInnnzdWDQ53/OTkhH5e2raNvMi8vkGDrAsgFy6Mvv/6Tj65cZF5rfx8ye4buLfc0rjIvNbEicYKu2bWrg3/2M6d0scfm+9/7rnW+SJJ114bvgC4lp2V0xNhxgxjpWkz991nPX+32yiUr7+icijx+jr/7/+MYz9UkblkFGKOGmUU96bK998bF2asXm3erlMn4+dQuAL2dHmOpk+3vstE8+bGXR7MVq0fOVL61a/M+2lqzjnHKPCtv8K7lf/8x/zxvDzphResV6c+4gjp9tvN2xw4EP6COjvH7IUXhi4yr+VyGQXrXbpY95UMdlZoP+cc64uVzjjDyPd4jGelSxfpH/8If4eS9u2Nn0lWrC5ewCGH97UAOA25BcBJyCwgcUb3bqez+3ewbhiDCQM6aFTvtgkdI90EAgEdOHBABw4cSGqdZDr4/vvvG21rGcUd6t577z2VWi06gkMCheYOtnbtWg0dOlQfffRRRPtVV1frzjvv1EUXXSSfz5eg2QX76KOP9Pjjj2vnzp0x9fP222+rb9++WmS18l6MHnjgAS2J5x/VAROcMAIAAKSBZL/B5Lbx67jZnM4911h9Mhy/31gVO5wXXjAf+5JLwhd6LVpkXSB41VXhC4caslqtuKLCWHU6Fi6XsZJqqJV14+WGG+x9zddfb93mk08ie02uXCk9+KB09dVGwXO3bkYxakGB8VpzuYI/7BQSm12oEIlrrjEv1otESYmxqvCttxqF6cccYzynLVoYhaYNv06XS9qxw7zPeH2dVoV5Rx1l3YfbLV15ZfjHi4qMVfHNHDwY/jE7KzdffbV1m1onn2z++ObNxmrSyTZzpvnjmZnmd3yor1Ur6eijzdvMmGGvLzNHH23c9cBubqbC3r1GkfmqVebtOnY0isy7mVxUny7PkZ33F3/+c+MuHFZ+9av0fv7iwO/xyH/55cZdL956yygEtquy0vxOLJKxkrbdCyns3Ong/fcbb9u9W1q/3nrfm26ybpOXJ02ebN0u0SoqpAULrNvZ+Zok6cYbrdusWSNt3Wqvv3B+/Wvje2jGzoUMZj/3AAAAAABwkLvO7qt2RdkJ6btdUbamntU3IX0jPeXn5zfaFqr43MqDDz4Yj+mgCbBYBgnpau/evRozZkzIwu3BgwdrwoQJ6tq1qyoqKrR27Vq99NJL2tHgj6svv/yy2rZtq4cffjhJs27M4/Fo4MCBGj58uDp37qy2bdvK4/Fo9+7dWrx4sd59912VNChg2Ldvn8aPH6/Zs2dryJAhcZ/TqlWrdNddd8W9X8DMXWf31eJNe7W7OP6r6HPCCAAAYIPV6pm1rAqs7bJTFGNWfJOTYxSDP/JI+DbPPy/9/veNt3/yiXWRlVkhslWhmGQUXsWz+Oqbb6Rx46Lf/5RT7K0kHgurVcLrt3O5zAvJ9+6VNmyQevQI36a83Hj+n3pK+vbbyOZqx/79sffhdku33RZ7P599Jt19tzRrlrHaejzF4+uUpLFjzR+3Uzw5YIDUpo15m9atjddGOGbZYufYjeU4C+Wbb6TevePbpxWrr7OmJn53p5CkXbuM1ZObN4++j1tusV6VO5X27ZPGjDEuajFz2GHGXSjMsktKn+fIzi1f7WZ7ly5Sv37W3yMHc/t8Cnz4oTRwoLHavJ2L5motWWJ9d5JXXjE+4iXUhS5Ll1rv166d9Wr6tc480/puJom2ZIlkdVfOli3tnwd162ZcvGF1UcnHH8d2IZmd1crt/Oyk0BwAAAAA0ES0yM/Sc1cM08+e+lQHK2ri1m+z3Ew9d8UwtchP4/cfEXfNmjVTXl6eysvL67Z98MEHuvXWW2338a9//Uvz5s1LwOzgRKxo7lDXXHONtmzZErStsLBQ//3vf7V06VLdcccduvjii3X11VfrL3/5izZv3qypU6c26ueRRx7R+6FWd0kgl8ul0aNH69///rf279+vJUuW6KGHHtKNN96oiy++WBdeeKF+/etf6+WXX9aWLVv0m9/8Rq4GKwIVFxfroosuUnWc/8Ds9Xo1adKkoH6HDRsW1zGAUGpPGJvlZsa1X04YAQAAbLJ7u7h4FbMUF1u3adHC/HGrVam/+kpavrzx9uefN9/v+OPNV4e1WiE6EfbsiW3/n/0sPvMIp3Nn6+erVmGh9arUklEYGc78+VL37kZxWyKKzKX4FGAff7xRfBmt6mpp0iTp2GOld9+Nf5G5FJ+vs2NH6yLjEKuHNNKnj3WbnBzzx83uXOfEYzcaMd5NLyqxfJ2ZmcYK/enqwAHptNOkzz83b9eunVFk3rOndZ/p8hzt3m29X79+9seIpK1DubZuNVZvHztWiuSWuemSP3aO1WOOsT/GUUdJGSley8fO8XTMMZGtuG/ne2B2nmKldWt75wd2fnZ6vdHPAwAAAACANNO7fZFe/b/j4rayebuibL36f8epd/s4LuoAxxg+fHjQ5/PmzdN7771na98ZM2boV7/6VSKmBYei0NyBZs2apTfffDNoW1ZWlubMmaMJEyaE3CczM1NTpkwJuXr59ddfL28S3pDNyMjQ5ZdfrnXr1mn27Nm65JJLVFhYaLpP8+bN9eCDD2ratGmNis3Xrl2rxx57LK5zvO+++7Rs2bK6zy+++GKNHz8+rmMA4dg7YQyoQOVqoWIVqFxS+FUQOWEEAACIQOfO9tqtWxf7WOXl9gqurApw+vSxXp3y3/8O/ry6Wnr1VfN9rArYo7i1XsxiHTPRFxC3bRtZe6tVqyVjBeFQZswwij5jKfCyoyYOK6bE8n33eqUzzpCeey72eViNEys7K65m2/jDQKSvo0g58diNVFmZ9arJiRDL13n00fbvqpFsBw8aeVPvvbKQ2rY1isztrF6fLs/RwYP2jv9IjstEH8Pp5MMPjRWpzS5uqS9d8ifcz9b6Wre2P0ZGRmx3M4iH776zbhPpa9NOezvjhtOxo7126XynBwAAAAAAEqR3+yLNuGGEJgzoEFM/EwZ00IwbRlAzdAg7//zzG2372c9+ptdffz3sPpWVlbr77rs1YcIEVfzwPm5RPO8+Ccei0NyB7rnnnkbbpkyZoiFDhljue8MNN2jMmDFB2zZs2KCXXnopbvMLZfDgwfrmm2/0zDPPqHv37hHvf9lll+m6665rtP3fDYsmYrBixQr98Y9/rPu8bdu2IQvzgUQKdcLYy7VFv8t4RS9k3qsvsq/Rqpyr9HnOZK3KuUpfZF+jFzLv1e8yXlFP19a6fThhBAAAiJCd4jhJWrEi9rG+/FLy+63b2ZmTVVH4yy8HF9K99555kVXz5tarf8djBehIxbqSdadO8ZlHOJEWidpZobOkpPG2Awekyy+XqqoiGy9VYvm+//WvRiGjE9h5Pt023oKz008snHjsRioVX6MU29eZ6HyKVnGxsWr1kiXm7Vq3lmbPtrciv5Q+z1GojG3I5bK+i0B9iT6G4+3ww6VAwPjw+43zg48+kq64wl5mffCBVO+9VFPpkj92fn7auTAolvbxZuduO3l5kfVp57Ucy11+7P5x0s7rEAAAAACAJqhFfpYeuWCgnpk0RMO62rwj7w+GdW2paZOG6pELBqpFPhdxH8ouvfTSRnWapaWlmjhxogYPHqypU6fqhRde0KuvvqrHHntMkyZNUqdOnTRlyhRV//DeWp8+fXTttdemYvpIMym+ryMitXr1an388cdB21q3bq2bbrrJdh/333+/Zs2aFbTtySef1KWXXhqXOYbSLw63jr3zzjv15JNPyldvpZyVK1dq69at6mx39cEwampqdNlll6mm3optf//739WqVauY+gWiUXvCeEXbNcr89DH1qV4Vtm1zV5mGe77ScH2l6zKma3XW0ao57gb1Hz0wiTMGAABoAjp1kg47TNq507zdp58aKz1nZkY/VoPf6cIaOtS6zcSJ0g03GAXIoezZI82caawMLUnPP2/e38UXWxdNx/K1p0qzZontP9LVecvKrNuEugPYQw/ZW8n8pz+VLrtMGjTIWD29YRHcs88aBeuJFu33vaxMCnGRfSOHHSb9+tfSmDFS9+5SQUHjorQjjpC+/Ta6edgVr0K4RBfUOfHYjZQTv8ZE51M0Skul8eOlxYvN27VsaVwQcvTR9vtOl+fI4i6LkowC7MpK+8XmdrI9XblcUosW0ogRxse550o/+Yn1RRT33iudf7501FHm7dLlebez+ridixDqKy6OaipxYydDyssj69POazmW7LL7845CcwAAAADAIW5073Ya3bud1uwq0fQV27Vi60F9uf2gDlb8WN/WLDdT/To2U//OzXR2/47q1d7G+144JGRmZuq1117T8OHDVd7g/aHly5dr+fLlpvt37NhR7777rp599tkEzhJOQaG5w7z88suNtl1++eXKjmDllMGDB2vw4MFaVu+2t59++qk2bdqkrl27xmWeidCmTRsNGTJEixv8kWvjxo0xF5rfc889WlFvZcKf/vSnOu+882LqE6jlcrnU7odbqbtcLusdyvdJ7/1O/VeFv1VJOH2qV0nzr5b2fyCN/7OUF9mVjQAQcWYBQIrFNbdGj5ZefNG8zf790owZ0llnRT+O3TtKnXKKdZvcXKM4/PHHw7d5/nmj0Hz/fundd837s1ohXTJWr7UyZ440apR1u2TxeBLb/549kbX/7jvrNi1DnMub3M6wzmOPSb/8pXmbSAvOohXt9/2996zn2Lu3cdGG1esxWV+rE9g5djdulNL4vSFLLVsahYlmd43o0iXxFx9EItH5FKmyMqPIfNEi83YtWhhF5v37R9Z/ujxHzZpJGRnBd/0I5bvvJLvvO9rJdqc4/XTj4qYQd5gMUlMj3Xij9P775u3s5M+dd0p33WV/jtEI9bO1oS1b7Pf3/feRF6bHW5s21m0iPU+x097OuEAK8L4WAKchtwA4CZkFpE6v9oX6XXvjLriBQEBl1T5Ve/3KynArP8vDMWkiI+PQLo8dOHCgZs6cqfPPP187rRbbque4447Ta6+9pk7pekdOJB1LQjjMjBkzGm2LpiA61D6h+k43Xbp0abRtl53V3EwsX75c999/f93nLVu21BNPPBFTn0B9Ho9Hxx13nI477jh5rP6AvGuV9OQJUhRF5kG+fM3oZ/dXsfUD4JATUWYBQBqIa2795Cf22j35ZPRjLF4sff65dbt+/aRu3ez1aVUc/vbb0sGD0n/+I1VVhW933HHGuFbsFKHWu7D5kLB1q1HIb0dJib0Cyvbtgz/ft09avdp8nz59rIvMJWnTJus2qWRn1f+777YuXCwpkfbujc+cmoJD4djNzJQ6djRvs2VL0yoIjqfycuPCpAULzNs1by7NmiUNjOJuaun0HP3wx3FTX35pv79I2jrBtddKw4dbt5sxQ5o/37xNuuRPjx7WbVatMlayt+OTT2KbTzwcdph1m5UrjRX67Vq50rpNw/MUIE3wvhYApyG3ADgJmQWkB5fLpYLsDLXMz1JBdgZF5iZcLpcKCgpUUFBwSH+fhg8frhUrVujmm29Wc4s7/g0ZMkTPPfecFi5cSJE5glBo7iBlZWWNblmQl5engVH8Ueekk05qtO1ju7dvT6GGt3GQpFyr27qbqK6u1qRJk+Stt3rRww8/XHcVJpBUu1ZJz54hldi/gsxUyU5p2ukUmwMAANh11ln2is7ef9965c5QAgHpV7+y1/bqq+3326+fUSQeTmWlsRL288+b92NnNXNJGjLEus0rr9jryw6zVW/TyXvv2Ws3Y4Z1sVerVlL37sHb7Fxkfcwx9uYQzes3meL1tc6Y4ZzXTzKk27Fr5439SAoja9n5Ol99NfJ+Q2lKr6+KCunMM6WPPjJvV1QkzZwpDR4c/Vjp8hwNG2a9v91s37bNXnGuk7hc0p//bK+t1UrkgwdbH/OzZsXv4qBwz/uAAVJBgfm+FRXS9On2xrE6t0qGIUMkq7ud7ttnfZeCWps2GcX2VuxchAAAAAAAAHAIGjlypAKBQNDHvHnzotpv5MiRcZlTmzZt9MADD+j777/Xp59+qqeffloPPPCA7r33Xj366KN65513tHPnTi1ZskSXXnqp3O4fy4qnTp3aaF52zJs3L+LvgaRGY02dOjWKr1h69tlng/rZvHlzVP3AQKG5g3zxxRfyN3iTfMiQIcrMzIy4r6FDhzbab5kDVq1av359o22H2Vm1JYy77rpLX9Zbbej000/XJZdcEnV/QNTK90kvnidVHohvv5UHpBfONfoHAAA41H31lVHkFO4jO1v62c/s9XXVVVKkb0jceqv02WfW7Vq3liZNiqxvq8L0v/xFWrgw/OPNmtn/2keOtC5oWrbMWEE9FgsXSmPH2lvdOh088oi9otjHHrNuc/zxjQvyDhyw3s/Oqur/+Y9xLKSzeHytNTXSvffGZTpNxtix1m3efFP69NPYxnnvPePily1bzNvl51v3ZfdOAfXZ+Trvu8+400O0qqqkJ54w8rApqKyUzj5bmjvXvF1hoXEBh50CbTPp8hydcIJ1Py+9ZK/4+bHHorswIt0df7x02mnW7ebMMS9kbtZMOvZY8z6qq6U77ohsfg1t3iz93/9JDzwQ+vGMDHvP+5Qp5neBkYzzlNdjvCNhPOTlSSeeaN3uwQft9WenXa9eUog7jwIAAAAAACC9eTweHXvssbriiit088036w9/+IOuv/56nXnmmWrPHexggkJzB/nmm28abeth53afIWRlZTW6vcGGDRuCVvZON1999ZXWrFkTtC03N1d9+/aNqr+lS5fqz/VW5SkqKtJTTz0V0xyBULxer/73v//pf//7X/hj7L3fxW8l84ZKdkrv35yYvgE0ObYyCwDSSNxz69JLJTu3gtuxQzr1VGnFCuu2NTVGkXm4oqeGpk41ivkiccEFxiqz4TT4XaqRiy4yCpXsKCoyVn+3cuWVks3VCepUVBir2J5yirFS5gcfOKdwb8kSozDSzMMP2yucnzCh8TaL2xlKMlYi/vbb8I9/8YVRgJfu7Hytzz0X/jG/X5o82d7xeSjp1ct6JelAQPrJT+ytZlvfwYPS008b/Z9xhrR4sfU+zZpZt1mwQCoujmwu550n5eSYt9m508ixSAvZv/3WOM67d5euu866mN4JKiuNzPnwQ/N2BQXG3RCOPz72MdPlOTr7bOtVtvfvN+74YbYy+vz5Rr43Ud5bb7XX8J57zB+/+GLrPp58Uvrb3+yNV8vvN843Jk2SevaU/vlP8yLxK6+07vObb6SJE6WSktCPL1kinXNO+pyjnHeedZs335SmTTNv8/77xnNg5ac/tTcvIAV4XwuA05BbAJyEzALgNIFAQAcOHNCBAwdsr8INIDQKzR0k1PL9hx9+eNT9dWmw6ojP59OWNP4DWagi8DFjxijPbjFEPVVVVbrsssuCTn7//Oc/Nyq+B+LF5/PJ5/OFfnDtTGlVgldA+vI1YxwAsME0swAgDcU1t3JyjGJJq8IzSdqwQRo6VPrNb4wC3oaKi6Xnn5cGD5b+9Cd74598snTttRFNWZJRJH7RRZHvV+uaayJr/7vfWbcpLTUKxq+6yijIClWk5/cbBa3PPGMU+bdrZxTNz5kT2XzSxe23G4Xc27YFb9+1y3id3HijdR8tWoR+Lrt0MVZiNVNZaaw8O7PBuX9ZmfToo9KIEfZWC0+1rl2t2/zjH8b3dF+Duzd9/rmxWvIzzyRmbk53s42LkHftMgrGf/tbafXq0G1qaqTly42CxHPPNY7dq64y7mZgV2am1KGDeZv9+42LTh55xCiAnDcv+CPU6uutWklXXGE9/scfS336GAXCO8Nc+F1aaozzwAPSSScZr83bbpO2b7fu3wlqaoyC0Q8+sG77u98Z7Rs+B1Yfu3Y17itdnqOePe2t1v3mm9K4cVK9uyLWjf3oo8bFFdXV1v041fDh+r5PH+t2M2YYP+/DufxyqU0b635++1vj/OHdd42fa6Fs2SK99pr0619Lhx8ujRplXIBUU2Pd/7nn2luN+513jNfILbdIb7xhHCf//rd04YXGBRfff2/dR7JccYW9r+mqq4xzkYbH0/79xl1AfvITyeqcurBQuumm6OcKJAHvawFwGnILgJOQWQAAHJos/kKJdLIrxB9mOnfuHHV/ofbdvXu3unXrFnWfibJmzZqQheZXW90ePow777xTq+v9sXTUqFG6JtLCCiBeFjycnHEWPiL1tHF7agAAgEPdaadJd9wh3X23dduaGqMA7uGHpZYtpY4dpfx8o7Bu+3Z7BU+1OnaUXnxRckd5Tfg119hbhbKhYcOk/v0j32fSJOnZZ83b+f1G4f7TTxur4XbrZhQolZdLe/dK331nrGLelPzzn9L/+3/SkUdKrVsbhdBr15qvhlvfL38p5eY23l5QYBS2Wa2IvnatURDZqpVRcFlTY6xoH65YLx2NHSvVuwNZWA8/LP3970aRYevWRoF/wyJ/BJs40SjInDvXvF1VlbGq8N/+Zlz8cPjhRraVlBiv6T174lNYO2SINH26eZsvvzSKSUM5/HApxMIMuvtu6T//sS4Erb0I5De/MTL4sMOk7Gyj6LI2o+weu060fbtRwG/HlCnRjTFtmvHzoqF0eY5+97vGF+eEMmuWdMwxUufOxjzKy428dVK2xmDtxIlqfddd1g3vuSf8MZ2XJ/3lL6FfDw3NmWN8ZGRIPXoYOeT1Gvnz3XeR3+mgPo/HuDDhwgut2+7aZf+ONKmUnS3ddZdRzG/G75ceesj4+Vl7nnLwoHGeYHc1wt/+1jjHAAAAAAAAAHDIoNDcQfY1XKVLUkFBQdT9hdp37969UfeXKDU1NbrkkktU3eAPmMOHD9eZZ54ZcX+LFy/W3+rdgjUvL0//7//9P7nsrFgIxNvur6Qti5Iz1rcLpd2rpXY2VqECAAA41N11l1HA9ve/299n377Gqyvb1batUejWsWN0+0vSgAHGCutmq4mGEu1Ft48+Ki1eLH39tb32paXSypXRjeU0gYBRgLh2bWT79eljrMQbzlVXWRea19q71/gIpUsXYzXYdDVihLGKrJ3vX02NtH698dFQUZFxd4KDB+M/RyervdPC7t322u/fb3wkwjnnWBeaR6NVK+PCndNPt16dt9b27U1npXInSJfn6JRTjOLcadPstd+61fg4xHw3cKD2H3mkWqxbZ97wnXeMu7wMGBD68csukz78UHrhBXsDe73SN99EMlV7LrhAevtt6ZVXYuunZUvj9ZsOP2cmTTLOEezc0SPa85SxY83PUwAAAAAAAAA0SRSaO0hZWVmjbbmhVjmzKdS+5eXlUfeXKDfddJOWNCiUyMnJCbnCuZXKykpddtllQbfyuffee9W9e/eY5xkve/bs0XfffRfRPusb/EHd6/XK22AVGpfLJY/HE9QmnES1laSMerd6j6Stz+dTIBBI67Yej6fugoX6bet/nbX/r2v75eth+0sE/8r/yD/q9kbz9fv98pusABZJW7fbLfcPq3CmQ9tAIGB6+650aFv/OEpUWyk1xz0Z8aNwGRHPtvE4lkN938kIMiId2kpkRKxt43ksp9t5RK1wr4tQbcPx+/2qbRl47DH527aV+6675ErgiraBXr3kmz7dWO3b640pI/xXXCFPBIXmgaIi+c47r9EqlraOz9xc6b335DntNLmsCs/iINzz6wkEZHXZcMN9oz2WwwmccIICOTlyz5lj2TZsH3l58j3zjLHSaoP51h1HF1+swKOPyrVsWfTjDBwo/1VXyXPddabt/H6//PXm0fBYDvh88oTbuWE/UWSE6/775Tn3XBsjhBZwueT617+M1YotCgD9fr9p9li9AgKBgHwhXp8Nj2W7l7ebHfeRvt5Dnhu0aye9/74848bJtWePzVlFzuv1Sl6veZ6cd548t94ql92id6vx6hs9Wq5nnpH7yivlsrtSb4QCUtDz0ShPvF7L10/DY62W3Zxy2TgWfT6fAg3fj/hhu53jOBb1x65VN4fTTpN/2jS5rrgioc+R1fHpf/BBuRYsiOlnWaB1awXGjpX7xRdN29X/fiT0dw07c5bk9/ksf3+oPbbWTpyoY++7z7Jf/913K/Daa+GP+6eekru4WO5EXGRSO4fan1Nm5zJ//7s8mzfL9emn0Q2Slye99ZYCl14ql9nPmXqLm4Q7lt31zj/Dqc3UWiEz4vHH5d66Ve5Zs2x/GXYFjjlGvpdeMorUQ+RJ/Z+flj83Ffq4rFW/X8t51fu5mA7vMfB+ROrfj6iP9yPsteX9CAMZEXlbJ2ZEOr5nGepviOHakhFkRKqPezIi8rZS0zqPaJhZZISBjIi8rZ3j3uv11r1Oap+LQCBg+tqp37a2PW1T17Z++1S1rf+4E+ZL2+S1tdPebtuGjwUCgbA/8xN5HpFoFJo7SE2IW67n5ORE3V+oQvOGq4an2j/+8Q899thjjbb/5S9/UZ8+ka/KfNttt2nNmjV1nx9//PH61a9+FdMc4+2JJ57QXXZuRWti/vz52tzg1tHt2rXTcccdV/f5jBkzwp7YtWrVSsOHD6/7fNasWWFfG82bN9fJJ59c9/mcOXNUUVERsm1hYaFGjx4dNM+SkpKQbXNzc3XaaafVfb5gwQIdOHAgZNusrCyNHz++7vNPPvkk7Or8Ho8naCX8JUuWaLfJH7QnTJhQ9//ly5drx44dYdueccYZdQG/YsUKbQ2xwtXMH27HPG7cOGVnZ0vboy8Qicb3Kz/QJ+UDJUkjRoxQixYtJEkbNmzQ6tWrw+534oknqnXr1pKkzZs368svvwzb9thjj1X79u0lSdu2bdPnn38etu2QIUPU8YdVO3fu3KmlS5eGbTtw4EB16dJFknFBxuLFi8O27devn7p16ybJuFPDwoULw7bt06ePjjzySEnSgQMHNH/+/LBte/Xqpd69e0uSSkpKNNfkdvM9evRQ3759JUkVFRWaZfJHviOOOEL9+/eXZOTwjBkzwrbt3LmzBg0aJMn4Jfzdd98N27ZDhw4aOnRo3edmbckIQ6ozolZdRkhatWpVo0yvb8yYMcrLy5Mkff31140uPqpv1KhRKioqkiStXbs26GeiGTKCjCAjDGTEj9LtPKJW7blWQ/Uzwsr27dvVuV8/ST9kRP/+anX33Rr42GPKj0MhZH0Bt1ubTztNX11+uXxff123MngsGTGreXONzc1VZphjqaHNJ5yglfPmNdoeSUZ0ePJJDZ02zViZNhF+eCMnXEacuHevWlt0UX/+sWREOHsPHtSSG27Q+OpqacECy/YN+XJz9ckf/qC9u3ZJIb7XdRnhdmvV3XerxyWXKDeK1fNLO3TQghtuUNtlyzTIou3atWu1pt5cGmZE+YoVln1IMWSEx6PeEyeq12uv2RilseI77lCziRONQnML27ZtMz2PmBBqp3r27t2rhSGet/rnEeXl5cq3N3XT84hIX+9m5xHZf/qTBj3yiNquWGFzZpGZM3euKtq2tTyPaP2LX+j4u+6SO8YLekJmRLNmanHffRry8MPKM/k5HK2Kykrl1fu84XlE7u7dOq3xbkG2bdumz0O8fuyeR3S2cSyuWLFCW38Yo/55xJo1a5Toe57VH7tW0HnEccdp5333adCDD6pg1664j19eUaEPQ3x/g37XKCvTqltv1Ql33KHCKFZMryos1Ce33KL2S5aot0XbTz75RHtLSyUl+HcNG/OuKC/XulWrbP+usWvoUAWOOUYui7uTuP77X61+7TUdfcEFddsanUdMmqQezZvrqJdekjsBfyBZu26ddi9YYPm7hueGG3RsWZnamJyfhtSmjfTGG9JJJ6mmuFhZZm1/ON6k8L9rDNy2TVZnqnPmzFFFu3Z1n4fLCNfkyToqP1893n5bLht/XLNjy6hRWvl//ydfg7uqhPtdw+rnZkV5uWaZnFvWzwgrFRUVdTnM+xG8H9GqVaugtnPnzuX9CPF+BO9ZkhG1nPCeZf33tciIH5ERBjLCcChnRH3pkBEzZ84kI35ARhjinREul0tt2rRRVlZW3etBkg6aXOydmZmp/Px8W20zMjJUUFBgq63H41FhYWHd58XFxWGLTiNp63a7616/kvE9DndBgcvlUrNmzeo+Ly0tDZtpkbSVjFyrVVZWZlrMWr9teXl5yFrGWvXnYNW2qKiorqi3oqLCtH6xsLCwrlC3srJSVVVVYdvWf46rqqos29ZmWlVVlSorK8O2zc/PV2ZmpiTjNRzu50CkbfPy8pSVZbzTVFNTY7pgcG5ubl2uer3ekAsYR9M2Jyenrh7V5/Op9If3MkPJzs6uqz/1+/1hf8ZF2rb+cR8IBFRcXBy2bSTHfaIyoqHi4mJ98MEHIY/9WM4jlsWwCFU82FvSDWnL7soedve1c2VHskyfPl2//OUvG22/6KKLQm63smjRIj388MN1n2dnZ+vpp5+2vbIhEHeBgLQzMX/QD6d5+WZjXAAAANi29+ijNfvxx/XlFVeovE2bmPsLeDzaeeyxmvfgg1o5ebJ89QqQYuXLzdX2k06y3f7b06xKIG2MmZ8vvfCC9J//SD+8ORyrqqIibT/nHGnRIqneGy7pzJeXJ82aJV19dWQ79umjNU88ob1HH22reU379vr4/vt1IMI7c33Xr58+/tOfVFXvjeB0981FF2nV5ZfLX291ByvenBwtu+EGlV5zTQJn1jRUtWypT6ZO1YrJk1X6wx/DYlXRqpXWnXOO5jz8sCratrW1z/f9+2vhH/8Ytzk0tL9nTy355z+Niw7i9Po/0K2bVk2apE//9re49Heo29+zp+Y99JDWnXOOqvPtXpJhrrpvX62aNEkf/+lPttpXtmyphffeq53DhkU0zoFu3fTxn/+sgz16RDNNZ3G55L/lFutmgYAOmzbNvJHbrfU//anmP/CA9h9/fNCq39HyZWVpx/HHa/Ett2i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d2uq4N1wTk8hbAOzDWgtAWlq1apXOnj1rcSw0NFRdunTxUET2I28BnuFo3QLXtchZ8E4UmgOAg1asWGF17PHHH7d7nOT6JDc2AEhS8eLFrY5dvnzZA5GYd/PmTasdprJnz6527drZNU5ISIiaN29ucezs2bM6fPiwsyECSGcMw9Bvv/1mdbx79+52jePv76/w8HCLY3fv3tWGDRtS7UfeAjKX5HYz79Kli/Lnz++BaOxDvgIyn8y+Tjp48KDOnz9vcaxixYqqXLmyXfNXqVJF5cuXtzi2ZcsW3b59265xAKQdb7wmJpG3ANiHtRaAtJbcdbGnn35aAQEBHojGfuQtwHMcOUfjuhY5C96LQnMAcEBMTIx2795tcSxbtmyqWbOm3WM1adLE6tjDu+kBwAPJ3YWb3h/Dt23bNiUmJloca9Cggfz8/Owei5wJZA5Hjx5VZGSkxbGyZcuqQIECdo/lSN4gbwGZR0JCgmbOnGl13FseD0y+AjKfzL5OSu77Zh9tbGv+hIQEqw8bAaQf3nhNTCJvAbAPay0AaenatWtatGiR1fEBAwZ4IBrHkLcAz3HkHI3rWuQseC8KzQHAAXv37lVSUpLFsTp16jh0Z2/dunWt+u3atcup+ABkXCdOnLA6VqhQIQ9EYl5yOa1BgwYOjdWwYUNT4wPwbp7OG56eH0DaWbp0qdUuK8WKFVPbtm09FJF9yFdA5uPp131mnx+A53jjNTGJvAXAPp7OGZ6eH0DamjVrlu7du2dxrGHDhqpYsaKHIrIfeQvwHEfO0Tz9ms3s8wPO8Pd0AADgjY4cOWJ1rEyZMg6NFRgYqKJFi+r06dN/Hzt58qQSEhLk70+aBvB/EhISkt1ZoG7dug6NFxkZqVGjRmnz5s06ceKErl69qsTEROXNm1chISGqXLmymjRpotatW6tcuXIOx+3KnFm6dGmrY0ePHnVoLACOiY2N1ZgxY7R582YdOXJEUVFRunfvnvLmzau8efOqXLlyatq0qVq2bKnq1as7NIcr80bx4sXl7++vhISEv4/ZyhvkLSDz+Pbbb62O9e/fX76+zu3NwDoLgLtk9nWSp+cH4BnR0dFav3691fE6deo4PObJkyf1xhtvaOvWrTpz5oyioqLk6+urkJAQhYSEqHr16mratKnatGmjYsWKOTwPeQvIGDLLOZ6n5weQtr777jurY656yh9rLSBjc7Rugeta5Cx4LyoYAcABZ86csToWFhbm8HjFixe3KDRPTEzUuXPnVKpUKYfHBJDxLF682GrHzbx58zr8OKXDhw/rvffeszp+8eJFXbx4Ufv379dPP/0kSerQoYPeeOMNNW3a1O55XJkzixQpIj8/P4tHWp06dcqhsQA4JioqSm+99ZbV8cuXL+vy5cs6dOiQFi5cKOmvu+n//e9/q0uXLnbN4cq84efnpyJFiujs2bN/Hzt//rzu37+f4tNoyFtA5nDp0iUtX77c4piPj4/69+/v9NisswC4S2ZfJ7ly/uLFi1sdI+8B6dP06dOtdtusVq2aSpYs6fCYmzdv1ubNm62Ox8bGKiIiQnv37tWMGTPk5+enHj166N///rdq1Khh9zzkLSBjyCzneOQsIPPYsWOH9u/fb3EsR44ceuKJJ1wyPmstIGNztG6B61qum5+chbTm3PZMAJBJPbxgkuTUnbbJ9b1y5YrD4wHIeOLi4vTmm29aHe/fv3+aPP1g+fLlatasmV588UXFx8fb1deVOdPPz8/qkVvkSyD92rp1q8LDw/XEE0/o1q1bpvu5e62VmJioq1evpsn85C0g/Zo+fbrFRWBJat26tUqUKJGmcbDOAmCPzL5Oevj7Pj4+KlKkiEPzFy1aVD4+PnbNDyDtXb16VR988IHV8YEDB6bJ/ImJifrpp59Up04djR49WoZh2NWfvAVkPt58jkfOAjKP5HYz79Wrl4KDg9M0DtZagPdxpm6B61rkLHgvCs0BwAHR0dFWx7Jnz+7weMn1vXbtmsPjAch43nzzTR07dsziWJ48efTvf//b6bFDQ0NVpkwZVaxYUQUKFJCfn1+KbSdOnKhmzZopJibG9Pjuzpnx8fG6c+eOw+MBcEzevHlVqlQpVapUSYUKFUpxdwBJmjt3rmrXrp3sBZzkeHqtRd4CMj7DMDR16lSr4656PPADrLMAuFpmXifdvn1b9+/ftziWNWvWVHNravz9/ZUlSxaLY1yPA9KfgQMHWr02S5cu7ZJCc19fXxUsWFDlypVThQoVFBoaKl/f5D86TUxM1Ntvv61u3bpZ3ayYEvIWkPFk5HM8chaQecTGxv79FIZ/cvV1MdZaQMbkTN0C17XIWfBeFJoDgAOSuyiUNWtWh8d2KgiHAAByKElEQVRLrm9sbKzD4wHIWObPn6/PP//c6vgnn3yi0NBQu8erX7++3n//fW3ZskU3b95UZGSkjh8/rkOHDuny5cu6efOmVq1apWeeeUaBgYFW/bdv365evXqZvtBDzgQyhqpVq+rNN9/U2rVrdfXqVV27dk0nT57UwYMHdfHiRd2+fVubNm3SiBEjkr0oc+LECT366KOmPlzzdN7w9PwA3G/Dhg06ceKExbGQkBA99thjTo3LOguAu3n6de/J+V09d3L9yXlA+jJu3DgtXLjQ4piPj48mT55s9YG6GX5+fmrZsqU+/vhj7dy5U7dv39alS5d09OhRHT58WJGRkbp27Zp+/fVXPf7448kWQi1cuFAvvviiqfnIW4D3y0zneOQsIPP4+eefrZ5AWqVKFT3yyCNOjctaC8j4nK1b4LqW6+ZOrj85C+5EoTkAOODhu8wkKSgoyOHxkls82Ps4PQAZ0+7du9W3b1+r4927d9czzzxj11h9+/bVkSNHtG3bNr3zzjtq2LChcubMadUuODhYrVu31nfffadjx46pQYMGVm1+/fXXZE8ik0POBLxbp06dtHPnTv3555/64IMP1KJFC4WEhFi1y5Ilixo3bqxx48bpzJkz6ty5s1WbXbt26Y033rA5p6fzhqfnB+B+yT0euE+fPskWBpjBOgtAWvH0696T87t67uTmJ+cB6cfSpUv1+uuvWx1/+eWX1apVK7vHe+2113T69GmtWbNGr776qurUqaNs2bJZtcudO7c6deqkuXPnas+ePapQoYJVm6+++krz58+3OSd5C/BemfEcj5wFZB7JXRdzdjdz1lpAxueKugWua7lu7uTmJ2fBnSg0BwAX8fHxcWlfwzCcCQdABnDq1Cl16tTJ6u7WChUqaNq0aXaP179/f5UvX96uPmFhYdqwYYPatm1r9b0PPvhAN2/etDsOiZwJeJMePXqoTp06dvUJCQnR4sWLk72wNHnyZJ06dcruODydNzw9PwDXuXHjRrIfVjnzgRrrLACe5OnXvSfnd2bu5PqT84D0YceOHerZs6fVDsDNmjXT2LFjHRpzyJAhKlasmF19qlWrph07dqh69epW3/vPf/5jeofifyJvAd6Bczzn506uPzkL8LyjR49q8+bNFseyZMmiPn36ODUuay0gY3N13cI/cV3LceQspCUKzQHAAQEBAVbH7t696/B4yfV1dCc9ABnDhQsX1Lp1a12+fNnieNGiRbVixQrlyJEjzWIJCAjQvHnzVLRoUYvj0dHRpk4cyZlA5jV58mTVrl3b4tj9+/f1xRdfpNrP03nD0/MDcK/Zs2dbvS7r1aunypUrp3ksrLMA2MvTr3tPzu/quZPrT84DPO/AgQPq2LGjVQFDtWrVtGDBAvn7+6dpPDly5NCSJUsUHBxscfzo0aNasmRJqn3JW0Dm483neOQsIHNIbjfzxx57THnz5vVANKy1AG/gyroFrmu5bu7k+pOz4E4UmgOAA5J7zJOrFx8Pn0wByDwiIyPVunVrnT592uJ4gQIFtGbNGoWFhaV5TDly5NDIkSOtji9fvtxmX3ImkHn5+/snu9ucrdzh6bzh6fkBuJc7Hg/sDNZZAOzh6de9J+d39dzJ9SfnAZ517NgxtWnTRteuXbM4XqFCBa1atUp58uTxSFzFihXTSy+9ZHU8rc9tk+tP3gLSH289xyNnARlfQkKCZs6caXXck9fFJNZaQHrm6roFrmu5bu7k+pOz4E4UmgOAA0JCQqyO3blzx+Hxkuub3BwAMr5r166pdevWOnLkiMXxfPnyac2aNSpXrpyHIpOeeOIJq12jNm7cqISEhFT7uTtnBgYGKnv27A6PB8C9WrVqpQIFClgcO3bsmM6fP59iH0+vtchbQMa1d+9e7d692+JYcHCwevXq5aGI/sI6C4BZmXmdlDNnTqvdn+Li4hx6nLr0V5FFXFycxTGuxwGec+rUKbVs2dJql7zSpUtrzZo1yp8/v4ci+8uTTz5pdWzNmjWp9iFvAZmXN57jkbOAjO/XX3/VlStXLI6VKFFCrVq18lBE/4e1FpD+uKNugeta5Cx4LwrNAcABDxdLSUq1WMqWiIgIU3MAyNiuX7+uNm3aaP/+/RbH8+bNq9WrV6ty5coeiuwvOXPmVM2aNS2OxcXFKTIyMtV+rsyZiYmJunTpks3xAaQfPj4+atq0qdXxc+fOpdjH3WstX19f5cuXL03mJ28B6cu3335rdaxnz54eL6ZmnQXArMy+Tnq40DQpKUkXL150aP4LFy7IMAy75gfgHmfPnlXLli114cIFi+MlSpTQ2rVrVbhwYQ9F9n8qVaqk0NBQi2PJXdd/GHkLyJy89RyPnAVkbMldF3vmmWfk4+PjgWgssdYC0hd31S1wXYucBe9FoTkAOKBkyZJWx86ePevweA8XWvn5+al48eIOjwfA+9y8eVNt2rTRnj17LI7nzp1bv/32m6pXr+6hyCwVKlTI6lhUVFSqfVyZMy9evGi160ty4wNIX+zNHa7MG0lJSVbFCkWLFrXaNcBd85O3gPQjLi5Os2fPtjru6ccDP8A6C4AZmX2d5Mr5k7vxkbwHpL2IiAi1aNHC6rVcrFgxrV27Nl1dJ394vRYfH6+bN2+m2oe8BWRe3niOR84CMq6LFy9qxYoVFsf8/PzUv39/D0VkjbUWkD64s26B61rkLHgvCs0BwAHly5e3OnbixAmHxoqPj7e6y6506dJWj9QDkHHdunVLbdu21a5duyyO58yZUytXrlTt2rU9FJm14OBgq2N3795NtY8rc+bJkyetjlWoUMGhsQCkHXtzhyvzxrlz53T//n2LY7byBnkLyJh++eUXXb9+3eJYpUqV1KBBAw9FZIl1FgAzMvs6ydPzA3CtCxcuqEWLFjp9+rTF8SJFimjdunXp7kNy1msA7OGNOcPT8wNwn+nTpysxMdHiWLt27VS0aFEPRWTNG/MmkNG4u26B61rkLHgvCs0BwAE1a9aUr69lCv3jjz+s7lYz448//rBa/NSqVcup+AB4j9u3b6tdu3basWOHxfEcOXJo5cqVeuSRRzwUWfKS23EltcdPSUr2hHPbtm0Ozb9161arY+RMIP2zN3d4Om94en4A7pHc44EHDBjggUiSxzoLgBmeft1n9vkBuM7FixfVokULqw/HCxcurHXr1ql06dIeiixlya3XQkJCUu1D3gIyL288x/P0/ADcwzAMTZ061ep4erouJrHWAjwtLeoWPP2azezzA86g0BwAHBAcHKyaNWtaHIuJibF6dIwZmzdvtjrWtGlTh2MD4D3u3Lmj9u3ba/v27RbHs2fPruXLl6t+/foeiixlhw8ftjoWGhqaap+GDRvKz8/P4ti2bdusdk4wg5wJeCd7c0eFChWsvn/s2DFFRkbaPbcjeYO8BWQ8p06d0vr16y2OBQYG6umnn/ZMQMlgnQXAjMy+TmrSpImpcRyZ39/fP9085QLI6C5fvqyWLVvq+PHjFscLFiyotWvXqmzZsh6KLGUxMTFWjybPnTt3qo9ll8hbQGbmjed45CwgY1q/fr3VzX358+dX586dPRSRNdZagGelVd0C17XIWfBeFJoDgIPat29vdWz+/Pl2jzNv3jxTYwPIWGJiYtSxY0erO02Dg4O1bNkyNWrUyEORpezPP//UhQsXLI6FhYUpV65cqfbLlSuX1cnn7du3tWrVKrvmj46O1rp16yyOFS9eXBUrVrRrHABpKzo62urCVFBQkMqVK5diHx8fH7Vt29bq+C+//GLX3AkJCVq0aJHV3M2aNUu1H3kLyHimTp0qwzAsjoWHh9vcTS6tsM4CYFZmXydVqVJFRYoUsTh26NChZAu5UpNcn4YNGypnzpx2jQPAfpGRkWrZsqWOHj1qcbxAgQJau3Ztso8STw/WrFmj+Ph4i2PVq1e32Y+8BWRO3nqOR84CMqbvvvvO6ljfvn1tFnGnJdZagOekZd0C17XIWfBeFJoDgIN69+5tdWzatGlWJ0Cp2bNnj3bu3GlxrF69eipZsqTT8QFIv2JjY/Xoo49q06ZNFsezZcumpUuXJnsna3owduxYq2MdOnQw1bdXr15Wx77++mu75p8xY4bi4uIsjiWXiwGkL5988onVTgDNmzdX1qxZU+3niryxZMkSXbx40eJYeHi4smXLZrMveQvIOBITEzV9+nSr4+np8cCsswDYI7Ovk5Kbf/LkyXbNn1x78h7gflFRUWrZsqXVB+KhoaFas2ZNur7J7cMPP7Q65sx6jbwFZGzefI5HzgIylhs3biRbwJmerotJrLUAT/FE3QLXtchZ8FIGAMBhjRs3NiRZfI0dO9Z0//bt21v1nz59uhsjBuBpd+/eNVq3bm312s+aNauxZs0aT4eXosWLFxs+Pj5Wca9evdpU/+vXrxs5cuSw6Ovr62ts2bLFVP/o6GijUKFCFv39/PyMEydOOPNjAXCznTt3GlmzZrXKHd9++63Nvvfv3zdKlChh1fenn34yNfe9e/eMqlWrWvVfv369qf7kLSDj+PXXX61yQfHixY3ExERPh2YYBussIKMLCwuzen07K7Ovk44ePWr4+vpa9M+WLZtx8uRJU/1PnDhhZMuWzaJ/rly5jBs3bpjqD2R07shbhmEY165dM6pXr241dr58+Yz9+/e7ZA53mThxolXcvr6+xtGjR031J28B7uOunOUMbz/HI2cB7pXWeSu5dUzjxo3dOqe9WGsBnuGpugWua5Gz4J08f6YFAF5sxYoVVouXLFmyGLt27bLZd8KECVZ9S5YsacTHx6dB5AA84d69e0aHDh2SPVkze5HZETt37jR++uknh4upli1bZgQHB1vF3aJFC7vGeeONN6zGKFeunBEdHZ1qv8TERKNHjx5WfZ966imHfh4A5pw6dcr4+uuvjbi4OIf679ixwyhQoECyr/v79++bGuPrr79OtgjhzJkzNvu+8sorTl9AJ28BGUPXrl2tXo+jRo1yydisswDY4q4igsy+TurVq5fVGE2aNDHu3r2bar+7d+8ajRo1sur79ttv2zU/kJG5I29dv37dqFWrltW4ISEhxr59+1wQdcpWrlxpLF++3OH+U6dONfz8/Kxi79+/v13jkLcA93B1zuIc7y/kLMB90rrQvGbNmlbzuXLjO9ZagHfyVN3CA1zXImfB+1BoDgBOCg8Pt3oTz5kzp7F48eJk28fHxxvvv/9+srsZLFmyJI2jB5BW7t+/n2y+CAoKMlauXOnWuRcsWGBIMsqWLWuMGTPGOHbsmKl+Z86cMQYPHpxsvgoKCjL27NljVxy3b982ihYtajVWhQoVjMOHDyfbJzo6OsU8e+HCBbvmB2CfPXv2GJKMwoULG2+//baxd+9eU/0iIyONt99+2wgICLB67fr6+hrLli0zHUNCQkKyxQiFChUytm7dmmyfmJgY4/nnn7fq4+/vT94CMqErV65Y5SNfX1/j7NmzLhmfdRYAW9xVRJDZ10nnzp0zsmfPbjVWo0aNUhzr/PnzRoMGDaz6hIWFGTExMXbND2Rkrs5bt27dMurVq2c1Zt68ee3OPY747LPPDElGzZo1jfHjxxvnz5831e/gwYPGE088YRW39FeBvNlxHiBvAe7h6pzFOd5fyFmA+6Rlofnu3buTzQmufE2y1gK8jyfrFh7guhY5C97HxzAMQwAAh129elU1a9bU+fPnrb5Xp04dhYeHq2TJkrp7966OHz+uH374QRcuXLBqO3ToUE2YMCEtQgbgAT/88IOeeuopq+O5c+dWWFiYU2PXqVNH3377bYrfX7hwobp27WpxrHTp0qpZs6aqVq2q/PnzK2fOnAoICFB0dLTOnj2rzZs3a9u2bUpISLAaz8/PT/Pnz1d4eLjdsW7atEktW7a0GtfX11edO3dWkyZNVKRIEV29elX79u3Tjz/+qJiYGKtx5s6dq8cff9zu+QGYt3fvXtWsWdPiWNGiRVWrVi1Vq1ZNhQoVUq5cuZQlSxZdv35dFy5c0NatW7Vp0ybFxcUlO+aECRM0dOhQu+I4duyY6tatq1u3bll9r2XLlmrbtq2KFy+umzdv6vDhw5o1a5aio6Ot2n7yySd65ZVX7JpbIm8B3u7jjz/W66+/bnGsXbt2WrFihUvGZ50FoGPHjrp48WKK3z906JDu379vcax69eqpjrls2TIVLlzY5tyZfZ30008/qXfv3lbHAwMD1aNHD9WtW1cFCxbUpUuXtGPHDs2bN8/qdxEQEKANGzaoQYMGds8PeKu0zlujR4/W22+/bXU8f/78KlSokImIU9alSxe9//77qbb5/PPPNWLEiL//7ePjo4oVK6p69eqqXLmy8uXLp1y5csnHx0fR0dE6ceKENm3apD/++EPJfYSaLVs2rV27VvXq1bM7XvIWYL+0zlmc4/0fchbgGE+eIz5syJAh+vLLLy2ODRo0SF9//bXdY6WEtRbgfTxZt/BPXNciZ8HLeLbOHQAyhkOHDhkFCxZM9o5bM19PPPGEkZCQ4OkfA4AbTZs2zeEcYeurWbNmqc79YBcWV3zlzp3bmDdvnlP/Fz///LPh7+/v0Pw+Pj7G+PHjnZofgDkPdjR3xVdQUJAxceJEh2PZuHFjsnf2m/165ZVXnPq/IG8B3qtChQpWr8u5c+e6bHzWWQCS243O2a/Tp0+bnj+zr5PGjRvn8M8eEBBgzJ8/36n5AW+U1nlr5MiRLp/vwVffvn1t/rwPdtl0xVfRokWNDRs2OPX/T94C7JPWOYtzPEvkLMB+nj5HfODu3btG7ty5rcbauXOnS39e1lqA9/Fk3cLDuK5FzoL38BUAwGkVK1bUzp071aRJE7v6BQQEaNSoUfrxxx/l5+fnpugAwHk+Pj569NFHtX//fnXv3t2psXr06KG1a9eqePHidvXLly+ffvnlFw0bNsyp+QGkrcaNG2vXrl0aMmSIw2M0adJE27dvV5UqVezqFxwcrK+//lqffPKJw3NL5C3AW23ZskVHjhyxOJYvXz516dLFQxElj3UWAGdk9nXSiBEjNH/+fIWEhNjVLywsTOvWrVO3bt2cmh9A5uDv76+nn35af/75p5o2berUWOQtIOPLSOd45CzAe82fP183btywOFatWjXVqVPHMwGlgrUWkHlxXYucBe9BoTkAuEjRokW1YcMGzZkzRw0bNpSPj0+KbbNnz67+/ftr//79GjlypHx9SccA3Kd9+/b67bff9NZbb6lZs2bKnTu3qX4+Pj4qV66cXnrpJR05ckRLlixR0aJFXRJTkyZNdPjwYX388ccqU6ZMqm2LFCmid955R8eOHdNjjz3mkvkB2FalShVt3LhR77//vtq2bavQ0FDTfcPCwvTss89q165d2rRpkypVquR0PJUrV9aePXv0zTff2HyUaEhIiIYPH66jR49q0KBBTs8tkbcAb/Tdd99ZHXv66acVGBjosjlYZwFIDzL7Oqlbt246duyY3nnnHZuPky9Tpow+/vhjHT58WI0aNXLJ/ADStwEDBmjx4sV6+eWX1aBBAwUHB5vq5+vrq6pVq+rtt9/WmTNnNGPGDOXJk8clMZG3gPSLczxr5CzAOyV3XezZZ591+TystQA4i+ta5Cx4Bx/DMAxPBwEAGVFkZKR27NihU6dO6datW/L391e+fPlUsWJF1a1b16X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      "text/plain": [
       "<Figure size 3000x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Plot Results\n",
    "plt.figure(figsize=(10, 6), dpi=300)\n",
    "plt.plot(hidden_units, train_loss_all, label='Train', marker='o', linewidth=2, markersize=6)\n",
    "plt.plot(hidden_units, test_loss_all, label='Test', marker='o', linewidth=2, markersize=6)\n",
    "plt.xlabel('Model Complexity (Hidden Units in Layers 1)', fontsize=14, fontweight='bold')\n",
    "plt.ylabel('Loss', fontsize=14, fontweight='bold')\n",
    "plt.title('Double Descent, Overfitting and Generalization', fontsize=16, fontweight='bold')\n",
    "plt.legend(fontsize=12)\n",
    "plt.grid(True, linestyle='--', linewidth=0.5)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "\n",
    "overparameterized_index = next(i+1 for i in range(len(hidden_units)) if test_loss_all[i+1] > test_loss_all[i] and test_loss_all[i] < test_loss_all[i-1])\n",
    "# add a vertical line for the overparameterized region\n",
    "plt.axvline(x=hidden_units[overparameterized_index], color='red', linestyle='--', linewidth=2, label='Overparameterized Region')\n",
    "plt.text(hidden_units[overparameterized_index], max(test_loss_all), 'Overparameterized Region', color='red', fontsize=12, fontweight='bold', ha='left')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('double_descent_plot.png', format='png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "358f0472-6389-4534-ac49-19cadea4b76c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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