diff --git a/docs/08_pytorch_paper_replicating.ipynb b/docs/08_pytorch_paper_replicating.ipynb
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--- a/docs/08_pytorch_paper_replicating.ipynb
+++ b/docs/08_pytorch_paper_replicating.ipynb
@@ -1,3576 +1,3578 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e7e81227-aa0c-4e15-9ac4-20cc7128c915",
-   "metadata": {},
-   "source": [
-    "<a href=\"https://colab.research.google.com/github/mrdbourke/pytorch-deep-learning/blob/main/08_pytorch_paper_replicating.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "873828f0-e50f-40b9-9879-f9a01adaa020",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# (WIP) 08. PyTorch Paper Replicating\n",
-    "\n",
-    "TK intro\n",
-    "\n",
-    "Want to recreate ViT paper: \"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale\" - https://arxiv.org/abs/2010.11929 - TK will refer to this as \"ViT paper\" throughout.\n",
-    "\n",
-    "* TK what is ViT?\n",
-    "\n",
-    "* TK - The name Transformer comes from the architecture name in the paper where it was originally introduced, [*Attention is all you need*](https://arxiv.org/abs/1706.03762). An architecture is usually considered a Transformer variant if it uses attention layers in a specific pattern. Since the Transformer architecture originally focused on text data, the goal of the ViT paper was to bring it to the vision.\n",
-    "\n",
-    "* TK - The original transformer was made to work on sequences of text (1D), Vision Transformer turns images into sequences of \"patches\".\n",
-    "\n",
-    "* TK - original ViT also called \"vanilla vision transformer\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ccb53b99-277c-4ac0-a1fb-283c47576be1",
-   "metadata": {},
-   "source": [
-    "## TK - What is paper replicating?\n",
-    "\n",
-    "It's no secret machine learning is advancing fast.\n",
-    "\n",
-    "Many of these advances get published in machine learning research papers.\n",
-    "\n",
-    "And the goal of **paper replicating** is to take replicate these advances with code so you can use the techniques for your own problem.\n",
-    "\n",
-    "For example, let's say a new model architecture gets released that performs better than any other architecture before on various benchmarks, wouldn't it be nice to try that architecture on your own problems?\n",
-    "\n",
-    "* TK image: paper replicating = research paper (language + diagrams + math) -> code (turn language, diagrams and math into usable code) / (translate a research paper into usable code)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "966b353c-c9d8-4568-ad64-c0df45a39442",
-   "metadata": {},
-   "source": [
-    "## TK - What is a machine learning research paper?\n",
-    "\n",
-    "A machine learning research paper is a scientific paper that details findings of a research group on a specific area.\n",
-    "\n",
-    "The contents of a machine learning research paper can vary from paper to paper but they generally follow the structure:\n",
-    "\n",
-    "| **Section** | **Contents** |\n",
-    "| ----- | ----- | \n",
-    "| **Abstract** | An overview/summary of the paper's main findings/contributions. |\n",
-    "| **Introduction** | What's the paper's main problem and what are previous methods used to try and solve it? |\n",
-    "| **Method** | How did the researchers go about conducting their research? For example, what model(s) were used, data sources, training setups, etc. |\n",
-    "| **Results** | What are the outcomes of the paper? If a new type of model or training setup was used, how did the results of findings compare to previous works (this is where **experiment tracking** comes in handy)? |\n",
-    "| **Conclusion** | What are the limitations of the suggested methods? What are some next steps for the research community? |\n",
-    "| **References** | What resources/other papers did the researchers look at to build their own body of work? |\n",
-    "| **Appendix** | Are there any extra resources/findings to look at that weren't included in any of the above sections? |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8ce67f6-0b00-448b-885d-b7d22bce4ff6",
-   "metadata": {},
-   "source": [
-    "## TK - Why replicate a machine learning research paper?\n",
-    "\n",
-    "A machine learning research paper is often a presentation of months of work and experiments done by some of the best machine learning teams in the world condensed into a few pages of text.\n",
-    "\n",
-    "And if these experiments lead to better results in an area related to the problem you're working on, it'd be nice to them out.\n",
-    "\n",
-    "Also, replicating the work of others is a fantastic way to practice your skills.\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-george-hotz-quote.png\" width=600 alt=\"george hotz quote saying to get better at being a machine learning engineer, download a paper, implement it and keep going until you have skills\"/>\n",
-    "\n",
-    "*George Hotz is founder of [comma.ai](https://comma.ai/), a self-driving car company and livestreams machine learning coding on [Twitch](https://www.twitch.tv/georgehotz) and those videos get posted in full to [YouTube](https://www.youtube.com/c/georgehotzarchive). I pulled this quote from one of his livestreams. The \"٭\" is to note that machine learning engineering often involves the extra step(s) of preprocessing data and making your models available for others to use (deployment).*\n",
-    "    \n",
-    "When you first start trying to replicate research papers, you'll likely be overwhelmed.\n",
-    "\n",
-    "That's normal.\n",
-    "\n",
-    "Research teams spend weeks, months and sometimes years creating these works so it makes sense if it takes you sometime to even read let alone reproduce the works.\n",
-    "\n",
-    "Replicating research is such a tough problem, phenomenal machine learning libraries and tools such as, [HuggingFace](https://huggingface.co/), [PyTorch Image Models](https://github.com/rwightman/pytorch-image-models) (`timm` library) and [fast.ai](https://www.fast.ai/) have been born out of making machine learning research more accessible. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b09a7ccf-41e8-4ee4-8d78-aff5f650ca7f",
-   "metadata": {},
-   "source": [
-    "## TK - Where can you find code examples for machine learning research papers?\n",
-    "\n",
-    "One of the first things you'll notice when it comes to machine learning research is: there's a lot of it.\n",
-    "\n",
-    "So beware, trying to stay on top of it is like trying to outrun a hamster wheel.\n",
-    "\n",
-    "Follow your interest, pick a few things that stand out to you.\n",
-    "\n",
-    "In saying this, there are several places to find and read machine learning research papers:\n",
-    "* [arXiv](https://arxiv.org/) - Pronounced \"archive\", arXiv is a free and open resource for reading technical articles on everything from physics to computer science (inlcuding machine learning).\n",
-    "* [Papers with Code](https://paperswithcode.com/) - A curated collection of trending, active and greatest machine learning papers, many of which include code resources attached. Also includes a collection of common machine learning datasets, benchmarks and current state-of-the-art models.\n",
-    "* [AK Twitter](https://twitter.com/ak92501) - The AK Twitter account publishes machine learning research highlights, often with live demos almost every day. I don't understand 9/10 posts but I find it fun to explore every so often.\n",
-    "* [lucidrains' `vit-pytorch` GitHub repository](https://github.com/lucidrains/vit-pytorch) - Less of a place to find research papers and more of an example of what paper replicating with code on a larger-scale looks like. The `vit-pytorch` repository is a collection of Vision Transformer model architectures from various research papers replicated with PyTorch code (much of the inspiration for this notebook was gathered from this repository). \n",
-    "\n",
-    "TK image: showcase the above"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7448de48-ec72-4d63-9948-869a4fc58c04",
-   "metadata": {},
-   "source": [
-    "## TK - What we're going to cover\n",
-    "\n",
-    "TODO\n",
-    "\n",
-    "* ViT -> FoodVision Mini\n",
-    "* Layers = collections of functions to manipulate data -> Architectures = collections of layers (blocks) -> All layers (and blocks) have inputs and outputs\n",
-    "    * Replicating research papers starts by figuring out the inputs and outputs of your layers -> blocks -> model "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf677bb7-719a-447e-a8e8-c4f287146b62",
-   "metadata": {},
-   "source": [
-    "## TK - Where can you get help?\n",
-    "\n",
-    "All of the materials for this course [are available on GitHub](https://github.com/mrdbourke/pytorch-deep-learning).\n",
-    "\n",
-    "If you run into trouble, you can ask a question on the course [GitHub Discussions page](https://github.com/mrdbourke/pytorch-deep-learning/discussions).\n",
-    "\n",
-    "And of course, there's the [PyTorch documentation](https://pytorch.org/docs/stable/index.html) and [PyTorch developer forums](https://discuss.pytorch.org/), a very helpful place for all things PyTorch. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7a8913de-e49e-40c9-89c7-6b847fac9def",
-   "metadata": {},
-   "source": [
-    "## TK 0. Getting setup \n",
-    "\n",
-    "As we've done previously, let's make sure we've got all of the modules we'll need for this section.\n",
-    "\n",
-    "We'll import the Python scripts (such as `data_setup.py` and `engine.py`) we created in [05. PyTorch Going Modular](https://www.learnpytorch.io/05_pytorch_going_modular/).\n",
-    "\n",
-    "To do so, we'll download [`going_modular`](https://github.com/mrdbourke/pytorch-deep-learning/tree/main/going_modular) directory from the `pytorch-deep-learning` repository (if we don't already have it).\n",
-    "\n",
-    "We'll also get the [`torchinfo`](https://github.com/TylerYep/torchinfo) package if it's not available. \n",
-    "\n",
-    "`torchinfo` will help later on to give us a visual representation of our model.\n",
-    "\n",
-    "And since later on we'll be using a newer version of the `torchvision` package (as of June 2022), we'll make sure we've got the latest versions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ebe46d77-6c4d-4102-9994-2cb89f633f18",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch version: 1.12.0+cu102\n",
-      "torchvision version: 0.13.0+cu102\n"
-     ]
-    }
-   ],
-   "source": [
-    "# For this notebook to run with updated APIs, we need torch 1.12+ and torchvision 0.13+\n",
-    "try:\n",
-    "    import torch\n",
-    "    import torchvision\n",
-    "    assert int(torch.__version__.split(\".\")[1]) >= 12, \"torch version should be 1.12+\"\n",
-    "    assert int(torchvision.__version__.split(\".\")[1]) >= 13, \"torchvision version should be 0.13+\"\n",
-    "    print(f\"torch version: {torch.__version__}\")\n",
-    "    print(f\"torchvision version: {torchvision.__version__}\")\n",
-    "except:\n",
-    "    print(f\"[INFO] torch/torchvision versions not as required, installing nightly versions.\")\n",
-    "    !pip3 install -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu113\n",
-    "    import torch\n",
-    "    import torchvision\n",
-    "    print(f\"torch version: {torch.__version__}\")\n",
-    "    print(f\"torchvision version: {torchvision.__version__}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "30caf875-557e-410f-8dff-bd4a9f6c7ae4",
-   "metadata": {},
-   "source": [
-    "> **Note:** If you're using Google Colab, you may have to restart your runtime after running the above cell. After restarting, you can run the cell again and verify you've got the right versions of `torch` and `torchvision`.\n",
-    "\n",
-    "Now we'll continue with the regular imports, setting up device agnostic code and this time we'll also get the [`helper_functions.py`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/helper_functions.py) script from GitHub.\n",
-    "\n",
-    "The `helper_functions.py` script contains several functions we created in previous sections:\n",
-    "* `set_seeds()` to set the random seeds (created in [07. PyTorch Experiment Tracking section 0](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#create-a-helper-function-to-set-seeds)).\n",
-    "* `download_data()` to download a data source given a link (created in [07. PyTorch Experiment Tracking section 1](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#1-get-data)).\n",
-    "* `plot_loss_curves()` to inspect our model's training results (created in [04. PyTorch Custom Datasets section 7.8](https://www.learnpytorch.io/04_pytorch_custom_datasets/#78-plot-the-loss-curves-of-model-0))\n",
-    "\n",
-    "> **Note:** It may be a better idea for many of the functions in the `helper_functions.py` script to be merged into `going_modular/going_modular/utils.py`, perhaps that's an extension you'd like to try.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "960eb156-c1b1-4e76-a812-01bf045835bd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Continue with regular imports\n",
-    "import matplotlib.pyplot as plt\n",
-    "import torch\n",
-    "import torchvision\n",
-    "\n",
-    "from torch import nn\n",
-    "from torchvision import transforms\n",
-    "\n",
-    "# Try to get torchinfo, install it if it doesn't work\n",
-    "try:\n",
-    "    from torchinfo import summary\n",
-    "except:\n",
-    "    print(\"[INFO] Couldn't find torchinfo... installing it.\")\n",
-    "    !pip install -q torchinfo\n",
-    "    from torchinfo import summary\n",
-    "\n",
-    "# Try to import the going_modular directory, download it from GitHub if it doesn't work\n",
-    "try:\n",
-    "    from going_modular.going_modular import data_setup, engine\n",
-    "    from helper_functions import download_data, set_seeds, plot_loss_curves\n",
-    "except:\n",
-    "    # Get the going_modular scripts\n",
-    "    print(\"[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\")\n",
-    "    !git clone https://github.com/mrdbourke/pytorch-deep-learning\n",
-    "    !mv pytorch-deep-learning/going_modular .\n",
-    "    !mv pytorch-deep-learning/helper_functions.py . # get the helper_functions.py script\n",
-    "    !rm -rf pytorch-deep-learning\n",
-    "    from going_modular.going_modular import data_setup, engine\n",
-    "    from helper_functions import download_data, set_seeds, plot_loss_curves"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4f9bdd26-26ac-4756-bd8e-b7a50799f28b",
-   "metadata": {},
-   "source": [
-    "> **Note:** If you're using Google Colab, and you don't have a GPU turned on yet, it's now time to turn one on via `Runtime -> Change runtime type -> Hardware accelerator -> GPU`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "5e246f92-e509-474e-b6c7-c82cf11cb8ca",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'cuda'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "device"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5a695192-2644-4aa7-beab-7222a24b1a1a",
-   "metadata": {},
-   "source": [
-    "## TK 1. Get Data\n",
-    "\n",
-    "Since we're continuing on with FoodVision Mini, let's download the pizza, steak and sushi image dataset we've been using.\n",
-    "\n",
-    "To do so we can use the `download_data()` function from `helper_functions.py` that we created in [07. PyTorch Experiment Tracking section 1](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#1-get-data).\n",
-    "\n",
-    "We'll `source` to the raw GitHub link of the [`pizza_steak_sushi.zip` data](https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip) and the `destination` to `pizza_steak_sushi`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "37b5ffc0-7093-481e-8081-dbdfac4c24f0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INFO] data/pizza_steak_sushi directory exists, skipping download.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "PosixPath('data/pizza_steak_sushi')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Download pizza, steak, sushi images from GitHub\n",
-    "image_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\",\n",
-    "                           destination=\"pizza_steak_sushi\")\n",
-    "image_path"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "55a047b1-9f12-4dcf-8d97-83b7cbb37392",
-   "metadata": {},
-   "source": [
-    "Beautiful! Data downloaded, let's setup the training and test directories."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "92a426b6-df22-4a58-9d5e-b382c73c6048",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Setup directory paths to train and test images\n",
-    "train_dir = image_path / \"train\"\n",
-    "test_dir = image_path / \"test\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "14e84624-6af8-4231-b606-3eabffd172db",
-   "metadata": {},
-   "source": [
-    "## TK 2. Create Datasets and DataLoaders\n",
-    "\n",
-    "Since we've got some data, let's now turn it into `DataLoader`'s.\n",
-    "\n",
-    "To do so we can use the `create_dataloaders()` function in [`data_setup.py`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/going_modular/going_modular/data_setup.py).\n",
-    "\n",
-    "First, we'll create a transform to prepare our images.\n",
-    "\n",
-    "This where one of the first references to the ViT paper will come in.\n",
-    "\n",
-    "In Table 3, the training resolution is mentioned as being 224 (height=224, width=224). \n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-image-size-and-batch-size.png\" width=900 alt=\"Table 3 from the Vision Transformer paper showing the image size and batch size\"/>\n",
-    "\n",
-    "*You can often find various hyperparameter settings listed in a table. In this case we're still preparing our data, so we're mainly concerned with things like image size and batch size. Source: Table 3 in [ViT paper](https://arxiv.org/abs/2010.11929).*\n",
-    "\n",
-    "So we'll make sure our transform resizes our images appropriately.\n",
-    "\n",
-    "And since we'll be training our model from scratch (no transfer learning to begin with), we won't provide a `normalize` transform like we did in [06. PyTorch Transfer Learning section 2.1](https://www.learnpytorch.io/06_pytorch_transfer_learning/#21-creating-a-transform-for-torchvisionmodels-manual-creation).\n",
-    "\n",
-    "### 2.1 Prepare transforms for images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "a45ea650-c3fa-479c-8767-48bc3a1f1267",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Manually created transforms: Compose(\n",
-      "    Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=None)\n",
-      "    ToTensor()\n",
-      ")\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Create image size (from Table 3 in the ViT paper) \n",
-    "IMG_SIZE = 224\n",
-    "\n",
-    "# Create transform pipeline manually\n",
-    "manual_transforms = transforms.Compose([\n",
-    "    transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
-    "    transforms.ToTensor(),\n",
-    "])           \n",
-    "print(f\"Manually created transforms: {manual_transforms}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "437078c2-eb42-471f-8561-94845d0a878d",
-   "metadata": {},
-   "source": [
-    "### 2.2 Turn images into `DataLoader`'s\n",
-    "Transforms created!\n",
-    "\n",
-    "Let's now create our `DataLoader`'s.\n",
-    "\n",
-    "The ViT paper states the use of a batch size of 4096 which is 128x the size of the batch size we've been using (32).\n",
-    "\n",
-    "We're going to stick with a batch size of 32.\n",
-    "\n",
-    "Why?\n",
-    "\n",
-    "Because some hardware (including the free tier of Google Colab) may not be able to handle a batch size of 4096.\n",
-    "\n",
-    "Having a batch size of 4096 means that 4096 images need to fit into the GPU memory at a time.\n",
-    "\n",
-    "This works when you've got the hardware to handle it like a research team from Google often does but when you're running on a single GPU (such as using Google Colab), making sure things work with smaller batch size first is a good idea.\n",
-    "\n",
-    "An extension of this project could be to try a higher batch size value and see what happens.\n",
-    "\n",
-    "> **Note:** We're using the `pin_memory=True` parameter in the `create_dataloaders()` function to speed up computation. `pin_memory=True` avoids unnecessary copying of memory between the CPU and GPU memory by \"pinning\" examples that have been seen before. For more on this concept. Though the benefits of this will likely be seen with larger dataset sizes (our FoodVision Mini dataset is quite small). See the PyTorch [`torch.utils.data.DataLoader` documentation](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) or [Making Deep Learning Go Brrrr from First Principles](https://horace.io/brrr_intro.html) by Horace He for more."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "d0ac8145-f89a-490f-82e3-d4b22225d163",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<torch.utils.data.dataloader.DataLoader at 0x7fa8cc731550>,\n",
-       " <torch.utils.data.dataloader.DataLoader at 0x7fa8cc731520>,\n",
-       " ['pizza', 'steak', 'sushi'])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Set the batch size\n",
-    "BATCH_SIZE = 32 # this is lower than the ViT paper but it's because we're starting small\n",
-    "\n",
-    "# Create data loaders\n",
-    "train_dataloader, test_dataloader, class_names = data_setup.create_dataloaders(\n",
-    "    train_dir=train_dir,\n",
-    "    test_dir=test_dir,\n",
-    "    transform=manual_transforms, # use manually created transforms\n",
-    "    batch_size=BATCH_SIZE\n",
-    ")\n",
-    "\n",
-    "train_dataloader, test_dataloader, class_names"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4a980a5b-0c3b-440d-87f5-c54f1ab143ab",
-   "metadata": {},
-   "source": [
-    "### TK 2.3 Visualize a single image\n",
-    "\n",
-    "Now we've loaded our data, let's *visualize, visualize, visualize!*\n",
-    "\n",
-    "An important step in the ViT paper is preparing the images into patches.\n",
-    "\n",
-    "We'll get to what this means in a second but for now, let's view a single image and its label.\n",
-    "\n",
-    "To do so, let's get a single image and label from a batch of data and inspect their shapes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "b5734a22-ded5-403e-84f5-d7a90ed3f085",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(torch.Size([3, 224, 224]), tensor(0))"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get a batch of images\n",
-    "image_batch, label_batch = next(iter(train_dataloader))\n",
-    "\n",
-    "# Get a single image from the batch\n",
-    "image, label = image_batch[0], label_batch[0]\n",
-    "\n",
-    "# View the batch shapes\n",
-    "image.shape, label"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "898cbb6d-b433-41be-9280-41de129077df",
-   "metadata": {},
-   "source": [
-    "Wonderful!\n",
-    "\n",
-    "Now let's plot the image and its label with `matplotlib`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "afe85fae-38fd-4f34-a52c-29d02cce09c1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot image with matplotlib\n",
-    "plt.imshow(image.permute(1, 2, 0)) # rearrange image dimensions to suit matplotlib [color_channels, height, width] -> [height, width, color_channels]\n",
-    "plt.title(class_names[label])\n",
-    "plt.axis(False);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b8416fa-fb0c-4276-8405-12531ba78b71",
-   "metadata": {},
-   "source": [
-    "Nice!\n",
-    "\n",
-    "Looks like our images are importing correctly, let's continue with the paper replication."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "13bfb028-1afa-44ec-89e9-8890975311ea",
-   "metadata": {},
-   "source": [
-    "## TK 3. Replicating the ViT paper: an overview\n",
-    "\n",
-    "Before we write anymore code, let's discuss what we're doing.\n",
-    "\n",
-    "We'd like to replicate the ViT paper for our own problem, FoodVision Mini.\n",
-    "\n",
-    "So our inputs are: images of pizza, steak and sushi.\n",
-    "\n",
-    "And our ideal model outputs are: predicted labels of pizza, steak or sushi.\n",
-    "\n",
-    "No different to what we've been doing throughout the previous sections.\n",
-    "\n",
-    "The question is: how do we go from our inputs to the desired outputs?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e7f0a12-cce9-45d0-a572-4ad612a98735",
-   "metadata": {},
-   "source": [
-    "### 3.1 Inputs and outputs, layers and blocks\n",
-    "\n",
-    "ViT is a deep learning neural network architecture.\n",
-    "\n",
-    "And any neural network architecture is generally comprised of **layers**.\n",
-    "\n",
-    "And a collection of layers is often referred to as a **block**.\n",
-    "\n",
-    "And stacking many blocks together is what gives us the whole architecture.\n",
-    "\n",
-    "A **layer** takes an input (say an image tensor), performs some kind of function on it (for example what's in the layer's `forward()` method) and then returns an output.\n",
-    "\n",
-    "So if a **single layer** takes an input and gives an output, then a collection of layers or a **block** also takes an input and gives an output.\n",
-    "\n",
-    "Let's make this concrete:\n",
-    "* **Layer** - takes an input, performs a function on it, returns an output.\n",
-    "* **Block** - a collection of layers, takes an input, performs a series of functions on it, returns an output.\n",
-    "* **Architecture (or model)** - a collection of blocks, takes an input, performs a series of functions on it, returns an output.\n",
-    "\n",
-    "This ideology is what we're going to be using to replicate the ViT paper.\n",
-    "\n",
-    "We're going to take it layer by layer, block by block, function by function putting the pieces of the puzzle together like Lego to get our desired overall architecture.\n",
-    "\n",
-    "The reason we do this is because looking at a whole research paper can be intimidating.\n",
-    "\n",
-    "So for a better understanding, we'll break it down, starting with the inputs and outputs of single layer and working up to the inputs and outputs of the whole model.\n",
-    "\n",
-    "TK image: stacking the network together like lego (functions + layers + blocks = model)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c2852f3f-61f0-4dad-ae8c-49db54e28470",
-   "metadata": {},
-   "source": [
-    "### 3.2 Getting specific: What's ViT made of?\n",
-    "\n",
-    "There are many little details about the ViT model sprinkled throughout the paper.\n",
-    "\n",
-    "Finding them all is like one big treasure hunt!\n",
-    "\n",
-    "Remember, a research paper is often months of work compressed into a few pages so it's understandable for it to take of practice to replicate.\n",
-    "\n",
-    "However, the main three resources we'll be looking at for the architecture design are:\n",
-    "1. **Figure 1** - This gives an overview of the model in a graphical sense, you could *almost* recreate the architecture with this figure alone.\n",
-    "2. **Four equations in section 3.1** - These equations give a little bit more of a mathematical grounding to the coloured blocks in Figure 1.\n",
-    "3. **Table 1** - This table shows the various hyperparameter settings (such as number of layers and number of hidden units) for different ViT model variants. We'll be focused on the smallest version, ViT-Base."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bac7c7d2-e58b-47b1-ae3e-7ae9f922326d",
-   "metadata": {},
-   "source": [
-    "#### TK 3.2.1 Exploring Figure 1\n",
-    "\n",
-    "Let's start by going through Figure 1 of the ViT Paper.\n",
-    "\n",
-    "The main things we'll be paying attention to are:\n",
-    "1. **Layers** - takes an **input**, performs an operation or function, produces an **output**.\n",
-    "2. **Blocks** - a collection of layers, which in turn also takes an **input** and produces an **output**.\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-figure-1-inputs-and-outputs.png\" width=900 alt=\"figure 1 from the original vision transformer paper\"/>\n",
-    "\n",
-    "*Figure 1 from the ViT Paper showcasing the different inputs, outputs, layers and blocks that create the architecture. Our goal will be to replicate each of these using PyTorch code.* \n",
-    "\n",
-    "The ViT architecture is comprised of several stages:\n",
-    "* **Patch + Position Embedding (inputs)** - Turns the input image into a sequence of image patches and add a position number what order the patch comes in.\n",
-    "* **Linear projection of flattened patches (Embedded Patches)** - The image patches get turned into an **embedding**, the benefit of using an embedding rather than just the image values is that an embedding is a *learnable* representation (typically in the form of a vector) of the image that can improve with training.\n",
-    "* **Norm** - This is short for \"[Layer Normalization](https://paperswithcode.com/method/layer-normalization)\" or \"LayerNorm\", a technique for regularizing (reducing overfitting) a neural network, you can use LayerNorm via the PyTorch layer [`torch.nn.LayerNorm()`](https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html).\n",
-    "* **Multi-Head Attention** - This is a [Multi-Headed Self-Attention layer](https://paperswithcode.com/method/multi-head-attention) or \"MSA\" for short. You can create an MSA layer via the PyTorch layer [`torch.nn.MultiheadAttention()`](https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html).\n",
-    "* **MLP (or [Multilayer perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron))** - A MLP can often refer to any collection of feedforward layers (or in PyTorch's case, a collection of layers with a `forward()` method). In the ViT Paper, the authors refer to the MLP as \"MLP block\" and it contains two [`torch.nn.Linear()`](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) layers with a [`torch.nn.GELU()`](https://pytorch.org/docs/stable/generated/torch.nn.GELU.html) non-linearity activation in between them (section 3.1) and a [`torch.nn.Dropout()`](https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html) layer after each (Appendex B.1). \n",
-    "* **Transformer Encoder** - The Transformer Encoder, is a collection of the layers listed above. There are two skip connections inside the Transformer encoder (the \"+\" symbols) meaning the layer's inputs are fed directly to immediate layers as well as subsequent layers. The overall ViT architecture is comprised of a number of Transformer encoders stacked on top of eachother.\n",
-    "* **MLP Head** - This is the output layer of the architecture, it converts the learned features of an input to a class output. Since we're working on image classification, you could also call this the \"classifier head\". The structure of the MLP Head is similar to the MLP block.\n",
-    "\n",
-    "You might notice that many of the pieces of the ViT architecture can be created with existing PyTorch layers.\n",
-    "\n",
-    "This is because of how PyTorch is designed, it's one of the main purposes of PyTorch to create reusable neural network layers for both researchers and machine learning practitioners.\n",
-    "\n",
-    "> **Question:** Why not code everything from scratch?\n",
-    ">\n",
-    "> You could definitely do that by reproducing all of the math equations from the paper with custom PyTorch layers and that would certainly be an educative exercise, however, using pre-existing PyTorch layers is usually favoured as pre-existing layers have often been extensively tested and performance checked to make sure they run correctly and fast. \n",
-    "\n",
-    "> **Note:** We're going to focused on write PyTorch code to create these layers, for the background on what each of these layers does, I'd suggest reading the ViT Paper in full or reading the linked resources for each layer.\n",
-    "\n",
-    "Let's take Figure 1 and adapt it to our FoodVision Mini problem of classifying images of food into pizza, steak or sushi.\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-figure-1-inputs-and-outputs-food-mini.png\" width=900 alt=\"figure 1 from the original vision transformer paper adapted to work with food images, an image of pizza goes in and gets classified as 'pizza'\"/>\n",
-    "\n",
-    "*Figure 1 from the ViT Paper adapted for use with FoodVision Mini. An image of food goes in (pizza), the image gets turned into patches and then projected to an embedding. The embedding then travels through the various layers and blocks and (hopefully) the class \"pizza\" is returned.*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "add31aa8-2809-4f77-b94c-e34d865c12d0",
-   "metadata": {},
-   "source": [
-    "#### TK - 3.2.2 Exploring the Four Equations\n",
-    "\n",
-    "The next main part(s) of the ViT paper we're going to look at are the four equations in section 3.1.\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-four-equations.png\" width=650 alt=\"four mathematical equations from the vision transformer machine learning paper\"/>\n",
-    "\n",
-    "*These four equations represent the math behind the four major parts of the ViT architecture.*\n",
-    "\n",
-    "Section 3.1 describes each of these (some of the text has been omitted for brevity, bolded text is mine):\n",
-    "\n",
-    "| **Equation number** | **Description from ViT paper section 3.1** | \n",
-    "| ----- | ----- | \n",
-    "| 1 | ...The Transformer uses constant latent vector size $D$ through all of its layers, so we flatten the patches and map to $D$ dimensions with a **trainable linear projection** (Eq. 1). We refer to the output of this projection as the **patch embeddings**. |\n",
-    "| 2 | The Transformer encoder (Vaswani et al., 2017) consists of alternating layers of multiheaded selfattention (MSA, see Appendix A) and MLP blocks (Eq. 2, 3). **Layernorm (LN) is applied before every block**, and **residual connections after every block** (Wang et al., 2019; Baevski & Auli, 2019). |\n",
-    "| 3 | See above. |\n",
-    "| 4 | Similar to BERT's [ class ] token, we **prepend a learnable embedding to the sequence of embedded patches** $\\left(\\mathbf{z}_{0}^{0}=\\mathbf{x}_{\\text {class }}\\right)$, whose state at the output of the Transformer encoder $\\left(\\mathbf{z}_{L}^{0}\\right)$ serves as the image representation $\\mathbf{y}$ (Eq. 4)... |\n",
-    "\n",
-    "Let's map these descriptions to the ViT architecture in Figure 1.\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-mapping-the-four-equations-to-figure-1.png\" width=1000 alt=\"mapping the vision transformer paper figure 1 to the four equations listed in the paper\"/>\n",
-    "\n",
-    "*Connecting Figure 1 from the ViT paper to the four equations from section 3.1 describing the math behind each of the layers/blocks. Some details such as \"residual connections after every block\" are referred to in Figure 1 and in the text but not in the equations.*\n",
-    "\n",
-    "There's a lot happening in the image above but following the coloured lines and arrows reveals the main concepts of the ViT architecture.\n",
-    "\n",
-    "How about we break down each equation further (it will be our goal to recreate these with code)?\n",
-    "\n",
-    "In all equations (except equation 4), \"$\\mathbf{z}$\" is the raw output of a particular layer:\n",
-    "\n",
-    "1. $\\mathbf{z}_{0}$ is \"z zero\" (this is the output of the initial patch embedding layer)\n",
-    "2. $\\mathbf{z}_{\\ell}^{\\prime}$ is \"z of a particular layer *prime*\" (or an intermediary value of z)\n",
-    "3. $\\mathbf{z}_{\\ell}$ is \"z of a particular layer\"\n",
-    "\n",
-    "And $\\mathbf{y}$ is the overall output of the architecture.\n",
-    "\n",
-    "**Equation 1**\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\mathbf{z}_{0} &=\\left[\\mathbf{x}_{\\text {class }} ; \\mathbf{x}_{p}^{1} \\mathbf{E} ; \\mathbf{x}_{p}^{2} \\mathbf{E} ; \\cdots ; \\mathbf{x}_{p}^{N} \\mathbf{E}\\right]+\\mathbf{E}_{\\text {pos }}, & & \\mathbf{E} \\in \\mathbb{R}^{\\left(P^{2} \\cdot C\\right) \\times D}, \\mathbf{E}_{\\text {pos }} \\in \\mathbb{R}^{(N+1) \\times D}\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "This equation deals with the class token, patch embedding and position embedding ($\\mathbf{E}$ is for embedding) of the input image.\n",
-    "\n",
-    "In vector form, the embedding might look something like:\n",
-    "\n",
-    "TK - update the vector form to reflect a real exmaple\n",
-    "\n",
-    "```python\n",
-    "x_input = [class_token, image_patch_1, image_patch_2, image_patch_3...] + [class_token_position, image_patch_1_position, image_patch_2_position, image_patch_3_position...]\n",
-    "```\n",
-    "\n",
-    "Where each of the elements in the vector is learnable (their `requires_grad=True`).\n",
-    "\n",
-    "**Equation 2**\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\mathbf{z}_{\\ell}^{\\prime} &=\\operatorname{MSA}\\left(\\operatorname{LN}\\left(\\mathbf{z}_{\\ell-1}\\right)\\right)+\\mathbf{z}_{\\ell-1}, & & \\ell=1 \\ldots L\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "This says that for every layer from $1$ through to $L$ (the total number of layers), there's a Multi-Head Attention layer (MSA) wrapping a LayerNorm layer (LN).\n",
-    "\n",
-    "The addition on the end is the equivalent of adding the input to the output and forming a [skip/residual connection](https://paperswithcode.com/method/residual-connection).\n",
-    "\n",
-    "We'll call this layer the \"MSA block\".\n",
-    "\n",
-    "In pseudocode, this might look like: \n",
-    "\n",
-    "```python\n",
-    "x_output_MSA_block = MSA_layer(LN_layer(x_input)) + x_input\n",
-    "```\n",
-    "\n",
-    "Notice the skip connection on the end (adding the input of the layers to the output of the layers).\n",
-    "\n",
-    "**Equation 3**\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\mathbf{z}_{\\ell} &=\\operatorname{MLP}\\left(\\operatorname{LN}\\left(\\mathbf{z}_{\\ell}^{\\prime}\\right)\\right)+\\mathbf{z}_{\\ell}^{\\prime}, & & \\ell=1 \\ldots L \\\\\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "This says that for every layer from $1$ through to $L$ (the total number of layers), there's also a Multilayer Perceptron layer (MLP) wrapping a LayerNorm layer (LN).\n",
-    "\n",
-    "The addition on the end is showing the presence of a skip/residual connection.\n",
-    "\n",
-    "We'll call this layer the \"MLP block\".\n",
-    "\n",
-    "In pseudocode, this might look like: \n",
-    "\n",
-    "```python\n",
-    "x_output_MLP_block = MLP_layer(LN_layer(x_output_MSA_block)) + x_output_MSA_block\n",
-    "```\n",
-    "\n",
-    "Notice the skip connection on the end (adding the input of the layers to the output of the layers).\n",
-    "\n",
-    "**Equation 4**\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\mathbf{y} &=\\operatorname{LN}\\left(\\mathbf{z}_{L}^{0}\\right) & &\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "This says for the last layer $L$, the output $y$ is the 0 index token of $z$ wrapped in a LayerNorm layer (LN).\n",
-    "\n",
-    "Or in our case, the 0 index of `x_output_MLP_block`:\n",
-    "\n",
-    "```python\n",
-    "y = LN_layer(Linear_layer(x_output_MLP_block[0]))\n",
-    "```\n",
-    "\n",
-    "Of course there are some simplifications above but we'll take care of those when we start to write PyTorch code for each section.\n",
-    "\n",
-    "> **Note:** The above section covers alot of information. But don't forget if something doesn't make sense, you can always research it further. By asking questions like \"what is a residual connection?\"."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cd36899e-5bc7-411a-aab7-28e3a5a2c6cb",
-   "metadata": {},
-   "source": [
-    "#### TK - 3.2.3 Exploring Table 1\n",
-    "\n",
-    "The final piece of the ViT architecture puzzle we'll focus on (for now) is Table 1.\n",
-    "\n",
-    "| Model | Layers | Hidden size $D$ | MLP size | Heads | Params |\n",
-    "| :--- | :---: | :---: | :---: | :---: | :---: |\n",
-    "| ViT-Base | 12 | 768 | 3072 | 12 | $86M$ |\n",
-    "| ViT-Large | 24 | 1024 | 4096 | 16 | $307M$ |\n",
-    "| ViT-Huge | 32 | 1280 | 5120 | 16 | $632M$ |\n",
-    "\n",
-    "<div align=center>\n",
-    "    <i>Table 1: Details of Vision Transformer model variants. Source: <a href=\"https://arxiv.org/abs/2010.11929\">ViT paper</a>.</i>\n",
-    "</div>\n",
-    "<br>\n",
-    "\n",
-    "This table showcasing the various hyperparameters of each of the ViT architectures.\n",
-    "\n",
-    "You can see the numbers gradually increase from ViT-Base to ViT-Huge.\n",
-    "\n",
-    "We're going to focus on replicating ViT-Base (start small and scale up when necessary) but we'll be writing code that could easily scale up to the larger variants.\n",
-    "\n",
-    "Breaking the hyperparameters down:\n",
-    "* **Layers** - How many Transformer encoder blocks are there? (each of these will contain a MSA block and MLP block)\n",
-    "* **Hidden size $D$** - This is the embedding dimension throughout the architecture, this will be the size of the vector that our image gets turned into when it gets patched and embedded. Generally, the larger the embedding dimension, the more information can be captured, the better results. However, a larger embedding comes at the cost of more compute.\n",
-    "* **MLP size** - What are the number of hidden units in the MLP layers?\n",
-    "* **Heads** - How many heads are there in the Multi-Head Attention layers?\n",
-    "* **Params** - What are the total number of parameters of the model? Generally, more parameters leads to better performance but at the cost of more compute. You'll notice even ViT-Base has far more parameters than any other model we've used so far.\n",
-    "\n",
-    "We'll use these values as the hyperparameter settings for our ViT architecture. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9aedd15-5a98-431e-bd9e-9d18616e4bff",
-   "metadata": {},
-   "source": [
-    "### TK - 3.3 My workflow for replicating papers\n",
-    "\n",
-    "When I start working on replicating a paper, I go through the following steps:\n",
-    "\n",
-    "1. Read the whole paper end-to-end once (to get an idea of the main concepts).\n",
-    "2. Go back through each section and see how they line up with each other and start thinking about how they might be turned into code (just like above).\n",
-    "3. Repeat step 2 until I've got a fairly good outline.\n",
-    "4. Use [mathpix.com](https://mathpix.com/) (a very handy tool) to turn any sections of the paper into markdown/LaTeX to put into notebooks.\n",
-    "5. Replicate the simplest version of the model possible.\n",
-    "6. If I get stuck, look up other examples.\n",
-    "\n",
-    "TK - gif of mathpix\n",
-    "\n",
-    "We've already gone through the first few steps above (and if you haven't read the full paper yet, I'd encourage you to give it a go) but what we'll be focusing on next is step 5: replicating the simplest version fo the model possible.\n",
-    "\n",
-    "This is why we're starting with ViT-Base.\n",
-    "\n",
-    "Replicating the smallest version of the architecture possible, get it working and then we can scale up if we wanted to.\n",
-    "\n",
-    "> **Note:** If you've never read a research paper before, many of the above steps can be intimidating. But don't worry, like anything, your skills at reading *and* replicating papers will improve with practice. Don't forget, a research paper is often *months* of work by many people compressed into a few pages. So trying to replicate it on your own is no small feat. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9f1717f5-f6bc-4cce-b5eb-093822da988d",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## TK 4. Equation 1: Split data into patches and creating the class, position and patch embedding \n",
-    "\n",
-    "I remember one of my machine learning engineer friends used to say \"it's all about the embedding.\"\n",
-    "\n",
-    "As in, if you can represent your data in a good, learnable way (as embeddings are learnable representations), chances are a learning algorithm will be able to perform well on them.\n",
-    "\n",
-    "So with that being said, let's start by creating the class, position and patch embeddings for the ViT architecture.\n",
-    "\n",
-    "We'll start with the **patch embedding**.\n",
-    "\n",
-    "This means we'll be turning our input images in a sequence of patches and then embedding those patches.\n",
-    "\n",
-    "Recall that an **embedding** is a learnable representation of some form and is often a vector. The term learnable is important because this means the representation of an input image can be improved and learned over time.\n",
-    "\n",
-    "We'll begin by following the opening paragraph of section 3.1 of the ViT paper (bold mine):\n",
-    "\n",
-    "> The standard Transformer receives as input a 1D sequence of token embeddings. To handle 2D images, we reshape the image $\\mathbf{x} \\in \\mathbb{R}^{H \\times W \\times C}$ into a sequence of flattened 2D patches $\\mathbf{x}_{p} \\in \\mathbb{R}^{N \\times\\left(P^{2} \\cdot C\\right)}$, where $(H, W)$ is the resolution of the original image, $C$ is the number of channels, $(P, P)$ is the resolution of each image patch, and $N=H W / P^{2}$ is the resulting number of patches, which also serves as the effective input sequence length for the Transformer. The Transformer uses constant latent vector size $D$ through all of its layers, so we flatten the patches and map to $D$ dimensions with a trainable linear projection (Eq. 1). We refer to the output of this projection as the **patch embeddings**.\n",
-    "\n",
-    "And size we're dealing with image shapes, let's keep in mind the line from Table 3 of the ViT paper: \n",
-    "\n",
-    "> Training resolution is **224**.\n",
-    "\n",
-    "Let's break down the text above.\n",
-    "\n",
-    "* $D$ is the size of the **patch embeddings**, different values for $D$ can be found in Table 1.\n",
-    "* The image starts as 2D with size ${H \\times W \\times C}$.\n",
-    "* The image gets converted to a sequence of flattened 2D patches with size ${N \\times\\left(P^{2} \\cdot C\\right)}$.\n",
-    "    * $(H, W)$ is the resolution of the original image.\n",
-    "    * $C$ is the number of channels.\n",
-    "    * $(P, P)$ is the resolution of each image patch (**patch size**).\n",
-    "    * $N=H W / P^{2}$ is the resulting number of patches, which also serves as the effective input sequence length for the Transformer.\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-annotated.png\" width=900 alt=\"mapping the vit architecture diagram positional and patch embeddings portion to the relative mathematical equation describing what's going on\"/>\n",
-    "\n",
-    "*Mapping the patch and position embedding portion of the ViT architecture from Figure 1 to Equation 1. The opening paragraph of section 3.1 describes the different input and output shapes of the patch embedding layer.*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2010c168-88c7-4045-8c02-8e759ffacef8",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### TK - 4.1 Calculating patch embedding input and output shapes by hand\n",
-    "\n",
-    "How about we start by calculating these input and output shape values by hand?\n",
-    "\n",
-    "To do so, let's create some variables to mimic each of the terms (such as $H$, $W$ etc) above.\n",
-    "\n",
-    "We'll use a patch size ($P$) of 16 since it's the best performing version of ViT-Base uses (see column \"ViT-B/16\" of Table 5 in the ViT paper for more)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "bb10d7f1-1aca-416f-b5a4-3abe722ff207",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of patches (N) with image height (H=224), width (W=224) and patch size (P=16): 196\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Create example values\n",
-    "height = 224 # H (\"The training resolution is 224.\")\n",
-    "width = 224 # W\n",
-    "color_channels = 3 # C\n",
-    "patch_size = 16 # P\n",
-    "\n",
-    "# Calculate N (number of patches)\n",
-    "number_of_patches = int((height * width) / patch_size**2)\n",
-    "print(f\"Number of patches (N) with image height (H={height}), width (W={width}) and patch size (P={patch_size}): {number_of_patches}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0e5f118e-e828-498a-abe1-0de8a5d90cd5",
-   "metadata": {},
-   "source": [
-    "We've got the number of patches, how about we create the image output size as well?\n",
-    "\n",
-    "Better yet, let's replicate the input and output shapes of the patch embedding layer.\n",
-    "\n",
-    "Recall:\n",
-    "\n",
-    "* **Input:** The image starts as 2D with size ${H \\times W \\times C}$.\n",
-    "* **Output:** The image gets converted to a sequence of flattened 2D patches with size ${N \\times\\left(P^{2} \\cdot C\\right)}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "1f684bab-1e4e-4251-99b7-839b0b69dbd3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input shape (2D image): (224, 224, 3)\n",
-      "Output shape (flattened 2D patches): (196, 768)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Input shape\n",
-    "input_shape = (height, width, color_channels)\n",
-    "\n",
-    "# Output shape\n",
-    "output_shape = (number_of_patches, patch_size**2 * color_channels)\n",
-    "\n",
-    "print(f\"Input shape (2D image): {input_shape}\")\n",
-    "print(f\"Output shape (flattened 2D patches): {output_shape}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "addb44a8-dd44-4ad4-8641-5e8c6f49753b",
-   "metadata": {},
-   "source": [
-    "Input and output shapes acquired!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7ee6c9bf-e40f-4511-9325-498251f5b998",
-   "metadata": {},
-   "source": [
-    "### TK - 4.2 Turning a single image into patches\n",
-    "\n",
-    "Now we know the ideal input and output shapes for our **patch embedding** layer.\n",
-    "\n",
-    "What we're doing here is breaking the overall architecture down into smaller pieces, focusing on the inputs and outputs of individual layers.\n",
-    "\n",
-    "So how do we create the patch embedding layer?\n",
-    "\n",
-    "We'll get to that shortly, first, let's *visualize, visualize, visualize!* what it looks like to turn an image into patches.\n",
-    "\n",
-    "Let's start with our single image."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "336e1b36-9849-4104-8cb9-bb64a20ffc48",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# View single image\n",
-    "plt.imshow(image.permute(1, 2, 0)) # adjust for matplotlib\n",
-    "plt.title(class_names[label])\n",
-    "plt.axis(False);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c7ebc6e5-c601-49fa-a185-05a50fdc81cc",
-   "metadata": {},
-   "source": [
-    "We want to turn this image into patches of itself inline with Figure 1 of the ViT paper.\n",
-    "\n",
-    "How about we start by just visualizing the top row of patched pixels?\n",
-    "\n",
-    "We can do this by indexing on the different image dimensions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "bcd2e784-7989-40e5-b8f5-64de18f1fe3d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x1152 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Change image shape to be compatible with matplotlib (color_channels, height, width) -> (height, width, color_channels) \n",
-    "image_permuted = image.permute(1, 2, 0)\n",
-    "\n",
-    "# Index to plot the top row of patched pixels\n",
-    "patch_size = 16\n",
-    "plt.figure(figsize=(patch_size, patch_size))\n",
-    "plt.imshow(image_permuted[:patch_size, :, :]);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad0f2977-7c7b-45e5-91a9-a8626e8e73c7",
-   "metadata": {},
-   "source": [
-    "Now we've got the top row, let's turn it into patches.\n",
-    "\n",
-    "We can do this by iterating through the number of patches there'd be in the top row. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "93210158-3dcb-4d1f-b728-c7c9c3df99dd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of patches per row: 14.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x1008 with 14 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Setup hyperparameters and make sure img_size and patch_size are compatible\n",
-    "img_size = 224\n",
-    "patch_size = 16\n",
-    "num_patches = img_size/patch_size \n",
-    "assert img_size % patch_size == 0, \"Image size must be divisible by patch size\" \n",
-    "print(f\"Number of patches per row: {num_patches}\")\n",
-    "\n",
-    "# Create a series of subplots\n",
-    "fig, axs = plt.subplots(nrows=1, \n",
-    "                        ncols=img_size // patch_size, # one column for each patch\n",
-    "                        figsize=(num_patches, num_patches),\n",
-    "                        sharex=True,\n",
-    "                        sharey=True)\n",
-    "\n",
-    "# Iterate through number of patches in the top row\n",
-    "for i, patch in enumerate(range(0, img_size, patch_size)):\n",
-    "    axs[i].imshow(image_permuted[:patch_size, patch:patch+patch_size, :]); # keep height index constant, alter the width index\n",
-    "    axs[i].set_xlabel(i+1) # set the label\n",
-    "    axs[i].set_xticks([])\n",
-    "    axs[i].set_yticks([])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc30f0a2-7344-4a90-b5b7-7a98127c59fd",
-   "metadata": {},
-   "source": [
-    "Those are some nice looking patches!\n",
-    "\n",
-    "How about we do it for the whole image?\n",
-    "\n",
-    "This time we'll iterate through the indexs for height and width and plot each patch as it's own subplot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "7d45e15a-eb50-4c46-8055-2acaaacb881c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of patches per row: 14.0\n",
-      "Number of patches per column: 14.0\n",
-      "Total patches: 196.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x1008 with 196 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Setup hyperparameters and make sure img_size and patch_size are compatible\n",
-    "img_size = 224\n",
-    "patch_size = 16\n",
-    "num_patches = img_size/patch_size \n",
-    "assert img_size % patch_size == 0, \"Image size must be divisible by patch size\" \n",
-    "print(f\"Number of patches per row: {num_patches}\\nNumber of patches per column: {num_patches}\\nTotal patches: {num_patches*num_patches}\")\n",
-    "\n",
-    "# Create a series of subplots\n",
-    "fig, axs = plt.subplots(nrows=img_size // patch_size, # need int not float\n",
-    "                        ncols=img_size // patch_size, \n",
-    "                        figsize=(num_patches, num_patches),\n",
-    "                        sharex=True,\n",
-    "                        sharey=True)\n",
-    "\n",
-    "# Loop through height and width of image\n",
-    "for i, patch_height in enumerate(range(0, img_size, patch_size)): # iterate through height\n",
-    "    for j, patch_width in enumerate(range(0, img_size, patch_size)): # iterate through width\n",
-    "        \n",
-    "        # Plot the permuted image patch (image_permuted -> (Height, Width, Color Channels))\n",
-    "        axs[i, j].imshow(image_permuted[patch_height:patch_height+patch_size, # iterate through height \n",
-    "                                        patch_width:patch_width+patch_size, # iterate through width\n",
-    "                                        :]) # get all color channels\n",
-    "        \n",
-    "        # Set up label information, remove the ticks for clarity and set labels to outside\n",
-    "        axs[i, j].set_ylabel(i+1, \n",
-    "                             rotation=\"horizontal\", \n",
-    "                             horizontalalignment=\"right\", \n",
-    "                             verticalalignment=\"center\") \n",
-    "        axs[i, j].set_xlabel(j+1) \n",
-    "        axs[i, j].set_xticks([])\n",
-    "        axs[i, j].set_yticks([])\n",
-    "        axs[i, j].label_outer()\n",
-    "\n",
-    "# Set a super title\n",
-    "fig.suptitle(f\"{class_names[label]} -> Patchified\", fontsize=16)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "32a983e8-6d76-4ef0-b0f2-c97a42bdbb08",
-   "metadata": {},
-   "source": [
-    "Image patchified!\n",
-    "\n",
-    "Woah, that looks cool.\n",
-    "\n",
-    "Now how do we turn each of these patches into an embedding and convert them into a sequence?\n",
-    "\n",
-    "Hint: we can use PyTorch layers. Can you guess which?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f774b58d-7095-4272-aba3-fd9a2db4f28f",
-   "metadata": {},
-   "source": [
-    "### TK - 4.3 Creating image patches with `torch.nn.Conv2d()`\n",
-    "\n",
-    "It's time to start moving towards replicating the patch embedding layers with PyTorch.\n",
-    "\n",
-    "To visualize our single image we wrote code to loop through the different height and width dimensions of a single image and plot individual patches.\n",
-    "\n",
-    "This operation is very similar to the convolutional operation we saw in [03. PyTorch Computer Vision section 7.1: Stepping through `nn.Conv2d()`](https://www.learnpytorch.io/03_pytorch_computer_vision/#71-stepping-through-nnconv2d).\n",
-    "\n",
-    "In fact, the authors of the ViT paper mention in section 3.1 that the patch embedding is achievable with a convolutional neural network (CNN):  \n",
-    "\n",
-    "> **Hybrid Architecture.** As an alternative to raw image patches, the input sequence can be formed from feature maps of a CNN (LeCun et al., 1989). In this hybrid model, the patch embedding projection $\\mathbf{E}$ (Eq. 1) is applied to patches extracted from a **CNN feature map**. As a special case, the patches can have spatial size $1 \\times 1$, which means that the **input sequence is obtained by simply flattening the spatial dimensions of the feature map and projecting to the Transformer dimension**. The classification input embedding and position embeddings are added as described above.\n",
-    "\n",
-    "The \"**feature map**\" they're refering to are the weights/activations produced by a convolutional layer passing over a given image.\n",
-    "\n",
-    "<img src=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/images/08-vit-paper-patch-embedding-animation.gif\" width=900 alt=\"example of creating a patch embedding by passing a convolutional layer over a single image\"/>\n",
-    "\n",
-    "*By setting the `kernel_size` and `stride` parameters of a [`torch.nn.Conv2d()`](https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html) layer equal to the `patch_size`, we can effectively get a layer that splits our image into patches and creates a learnable embedding (referred to as a \"Linear Projection\" in the ViT paper) of each patch.* \n",
-    "\n",
-    "Remember our ideal input and output shapes for the patch embedding layer?\n",
-    "\n",
-    "* **Input:** The image starts as 2D with size ${H \\times W \\times C}$.\n",
-    "* **Output:** The image gets converted to a sequence of flattened 2D patches with size ${N \\times\\left(P^{2} \\cdot C\\right)}$.\n",
-    "\n",
-    "Or for an image size of 224 and patch size of 16:\n",
-    "\n",
-    "* **Input (2D image):** (224, 224, 3) \n",
-    "* **Output (flattened 2D patches):** (196, 768)\n",
-    "\n",
-    "We can recreate these with:\n",
-    "* [`torch.nn.Conv2d()`](https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html) for turning our image into patches of CNN feature maps.\n",
-    "* [`torch.nn.Flatten()`](https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html) for flattening the spatial dimensions of the feature map.\n",
-    "\n",
-    "Let's start with the `torch.nn.Conv2d()` layer.\n",
-    "\n",
-    "We can replicate the creation of patches by setting the `kernel_size` and `stride` equal to `patch_size`.\n",
-    "\n",
-    "This means each convolutional kernel will be of size `(patch_size x patch_size)` or if `patch_size=16`, `(16 x 16)` (the equivalent of one whole patch) \n",
-    "\n",
-    "And each step or `stride` of the convolutional kernel will be `patch_size` pixels long or `16` pixels long (equivalent of stepping to the next patch).\n",
-    "\n",
-    "We'll set `in_channels=3` for the number of color channels in our image and we'll set `out_channels=768`, the same as the $D$ value in Table 1 for ViT-Base (this is the embedding dimension, each image will be embedded into a vector of size 768)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "3d4fd046-6b51-4ac0-8d39-e67fb333a18a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch import nn\n",
-    "\n",
-    "# Set the patch size\n",
-    "patch_size=16\n",
-    "\n",
-    "# Create the Conv2d layer with hyperparameters from the ViT paper\n",
-    "conv2d = nn.Conv2d(in_channels=3, # number of color channels\n",
-    "                   out_channels=768, # from Table 1: Hidden size D, this is the embedding size\n",
-    "                   kernel_size=patch_size, # could also use (patch_size, patch_size)\n",
-    "                   stride=patch_size,\n",
-    "                   padding=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03dec513-eea5-4d13-b7ab-41c9e997ef48",
-   "metadata": {},
-   "source": [
-    "Now we've got a convoluational layer, let's see what happens when we pass a single image through it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "1d3424d2-2cfa-431c-9fd0-e2afdf15fc9c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# View single image\n",
-    "plt.imshow(image.permute(1, 2, 0)) # adjust for matplotlib\n",
-    "plt.title(class_names[label])\n",
-    "plt.axis(False);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "a72a5614-f1cb-4c01-9696-24f07bf2a219",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([1, 768, 14, 14])\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Pass the image through the convolutional layer \n",
-    "image_out_of_conv = conv2d(image.unsqueeze(0)) # add a single batch dimension (height, width, color_channels) -> (batch, height, width, color_channels)\n",
-    "print(image_out_of_conv.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "87afeb8f-86cf-4cad-bf88-84dfaccdbe2b",
-   "metadata": {},
-   "source": [
-    "Passing our image through the convolutional layer turns it into a series of 768 (this is the embedding size or $D$) feature/activation maps.\n",
-    "\n",
-    "So its output shape can be read as:\n",
-    "    \n",
-    "```python\n",
-    "torch.Size([1, 768, 14, 14]) -> [batch_size, embedding_dim, feature_map_height, feature_map_width]\n",
-    "```\n",
-    "\n",
-    "Let's visualize five random feature maps and see what they look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "af5b58ca-0d73-4c62-b4af-4e8867b764e2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Showing random convolutional feature maps from indexes: [180, 39, 286, 72, 105]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAq8AAACFCAYAAAB1yRHkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAZqklEQVR4nO3daXRc533f8f8MMIPBYBkABAiCIMBVIsVd+2aZtFWpsiVHcWLJle04teskx7HixHGWkzpNTnwaO00iJzqx6zW1Y7e2oqZypbiRZOlYC8XIWk2JG2guICCSIPYBBpgZzHbzInZe5f7+AFlU5/Z8P29/w3sf3HnuMw/mEL8bC4LAAAAAgCiIv9kDAAAAABaLzSsAAAAig80rAAAAIoPNKwAAACKDzSsAAAAig80rAAAAIqN+KS9ONDQFDen20Hx936h7jFNnV8m82qD/fcxp9qol3CGY1emDdKTnZd5ep3Mzs3JQJ/PhuRUyT8zE9Amc2MysKt7d8uyUVQrzizjK0iVjDUHKmkLz9Tvn3GNMVhplXnTe6Ma6kswz8aI7hjrn6pSduThXS7nnmBjLyLyW1P8+cOZyfMF/i717qjB2ZiIIgi73QBfAmysL69LuMepn9c+4qDVBWcRd4tzuiziA/5L6prLMy0X9g8aqzhCS/iAS4lovzE1ZeWF51pTm9mTQ0Rt+P02/0eoeo5bQQwuckdctOPdaQa85ZmZB1XkTGvUHYKnNn2jrV4zJfK6mz5Gr6HWrXPXH0JrU6+v5I9llW1MSmcYgtSp8XY392H+fau3ha5KZv0+J61vVYjV3CFZzLrM3H9Nr8u45VtbnZD5S1p9PcyV9IYKFRXw/mgy/GJXxrFVz//qasqTNa0O63Xa/7ddD86/f/zn3GB/4vd+Q+cwG/cN6C3Chx3mBmVlrRcbv2/2izO9ue8k9xbmKftM/uu8DMl/9qP4gChYxJ+a7w1904tv+e3WhUtZk18ZuDs2/9Q/73WN8Pbtb5gPz+pegXS1vyPydzYfdMbQ51/i8s4jvy1/inuNrn3+XzPO9eoEqrdDzvfmEf4vXO/v41//qN4fcg1wgb64c/6Mr3WN0f1/fK3lxH5j5m5aq/zuIVZr0++RtbmN6STIzs86r9JcD5wdWyjwxo69DaZ3/C92qR8N/mzr0+F+6//5CdfSm7JP/85rQ/KHfvMU9xtxqZ011bpXWQb3pSR3Ua46ZWS07o1+wTa8ZQ3f4m/Rv/uJfynxf/lKZPzupx3Am1+aO4dbeAZl/dtd3l29NWZWxq774/tC8/t8Mu8eYv/lamWcv0Td0ekSvB8k5f/dabNf3a8aZj7v/9EfuOe7tfFbm/3nkNpnvH14v8+pgszuGYE34unP2U18IzfhvAwAAAIgMNq8AAACIDDavAAAAiAw2rwAAAIgMNq8AAACIjCW1DaR7Crb798P/gu09/+V33GPUOX+V69XalDcXZP7tG7/mjuEDD31M5s996jqZ/68rb3LP0f5j/deEmS6nVaGm/4p8erNfV9J+LHwMdU6Vx8VY6Guy478dfg1PV/y2hmcm9F+83t3zsszvaTkr8+/k9F9Jmpk9NHqFzA8e6Zd5atS/vRa26fc5VtJ/Ct94Vp9jMRVOc/2L6GlaJgt9TXb8d8L/srf/Qf+vcud69TUq69YbK2Wcvwxer+tkzMxqJf0+tOzT1W+Nd/o1g6OHdJvAB2/Rfzn8nWNOc8OIc6HMLPnh86FZ7OXlW1TGJzP2pf9xe2je0eS3zHhtAp0HdIVf8PIhmZf2XO6O4eQ9a2X+H67fJ/OOXI97jnsH7pH5qFPPt2fzcZm/Ze0r7hg+/+pe5xXfdY9xoeKnatbw/vC/YD/zyRvcY3hrQuaEzqvOPiZe8dfclz/9RZm/7fCdMv+HU9vcc/zvo7tkHlT0PqW+QdektG+fcMcw/kZ4/apVw9d2vnkFAABAZLB5BQAAQGSweQUAAEBksHkFAABAZLB5BQAAQGSweQUAAEBksHkFAABAZLB5BQAAQGQs6SEFjfGSbW8KL38/cOca9xhTP9Aly5VL8jI/suerMt/5jY+7Y0gs6FLzudU63/DXp91zvPHv1sm8ktL/PnFcl7MX1vml3NVkeFNyRXdhX5R1mXH74h1fCc1PlzvdY9yw4pTM/33rmMx/f0w/YODvT293x1Ao6qbp7v36d79cn3sKa++f1sc4tELmyaw+/rr3nHTH8NpRXZy+nGJVs8Rs+HWcW+3/fp1f5TykoMUpFO/Q5fqJ51rdMey8a0DmL5+7VObTp/174oO36Jv2iZEtMm9+vFnmDXf5D0oYmw0/Rrm2fN+FJGeq1v+9qdB8bqMu3jczaxnW73Pw0kGZl267WubVT/iF7LGRDpnnqvqDoVj1P7LHBrpkHjTrz45kXBfP/83XbnPH0HjTjPua5VLuaLCRuzaF5g3T/gMC4s7DYaoNzkMMTpVkfv19L7pjuGz/L8i8NuDczzuy7jmKsw0yj83rp9zEckmZV86k3TH82sceD83ubwl/QAzfvAIAACAy2LwCAAAgMti8AgAAIDLYvAIAACAy2LwCAAAgMti8AgAAIDLYvAIAACAyltTzmj3VYt9971vD81t0h52Z2fw23X/28A1fkvkNf/QJmdd16X42M7OOY7rnbr5bd5vNXOcXeCZyugcud6nu0huN647RlhW6H9TMLPO98H7KEV2ne1FGShn749O3h+ZrmrLuMT7XG979Zmb28XN7ZP7oD66SeRD3u/7MmUoT7yzKvJr3b6/2hJ4Hwbbwbkszs/IWPVcHRle6Y4gX3rzfYWM1s0Qu/EIv5n0qtelO5FvfekDmzz2gO4HT/9bvP+1r1Pfjj4p6MsVq+n00M/vmK9fL/PrNutO351dPy7wvpeeamdn9+28JzYLK8s2jSrrOpna3h+eN/jG6/1Z38ebefa3MN/3uEZm/Pq47zM3MgoqeB3/31HUyrzX5/d5ej3nmmF6XnhrfLfPyVt2Xa2bWvJj1dZkEcbNSS3ie7/aP0TKkx19w9hk3/+orMn/gyJXuGOLDelKXu/VnR/zV8PvlX6zV+7HktF6XKpsKMp9t0D2yZmbfOnlNaDa5EN69zDevAAAAiAw2rwAAAIgMNq8AAACIDDavAAAAiAw2rwAAAIgMNq8AAACIDDavAAAAiIwl9bym1hVt89dPhObDfxfe1/VTg7d9TeY33fvrMs++RfevtR91h2DZjbq7LDOoeyPP7fXPkRrTPXAbNuruyPwzq2Xedt+QO4bZ23eEh34d7gXLJIr2ju7DoXk58Dst75vUnYt//+pumdc7NYPBqgV3DOkm/ZrCiYzMf++Oh91z/M2Q7nXctfKczJ8fXqdPcFQUHv5EPPUmdjLGzGrJ8Hx+jX+Mrq3jMn/yictlfuNd4V2CZmYvnet3x/DwsZ0yf9utB2T+xJGt7jlSTbqTcSinex0HZ3UPd3ZumzsGe5P6O2M1s/pi+LmTTq+2mVlp90aZd33ilMz3P7Vd5pkdk+4Ymo+JyW5mc5foDtXVT/jfN52/Xl+LeFUv/nUlnZe9xdXMkvW6g3Q51RcC6zwUfv7xXf62x+sNTt84IfN9f6LX9erNfl9v+zGdT7xV71OSs+4prHZe98nbZTkZZ55slvnsBn8M9XXhP0fMwuca37wCAAAgMti8AgAAIDLYvAIAACAy2LwCAAAgMti8AgAAIDLYvAIAACAy2LwCAAAgMpbU85qda7JH9l8Zmj/0y3/hHmPHX3xC5qX3zsk8PtQk89S07j4zM5vZpPfsyQO6o67vcX/PP66rJS3/33SP60Kr7tqr5fPuGNpeGgnN6uZ1n+DFqARxmyiH9799b8jvk8yN6/649KDup+u7Rffgnhpb4Y5hV7fuWN0/qsf42eff6Z7j+s0nZf7s8/paNa2fkXk+7XcyBovobVwusZpZ/Xx4/jMffM49xoOPvUXm6699Q+bjRf0+ZtIFdwyW1vGxbLfMt67Tc83M7Mfnu2ReKOl7YsuKMZk/P6x7YM3M4mmxNi5jB2ysGlhyNrwbc2ad/1HW91uDMi9W9TGazug1ebbmrykpZ9mNz+sO7FFdf21mZrVG/RlYvUMXgFYP677gDf16HpmZDR7rcV+zXCqpmE1tDn8vSxl/nu6980cyf2lUdz+PXa2Pv3mTf7+v3KE7VkfzrTKf69GdwmZm8wf1ulSd18co3ai70IMFv9N9Yjy8i7xSCf/3fPMKAACAyGDzCgAAgMhg8woAAIDIYPMKAACAyGDzCgAAgMhg8woAAIDIYPMKAACAyGDzCgAAgMhY0kMKLB5Y0BBegPyh+/QDCMzMOo+XZD43qR9C0PF+XTh+eqHPHUOwpijzs3tTMm894Z7CklmdZ36sH8Zw7CO69Tz7Bb+tOnM4vOC3/KAuNL8Ys6WUPXluc2juPYDAzCw5pqdmPLyv/J/zmC6ivqZ/2B1DwjnJn7/9AZl/9cxN7jleHFor885XdTF6Nt8m84YtupDczKytSZfwn3aPcOGCerNiV/h79fC3/Wu45fZTMl/XNCnz0/O6XH5Fo/9AkFSdbp8/9Gj4/WBmtvFdr7vnOFpeJfPslL6vXpjRa8qNO467Y9h/4NLwsKrn6sUI6mJWagn/rqX158IfyPJT2VKjzJPO/Z69XH921U/6a2qpTeer/lGvW3Or/dL38k36s+WK7jMyv2vbd2X+8Qc/7I6hMbd8c8ETq5klc+HXccWt/lzZd2aDzFV5vpnZzmv0w2def2GTO4a1ew7IPPfVNTKf/nnx9JefaBnS71NutfPAC+chBMlxf4up9lMTYh7xzSsAAAAig80rAAAAIoPNKwAAACKDzSsAAAAig80rAAAAIoPNKwAAACKDzSsAAAAiY0k9r6mRsm39zPnQfOA3et1jdL2q+xAnr9C9YSuc/s76gt8vFwzpHtd4WR+jousSzcysrqTHObWtReabv6z7OU/e0+qOofPnwztxB5/QnYUXo5art4Unu0LzRLe+NmZmqQn9Hsxu0+NfqOqpXQn839tKTpef1+NaXcQ5qjndDTm1Q//75LS+Tl0tftffXWtekfkP3SNcuIapqm18IBuaD3xU3ydmZkfO9Mj8cE3nPV0zMm9M6DXLzOyRSx6T+Yfepefr+vSEe47nB3bKfOWeczJf05yVeb7i95TGi2JO15av27N/zah98c/uD83zNX/sj+b09fvm8zfqA+jKS2vf7r+H+X3h66KZ2XyPXnMSeX/trG/Qc+2GjC4q/08Dd8q83OGUbJtZ4+jSKuT/b4q3lS317tHQfPzp1e4x8v0VmdfN67V9vqNB5v2P+5+/z1+yTuaFy519ylzSPUdTUc+n7hV6bTx3tkPm5b4FdwzFmfD9mLqt+eYVAAAAkcHmFQAAAJHB5hUAAACRweYVAAAAkcHmFQAAAJHB5hUAAACRweYVAAAAkbGkMraFrqSd+OU1oXnLoN/zN7VVl6S2nHT6Pff1yTzR7Q7BiluLMv/Dqx+R+We+8V73HGXTP0fNqWBLZZtk3vu07qEzMxuqhL9XpXm/A+6CxXQ/W+cBv6vw/A1OqWJF/951bjoj8+l8ozuGwoK+RjGnc3hrd3gn8k+lO/MyL0/rPt+GaT2GoTOd7hjun3qb84qn3GNcqIWOOjv53rbwF9T7HatruqZlnk7oTsXpr/TLfPhn9XtkZrb30M/KfE/3cZmvSU655yhtLch8cl6vrede1X235t+WltkWPs7RlL8mXajhYod9dOB9ofnVXcPuMdJxp1szodecWL3Oswf9ey3Woi/y/CY931PD/rqdelyP409ff7fMq+v05+Pd177ojuHB2jXua5ZLbTphxYfCNwLlDYuY6M7HTzWtX3D8qO68v+2zr7lD+P7xLTL/85/7lsz/7OSt7jliWd07nH16lczXvl13S08+4Xfqzu8In2+1xvD3im9eAQAAEBlsXgEAABAZbF4BAAAQGWxeAQAAEBlsXgEAABAZbF4BAAAQGWxeAQAAEBlsXgEAABAZS3pIQaxilpwOL98v6159MzOr0/3HNrtZF10XO+r0AeJ+AXHDgC6o//TQ3TKvdDsNxmbWtlGXjs//aIXMJ7brt6Zxwv85a2vDL3aQ9H+GC1YzU33gs2v935kaR3QeONOgsXdB5lNn29wxpLvmZZ6sr8r82MRK9xyXrRyV+YEF8bQHM6uM6WL65na/YL9YWMYHVjiCuFmlOXwururVDyAwMytW9L0yU0jJvNKn5+OvbHvOHcMDQ1fKPFOnHzDwwLmr3XPcseWgzB9+fZfMe3fruTYy4M/X6bGW0Kxadm7KixAzs0Q8fJ7MVxrcY4xVwsduZtbelZN54WW9Zpcz/poa6Klomdf1vVhYuYiCfecBOfGSzj+8U8/3vz58vTuC5PTyzQVPNR3Y1BVibV7ER9+q9ZMyPz/cIfOWAb0mLVzlb71WPKb3KZ86+EGZF7fpNcfMrGW1fp/mN+iHZjQ7a29+pz+G1Q+Hz/mJbPhc5ZtXAAAARAabVwAAAEQGm1cAAABEBptXAAAARAabVwAAAEQGm1cAAABEBptXAAAARMaSel7NzGKiIy2/Vne0mpn1PisKQM2s0qh7K3MbdEnb+kd0L5mZ2dgVuhOw86Du75zc5l+2bKcuvW2e0l17c2v1z1lX8n/vqOZER2hVn/+ixP65vzNMYRE9uQ2T+ucrt+q+w8KE7nRsWqk7XM3M8nN6nuQLuiOvs3fGPcdgVvcFxod0119Z/5gWe7XNHcPeO16T+Sn3CBchHljQGD4fxo51uYcI6vRcWLvlvMxHr5mV+Q+z690xJOr0mnFHi+5o/a+P3eqeY3KLXlN+7eqnZP5X+2+WebxTdyObmaVOhM/HWHn51pS6eM1aG8J7q0/P6fvIzKxc1ffr9GSzzG96xyGZ739hqzuG1vVZmc+t1EWw1Um/z7aU1z9n4nLdnfzlp94u81uue90dw75Dl7uvWS6xRM0aO8P7rQvTek01M9uY0T2v4y164c1dqdeDUzndGWxm1vXh0zL/TN/3Zf6xV97nnmPmMr1nSw/qnvHRir7vYjV/TchuDJ+v1X3h/45vXgEAABAZbF4BAAAQGWxeAQAAEBlsXgEAABAZbF4BAAAQGWxeAQAAEBlsXgEAABAZS+t5jZnVkuHx5i/73Zkn72mVedtRp7PxUac/7T26487MLHNU52ferc/R/7d6jGZmowndJdc6pM9hpn+Oxgm/K3V+h+hw8y/TxRH1bskZ/3emuHN5Yt7lc3ps8+d0p6OZWbyojxF0617MYtm/vRaOZmReyej3OTGr38hSuz9XL2sacV+zXGKlmDUOh3cJNlw15R7jHf1HZP7s6CaZ7117QubPPHSFO4bitoLMPz++V+Z33/yP7jlemeqX+SPndsq8vUf32W5o192WZmYHzodfS9XtfLFiFliqLrzDW2U/dXBktcxv3657XH8wdInMOzb5c7WtUc+TwmvtMq+1+Pdz043jMs/m9GfTms1jMn/pvJ6HZmYNWfclyyaoxK2QDe/LrZ/01+X9B/V7neoI7xw2M6sO62s8fqTXHcPpft3B+gdf+IjM7/yPL7jnGFyt+2YH+3SeP6s/v9Kr/D1h8w/DO3Pj4rEAfPMKAACAyGDzCgAAgMhg8woAAIDIYPMKAACAyGDzCgAAgMhg8woAAIDIYPMKAACAyFhSz2vi/Lz1/kl4H2HxHVe7x2iY0t2Z80792fya8E5IM7MtX5p2x1DqTOsXBA0yLq7wO1aTulLR5np0P2duvT7Hqvv9DrfJ7deFhyX9PlyMZKZka+44HZrv6TzuHuM7X7lF5iVdF2yxeqcP0X8LLbUhJ/Mret6Q+XMvXeaeY/uNgzI/Md4p80LQJPMg7RXimp0qdLmvWS4Nk2Vb/9/PhuZHerrdYzzToHtczw3qa3j+oD5Hrce/hum0KCQ0s6fP6DFu7Rp1zzFb0uvS6An9c9bP6+8qTg7rTkczs6R4idu9fBFWJWftt3ofC80/9Qu/5B6j+Iv6s+P/vLJL5hs2nZf5qaGV7hjmT+v3qNH5+Epm/XW7MKLv5/Im3Yn7Rl6UuZtZ50rnw83M4rN+H+1ySU6ZrX8g/PyJJ593j1HXpa/hqXt1D2y5S3e0xjfovl8zs5YXwvtPzcxmNup//8x94vP/J3J9ek1oGtHvY7pHz8fyhPNBbWa5/vBjqOcK8M0rAAAAIoPNKwAAACKDzSsAAAAig80rAAAAIoPNKwAAACKDzSsAAAAig80rAAAAIoPNKwAAACJjSQ8pCDJpK+65JjQ/u8ffC1dbdUFyYkIPKZHTpbgje9rdMeR7dPFupUm3bec6dCG5mVkwpYuegwbdkt96VBdqD/6xX0BcaRU/R90ylkgPxc1+pTE03vPYgHuIn/nkazL/xvQNMt+YGpP5109f744hndBztS6mr+FH9j7tnuPF6XUy39A5KfNye1bmMWeMZmYTJf2gg2UVMwvqwx/Ycem9r7iHOPO718q8PqOvQffL+l4c37WI3/HPZmRccVbawyV/3aqE31JmZtaop6ulJvV1yPe4Q7BqgzjGMn4V0hSL2TUN4Wvihs8dc4+R+FC/zIOEfnBMoVc/zKK9z/84LTud7XVFpxR+zL+f8yv1G9F6WH+2eEtGMaUftGBmFixiLi2XWjJmc73hn7/Z7+iHUZiZdXxP32wdR/WaUf+qPn68knLHMLZb52VnXSs3+Q+0qHO2MsUOfYz6vP73yVl/DN4Dh8LwzSsAAAAig80rAAAAIoPNKwAAACKDzSsAAAAig80rAAAAIoPNKwAAACKDzSsAAAAiIxYEi+/7jMVi42Y2tHzDwf9Da4Mg6FqOAzNP/r/DXMFiME+wWMwVLEboPFnS5hUAAAB4M/HfBgAAABAZbF4BAAAQGWxeAQAAEBlsXgEAABAZbF4BAAAQGWxeAQAAEBlsXgEAABAZbF4BAAAQGWxeAQAAEBn/BJxAuWBDyWUnAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 864x864 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot random 5 convolutional feature maps\n",
-    "import random\n",
-    "random_indexes = random.sample(range(0, 758), k=5) # pick 5 numbers between 0 and the embedding size\n",
-    "print(f\"Showing random convolutional feature maps from indexes: {random_indexes}\")\n",
-    "\n",
-    "# Create plot\n",
-    "fig, axs = plt.subplots(nrows=1, ncols=5, figsize=(12, 12))\n",
-    "\n",
-    "# Plot random image feature maps\n",
-    "for i, idx in enumerate(random_indexes):\n",
-    "    image_conv_feature_map = image_out_of_conv[:, idx, :, :] # index on the output tensor of the convolutional layer\n",
-    "    axs[i].imshow(image_conv_feature_map.squeeze().detach().numpy())\n",
-    "    axs[i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[]);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b847f2e1-1700-4040-a9aa-df9ab1139cce",
-   "metadata": {},
-   "source": [
-    "Notice how the feature maps all kind of represent the original image, visualizing a few you can see the different major outlines and some major features.\n",
-    "\n",
-    "The important thing to note is that these features may change over time as the neural network learns.\n",
-    "\n",
-    "And because of these, these feature maps can be considered a **learnable embedding** of our image.\n",
-    "\n",
-    "Let's check one out in numerical form."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "94f6f5b9-a1c7-4aa1-9780-7cd06457b2b3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(tensor([[[0.2642, 1.1367, 1.0221, 0.9712, 1.0950, 1.2478, 1.2884, 1.1481,\n",
-       "           0.9640, 0.6204, 0.5996, 0.5855, 0.5560, 0.4992],\n",
-       "          [1.1578, 1.0633, 0.9593, 1.1931, 1.3194, 1.1306, 0.7317, 0.4322,\n",
-       "           0.6025, 0.7246, 0.7891, 0.5383, 0.4786, 0.7098],\n",
-       "          [1.0163, 0.9368, 1.1675, 1.3373, 1.0429, 0.7497, 0.7445, 0.4270,\n",
-       "           0.7430, 0.8750, 0.6105, 0.5147, 1.0207, 0.7852],\n",
-       "          [1.0669, 1.0157, 1.3291, 1.0117, 0.4848, 0.6802, 0.8365, 0.7736,\n",
-       "           0.8618, 0.9144, 0.8926, 0.9795, 0.7475, 0.7585],\n",
-       "          [0.9371, 1.1937, 1.0068, 0.6377, 0.7283, 0.9625, 1.0372, 0.8920,\n",
-       "           0.9372, 0.9034, 0.9683, 0.9405, 0.5958, 0.8740],\n",
-       "          [0.9419, 0.8599, 0.5429, 0.6954, 1.0202, 0.9093, 1.0003, 0.7619,\n",
-       "           0.8472, 0.8062, 0.6418, 0.7741, 0.5791, 0.9816],\n",
-       "          [0.7965, 0.7202, 0.6424, 0.9137, 0.8264, 1.0243, 1.0920, 0.9548,\n",
-       "           0.9166, 0.7937, 0.4675, 0.5346, 0.7774, 1.1001],\n",
-       "          [0.3298, 0.4832, 0.5324, 0.7486, 0.8303, 0.8101, 0.9969, 0.9931,\n",
-       "           1.0058, 0.6002, 0.6643, 0.7254, 0.8453, 1.1323],\n",
-       "          [0.5384, 0.4798, 0.6725, 0.8014, 0.7044, 0.7988, 0.8185, 0.8911,\n",
-       "           0.9720, 0.8939, 0.6234, 0.5674, 0.5775, 1.0011],\n",
-       "          [0.6199, 0.6465, 0.6503, 0.6215, 0.8154, 0.7950, 0.8647, 0.9872,\n",
-       "           0.8513, 0.8833, 0.5799, 0.5914, 0.6936, 1.0554],\n",
-       "          [0.5140, 0.6462, 0.6982, 0.7445, 0.7394, 0.8124, 0.7462, 0.9183,\n",
-       "           0.7471, 0.9436, 0.7147, 0.6396, 0.5795, 1.0201],\n",
-       "          [0.5467, 0.7408, 0.6854, 0.6624, 0.7465, 0.5077, 0.7633, 0.8709,\n",
-       "           1.0026, 0.7276, 0.7847, 0.5811, 0.5521, 1.0318],\n",
-       "          [0.8041, 0.8868, 0.5559, 0.5889, 0.7236, 0.6976, 0.7940, 0.9365,\n",
-       "           0.9110, 0.8182, 0.7013, 0.4890, 0.8364, 1.0031],\n",
-       "          [0.1976, 1.0262, 1.1979, 0.9982, 0.9644, 0.8868, 0.9556, 1.0204,\n",
-       "           1.0060, 0.9586, 0.9351, 0.8819, 0.9290, 0.9289]]],\n",
-       "        grad_fn=<SliceBackward0>),\n",
-       " True)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get a single feature map in tensor form\n",
-    "single_feature_map = image_out_of_conv[:, 0, :, :]\n",
-    "single_feature_map, single_feature_map.requires_grad"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fc7c08ca-a4ef-4350-b471-088b2f12b80e",
-   "metadata": {},
-   "source": [
-    "The `grad_fn` output of the `single_feature_map` and the `required_grad=True` attribute means PyTorch is tracking the gradients of this feature map and it will be updated by gradient descent during training. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "572ae1c5-9488-4882-bdc1-409eef95424e",
-   "metadata": {},
-   "source": [
-    "### TK - 4.4 Flattening the patch embedding with `torch.nn.Flatten()`\n",
-    "\n",
-    "We've turned our image into patch embeddings but they're still in 2D format.\n",
-    "\n",
-    "How do we get them into the desired output shape of the patch embedding layer of the ViT model?\n",
-    "\n",
-    "* **Desried output (flattened 2D patches):** (196, 768) -> ${N \\times\\left(P^{2} \\cdot C\\right)}$\n",
-    "\n",
-    "Let's check the current shape."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "c8219029-6162-4046-8702-0c2cb42f2378",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Current tensor shape: torch.Size([1, 768, 14, 14]) -> [batch, embedding_dim, feature_map_height, feature_map_width]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Current tensor shape\n",
-    "print(f\"Current tensor shape: {image_out_of_conv.shape} -> [batch, embedding_dim, feature_map_height, feature_map_width]\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0160c70b-0fe8-42f9-b6e9-5cac23e06836",
-   "metadata": {},
-   "source": [
-    "Well we've got the 768 part ( $(P^{2} \\cdot C)$ ) but we still need the number of patches ($N$).\n",
-    "\n",
-    "Reading back through section 3.1 of the ViT paper it says (bold mine):\n",
-    "\n",
-    "> As a special case, the patches can have spatial size $1 \\times 1$, which means that the **input sequence is obtained by simply flattening the spatial dimensions of the feature map and projecting to the Transformer dimension**.\n",
-    "\n",
-    "Flattening the spatial dimensions of the feature map hey?\n",
-    "\n",
-    "What layer do we have in PyTorch that can flatten?\n",
-    "\n",
-    "How about [`torch.nn.Flatten()`](https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html )?\n",
-    "\n",
-    "But we don't want to flatten the whole tensor, we only want to flatten the \"spatial dimensions of the feature map\".\n",
-    "\n",
-    "Which in our case is the `feature_map_height` and `feature_map_width` dimensions of `image_out_of_conv`.\n",
-    "\n",
-    "So how about we create a `torch.nn.Flatten()` layer to only flatten those dimensions, we can use the `start_dim` and `end_dim` parameters to set that up?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "ed82899d-7bbc-49f9-a423-8fa6344b8e99",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create flatten layer\n",
-    "flatten = nn.Flatten(start_dim=2, # flatten feature_map_height (dimension 2)\n",
-    "                     end_dim=3) # flatten feature_map_width (dimension 3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "adcf7cfc-2635-4081-9ade-3542f77c47e2",
-   "metadata": {},
-   "source": [
-    "Nice! Now let's put it all together!\n",
-    "\n",
-    "We'll:\n",
-    "1. Take a single image.\n",
-    "2. Put in through the convolutional layer (`conv2d`) to turn the image into 2D feature maps (patch embeddings).\n",
-    "3. Flatten the 2D feature map into a single sequence."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "e3fa363b-1923-4e27-a0b5-980d885fcda2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original image shape: torch.Size([3, 224, 224])\n",
-      "Image feature map shape: torch.Size([1, 768, 14, 14])\n",
-      "Flattened image feature map shape: torch.Size([1, 768, 196])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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YIveHI1M/0miLsQKRA1dbw7YVtE7R2MT9wwlFpHMNKSu0bogxE3PmdAo8vxvJwpKOJ0LwhGnASbjpOjqtaEREyYSWCaUMCl0PbQLD4CmloGRDzoJhnJl9IOeaqllr2V89+XDGqRvF9/3gH+VXfvN3ePbiRC7nI/icl615oVi3eNEUBFJrPv7xj/CX/tKf47/2Z3+K11+7qSHhB4GDUs4brwJIdUPlEJhOJ453txxuD5zuD4Q51PBr2VG6cWx2O3ZXW3bbDtvUMCiVRJgj03HkdDrSn3pCCJS8nB7iEhx5zNvKCsqwIKlKn5FfpT6Qb6rqPc9GuwBErGGukku4KqsxLwir1vU1H8PmSzT70UAfDXGNI1iRtyWXvEjoLuIXWR5LL/n8vRREyShRyGeP+Yi2kguqPP6dUp9c0xax/NmcIc6E/kj/4hkv3nmbd9/6fQ4PD5QCn/mdL/DVr7/Nw2mmCI3TstoyokaBfqZIRUhgrGI6TpS5Aj0yQxwjUyo0dkPIib73KARzAF8KfgowR16/gkYXGpvRrYGoSCnXvzEknr3omcvE4DM+JERWaEY+9prjjY91tLawaSxSgNaCK7VlLxpy0QQPwzCSQsYohyqF8QT3pxMxQCoVeTZS0hhJZyQhQ4yBNNfDfPaBJMBnxTgVpAQlEykX/FzICZSEbmsxjaXdfMic87/8x/9LHPuBL3zxq8SoyEWQS15iHlmR2AvHt4aZWms+/vGP8XM/+zP8/M//JT7+iY9cGOajZb5cLqmgRl485njqubu74+72ntN9v5RuludJiTGG3X7P9fU1212HtRopBTlnwlyh7uHY0/c93vsPgD7n7f5oAGI1Drnkieoczl6Wg7TS9bYlz1Vanz1qNUyB0eacQyolEeoRqb3MOc9Ayjl0raE2FPISRj6CMC8jpS99jpeCj0tPV0sW6wFa80NAqpeuQYF6NJZSHyDqLZAheYqfmA8P3L7zNm///pd5/s5bTMOJFCZSCPSnATXc87FO0RXNcZgpQWJsS6cNxzRTIggjkbEwjyccihaNERJDwaYEp5kXx7cxErZthxKSlGEOkWka6UymazS7XUO7M0Rf0ff7h4lT7/FJ4r0gZBhOc/1+pMCHEa0anDN0jUBKmKeJjMI0LVo35CJxDTQNhCkgk4KoSXuHFYV+mAkxYYxh2zZsrEadIwyFWADBVDLTHBhDIsaE1hmIzHPGh0BKiW674ebpFa51aPchw9qPfuKj/Cd//5e4e+hJyVGLaBFEfARS1hBzASiUUnz0o2/wF//in+fnfv4v8YlPfBRr1Mt2AY+lhMtNshjmcOq5v7vj7vkdh8ORMMd6CotazrDOsd1uub65Yrvd4pxBSmqoNc01FH6oIc0Z+JECuYSAedmE3w6kWUPO1TjPCKyoJQ2lFELVEodY8s36mOodrTU1dJXVgwoErACRlIvxPa6ab38gL4QzMHRpjN/WMFekdb1vBYnq/7x0fasRy8U7ri6xriwg56VInwOCTAwTp7t3uH/3Te6+8SaHd99hOt4T40whcbg/MA4zu3bLXsPrr98w7RPfeOtdYrHsr64QyvC2f4FBMg4BWQoyJTSSivknSIWcPZ0ELSXOOVzTEEJCo6oXp3oquUQW8xi5ff7AfEyEmMlJUUIh+0j0kZ0xpJyY5wPOwKYxCArjOCEFFScwBu0sUgMxIhWkMIMI1Vhd5o3XN+w3mmnUeB8qgCcVOQZSjBSREUqAkOSSyUvEYa1GaVND2VjwQRBTwlrD9dMbuk3LZtfi2g9Z5/zy773Jl3/vTUIU5wJJhe1XLyOW9Kie1FIKXnvthn/tz/40P/tzf4GPffwjaKOWPZJfyk9XL3sOyHKuHrMfuL+75+72nsPhxDyHJTyqpQPXWHb7Hfv9ns12g3MGISAuxILT8cTxoWfoR8jr37zMJz+4cdc9LS4MUr8E3KxkgzVMrf+as3Fqo5FCP96nFGohHpTCwuipHl+8dCgtoJRYyg8X9rWYzMuGxyNwVZYC/mP+vgJD5QOftdQIVQCyZqS5lCWNqOGuzLWGPDwc+ObXv87x/gVWC/rjLS/e+yoyHGhSQMwDKp1IOZAL9EPPu2+/4LobaE3LMByhCD52fc04JTotMFYyikApHiskU050rYOiSEmRBJTsEbnQGs3eNTRtg+0cdw8HvJ8xKWGt4qqzWDT3L3oeTkeGY0Ani7PQGIVVoGRm00iMgZgz6arhar+hdQKr5JJ2FUIqaGRNZZSAkiuyTEDqDFJhjEbHWtPWWhFDWr5DTc6aeQ6k0VOyoggNCoQoyFIrv7ap+WuYR6QwOGewrUOqQikRaw3tpvsD7e87Guev/doXORwDGUURSz2rsJiJrJD3YmRSCK6vdvzpP/3j/OzP/gzf8z0fX0JN6gdf6mFr+LbmVFBraSkGpmHgcDhwd3fP4eHIPHtyfgx/m6Zhf7Vnv9/RbWoBWMjVMAdOxxOn+yN9Py7lD3ne3C+xlYQ8h4yr57z0lB/0muvvxqxlD41agB2t9VKTrIeQlDW0rUDReqpchO9c5rnLJRXlnOPVa3P2bQuVrXzAQB//rfexPL5usnxG0tdrnB+NWlamFSktIevM3B8ZTke+8sUv82u/+Mvcvf8+skRkmenayBtPO3TnUDmR07wmqzy5fp3+ITGPgTTOOG2gQNs2ZOGR/oTIimuTMDvFlARZNXitub3rycYypoj3MzeNoVGKRgo6CftGs1Ud9w8PyJ3GNYbdrkMWePbNWwYf0cKSYgJR0AqUEmz2jq6zaAOJSFTgWgspEH2o2MNCXzQuM8+RQkEtJI9uu6VkQUiQfIZUo5tCQqp6bYUVtK7BREe46wljJKaI1QZrDHEqpJTJOWGMZrNpyElXAE5DDp4YAs41aGE+nHG+9daBlAxFZLIICFT9Yoo8o3zrZru63vHjf+pP8lf+8s/xgz/4qUfDpJzh/nVdGgxASol5mjg8HLh7cctxMcxSVo9cQ9n91Z7r6yvarsU2FqilknkcODw80B9PjMNECgkhZEUdF0ZPNcrHcgmSyo1Vj0hs5ajqc1irz/lkzXGNUWhjUFpXb7nUIpWsoXARZaHYyeWz18OoXITOj1ZZzmYIvMQ5XQ3zg2vNm799eHtxrT9g3GsVRJRMSYXkJ4bjHdPDLafn7/Pum7/P3Yv3eOcb7/DwjfdgnJEloEWizRptMzl7dOOYppnoIURJP0fm4whhiZ5yrS+OMSBFriG+jNzsFF2WPEwB7TS3h5kmR1SjkL4QlMaVQqMVkoJMHjEkGhl4ui20+4bdvkULwfP3T4iYaE2DEgJnoOssQhYQGes02oLrKjgXikeJRJaQUy35pFIqz1oajKzGIaRejBCiz5RUQ1SFRFDRZSUkIXuyzKTsyaWQSyDFGSkMShtyiUvKVK+50RKJIQVQWiKsJmrBtutoTIP4Dib4HY3TR0kssiJ65MXVqAsAooIkN9dX/MRP/mn+xl//q/zoj/4wjTNoJYB8Jjw/QvM81s1KIcXEPI4cHw7c3VXmTzXMurmUlDWUvdpzfb1nu92gjIZSiCkyjTOn44GHwwN+8pT0WH+79FSX4eFaO5RSoc9I6mM4ezZOY86hqjUWbTRamwWJlY8kBCkXSt36/ytPtoaweSm7nBFtan63GtQ5DF1D/SUMLWvpolywgZaLv5aizvWrc0y8/oX6IitDi5IRKRPGnvfffouvfvFzPLz/Nv74Aj+emIeBcH+iKzNSRQygBewFNCkhvKcoiZ888ySJQXK8H5keTlhpkFbh04SxjhATSka0MXStIQsFIdNpKCWh48TeCmKcyDGxUYIrq+mUIoWJWBJRCK6uDLLVmK5wvReUmOhd5OlNQxGWnBIiBYwtmKahSAEiM8QRIwztRqGjoORCSlCURmiLj5U3rmVBLoWlFCr3NoZMCpkSIIVMTpkUEkYbrNGUuRBLIoVaERApY5e9I4rAx1ARfgqCjJEapSRzmNDS0DSOIBWNNZSUkOUxgvznMs4oUs1PygJslBpT15Oh5mFd2/LjP/4n+dt/62/yIz/yRzBG1lrmIyh/NoiXzvtSKLngp5nT4cj97R13d3eMw/hYAxWStnVsr7ZcXW/ZbFqs1eRS8EtXSX/qORyOTOMMOT9665wpRX3Le1iZSIJad1TKXJQ99EI2UNVTOns2Tm3MYrwrp/YRgUUIhCrnkJ31toXgcAZ8ao1icZCrUX4g1C1lecyCgBUuHsX50JKXh82lnz2HuPJs2CkmxtOBw/P3ePFuRVzfffP3EKnH6UyjDSFF4njEELG2oEVByYJUiRgSxlqkdEgp8CkSoqLERKMkndNYI4hJMidq0d5k5hBwG0WUiud3R0oWtEZys3PEJDj1ATH2tMbwpLOUOJPmCZxEK4vWGmsquGKkAKu4fmLZ7gQxwDxlSqrhabPVFNVwHHpSBETGaY1PupaPzEIsLZBTYJxOOGUppWCLAVEIKZJyQkkDyqCkIPlAKYpCIUuBazuK9/hhIvmCiLKWoRKEVEt9UkkkhTgHMhklFFoVtAYtColM9iOSiZKPH844y7qhuTCs8rjJnXN87/d+kp/5mb/Ap3/4j9awTz8ihJee6mW7XNg/3nM6nri/rR5zGiZyLudyhrOW3dUVVzd7NgvrByCGwDyOlY53PDENQz1FOfuRejSUDFyWLi5zzOoFa85YQ1itNdoYtFIYazHO1G6Q5TFSrFS8R89ZSQo1LD0b58VnPn/2vHR1rMazgjgX/5yrjJfn2uPTFwf5yKB66aoK4OIUFjlBqnzTd77xDb74O7/DO1/7Cv3DHXk+QThxs1XsGkuJE+F4D36gayyN05QSkRKkhiLBbizt1iEPB7ROlJjRMrDdarabhm6z43CYOd0OjKPH7A0USfJw8hP9YcaaBqkVMLPbbdg0ho3JOCO52mj8nFBS0RhFt2lpnCGmmRxrpCFUottYpNT4kIhB1etZCrYB5RYjLA1aZHKQ9IdMJrC5ajBGgC8YpWqIq0ALwewDSglyCihZMMqAkEg0oYASkhgCMVVEN5dMSpEQQn0fEYSSZKmWAxlKkcSUUCKjlKJtHFJLpCpIEoiZGA9Mw4cMa8U5dGIJRTmXT6QUXF3v+emf/lf5sX/p02hdQ8THDfcI88u1ZneBNAZfSyaHhwOHw4GxnypjX9SivXOO/f6K/fUV3aardLxSzu1ep+OR0/HIPE0VFDiDIPVNSiUoeWXePJZJVgRWG/NojEZjllzSLP9ff9cvEQpWptDKq13rk/X1XzbO9e9+O6WJcwpeLksni8ktoezq33MptR1N1MeWvIar5xdYo+EKHuXI8e6W/v6WMAw8f+cdfue3fpvPf+5zhGHEKolWGacjymv25gpiQM0jeyfZbDRNp2tPrqRGF1rR7BymqTS4FARZShocIShca9jtW0JKyHuPkgmrWuIcCUoSpoQuljLDEAaMCGAjG2uwVw3WCDadrht63yJSxlmD1IowJspUaKJCq4TQgCxoVXCbpd4cQRmBsgnbGsiOh9ue22cn7u9n2q2k21Hb/XIiTpkYC1JqrJGkFMipkELAGEERkZIlMSdyKuRUiDGTUqKESCmJlDMxJmLK5KIRKAq1z7YgyTmQYkXbc0koaxHUOrm0krbRDKdbTKO+ZX/8sxnn+b+P4dOaP7Zty5/443+MP/fn/yw3N1dos3iQvG68C2SUC5vNheAT4zByf/fAw/0Dw2kk+oSg9ixqY9lst+yuFo9pq8fMMTJOU0VlD0f8NNUc82JDr7nWmg+vPZMfRGGNtdUgF8M0i7FKvYawdVMqvRjjmf+65I9CLiTiCi69RKhgPZ8+4N3yoztcyeLrxRELBW/tuqtMH87eshp1wU8T42kk5QQ5k2Mgp0SOET9PGBH50u98luPz9zm9uOX5u+/z7jffpT/WNr+gNFLBIAJhHDEIrhuNEaC1ZmM1rtEUBUWtzWDg40g4eYyCXWuZksc0iuQEWRUiI6aFtpMoUWisYB4OyNIgMDipSTETxgnbaMLoUQWMEkuJwWKtRhRJHH2tV8aIEAK9sLGKqIduDfsjuSSMrM+VCoSK5BzJWROmxHAKCOGwtqVkCLMn+sJwmAh+2RtZklOmlErQT0aQrSCVRMkSKTRiCYel1LUfOKXan4qilFpmFFmQSkaUylP2UySHyoksJZNmjds0aGUwXYu1jvHUk3f7D2ecSyvEst8fIQlrLJ/+9Kf5y3/l55eSiaKCogvH86IOerlRWTzfOA48rHzZ4wHvPeR6sbS2bDZbrhYCu7YGBMQQmaeJ4+HA8XBgGkbSGsrmteb3Mmgi+FbD1FpjrMWuxrkgr+vvZ6rd6mXXjhL1yJMVgrPXrJWSb/28l8a5GtylOZ6NkpewnMeSzzmVKEsDUKF4z+d+9df56u9+gZwCJSWin8kxIIGxP9Fawf2z95HB098/cHro8UNAZUmSgiIVPlbQw+cZl4/opzt0USAKOWbS5JGO2qisCiElpjEh0TXkM4ren5j6gHQa23ZkGTGt5OqmpbQFJUEm0DJiTUOIgZw8VoJCkWM9UGqJSuPaisCXUJuu86J80bQW10qKyBX1VgYpQCZJSZkSM+ja/D6NE6fjRAkWP0ZKqntqnCLOWUrRxDlhcEghiH1gmAcoinmekRKiE0QLWUZKEVjbYIwlxABLaSp4SEESg8CHTCqi1kZFrZXGCLIIdpsNjZKkOGONpGss2mlc65BKMsVQc+wPZ5w8IquLX5JK8vGPfZSf+7mf5cd+7EdxzqL0Y0uRWAqIK1J5GdWlnJmWZum72zsOD8eKsOZSw1kpaduWq6srdrsdxlXDTCkxzzPH44njw4FxGMiL8kEtD14gn0KcjeeSPKCXMLV6SYuxi7dcjXLlverFIBeCwUq9q7etdv/YLnY+FD4Qvb7M2HmZCbte2su1kgkeubRLWrDAQTJnjvcP/NYv/SJvfu4zxGnEWcM89JQU2W83OKM5rqSCaSKPHsaILZqcEjHFJQqRxCSJAYajZ7AeLSNKREoOjENEm0y3s9idqfnbcpBooVDGsu025PlEQtBtWqYUySWz3zXIFoKf0EWTY0FZcEGgisQISUyRlFLNI6VDL8V5ZTQxFbwPyJxRTtG0GlQgZ1DKIYSi5Mx48sTZ15KdUFAkYYYSDMFLoq/haO97yljQ8gklZsZ+onEtxijGceC+H1DSkaitjTlAbgrCVOwjyYwQkSIK3nuiz8xjYp4qJS+Git5KBFIWckykLOiaDde7HVokDg8josykKHFaY0zNn40uEOcPZ5xSiQWgqRtGSsH1fsef//N/ln/1J3+cbWvRWi5k9XWTrUX+unFXJkvlvM70hyMP9/ecDkfCtHhMatG+bRu2Vzs2u4VgICDnSJw846l/NMyUyDmj1lKBeKy7iqXLQwhRW7yURBuJNpXJY+3CezX1R5na2SKlrgn7SlS/6EY5KxdcINVyQZ8rv1icP+NLueP67weQqrJSnhCVELCEs5cdJYW0/C4pQhLizFu//wXU8B43amQsI6oAMiB1orOeJ1cdcQwc7wfiMCGjYCsV2jn6caDPBZUD0nb02hC9AZ8JfWRKHoqH5HA2U3QgK0XWEt0YlBbEUCghkUX1RJutYwxVRyfOnuE00kiHLJUvqxpbi3AiY1Pk5moLMXF4ODDPc2XhbFqamyv0blMRzhgpqiLcxspa+BcJay2tc8sB7+n7GREzRjjyDCFlpgmKbEAJjClYLZBTYg6Bw91ASVQCfbSUHDgdR4Z+RoiIVBrbFFxrkEuNXMmMJOJ9JJbMPGTmMRB9JiVBSoYQM4mMUjUtS7HqaklA5AwioQQkPzKUgN02yBTpbEsWGalfjqX+mY1TSHEOyYQQtI3jT/2pP8HP/exf4LWne7SuXMeyeMwVCa37LT9utZIJwTP0tS/z+HBgnuYqS7J0gLjGsb++Yn+9X9y+IKdUDfo48HD3QH/sSSmevU+mFoklS0/m0pZ1bufSopZCdDVO4zTa6nPJRBuN1GYJX/WZN7sSE16SE2HlA1/UT1nz65f94GVddW1kRixA0JqHitU7ljNAuzY1F9Z2rgIlkULkzd/7Al/8rV+hLT3YjGsMfZi5udrQtrIigHmmfziQfUIWiVIGIQqKiNtYro1hnD0xe1IuRKlRxZN9OmMFKYJyFi0EJQjGo8dEMM6QQ8bPI8EPNefKGSVh6kdyLOQ5M6UJmWvXhnIWbStFTjWCbduQZk+WFucV3f6am098lM3TJwgpSNNIzAllNDoXMrlKxkiBVlXjh/o103UtGkXXdBirmWJkmCZSEZQokNoAHiMUISuG3iOlJkbB3UNPDAFVJELaqi80z4Sw5JMIhM1oAVM/M6VELILkC8nnxWEZfMiECEILtFRYq2mMQxSBljBPPVbV8FYKy1wKxESaJ7b2BtUavvni7sMZ51m0i8pu+NSnvo+//tf+Kt/zyU9gbS0rrJvxzLyBs7dcXoUYI+M4cnh44HB/z9j3pBjJpbbUOOe4utqzv76ibTuUkrUXcMkxH+5PHA9Lbioe63xrQ3cpgiwESiikXIgEeukiWRBZ6+wju0ebyvhYGEHiglMrlDiDQCt97+z9uCiRrHntB+iBj9ftMoAt5wPsDK+WCvCsmNLqN2t5U5ARqBJJw4Gvfv6z/NNf+E+Jd2/T+BOuZFJOHPsT2sFNe40iMw0z02lESYeWle9bRMaHkbZ1WJFQGu6OR2RSGFHlQJRk0doxkDOzTxV8yYLiKw9Uoymx4AfPNCbIsr5jKQlkYpGkWTBPkTBHXKvYqI52YxG6oLQkMKE7xcZtECja7TXtky2qs4hcCGNZoikWIA+M1QhduzliDCitabsGrSRaaJxWFCp5IIbE7BNxLsgsmWdP8JEcC6FkisygDNJKshTIVNBWIUxhnjw+JfJQ67PKFqxSxJzxRRAXo0+pLC2LkeADhUxjFM4aNl2DMwarLWGeGfsTOWZs17HZ7IkCJiIxjEgZ2bQbQhg/vHFSarJ9dbXjZ3/uL/KjP/ZprNVLC9i3SlusIORaNkkpMY8Tp4cD93f3HA9HQojncFlby3a/P5dMtFGknEjeczoeKy2vH4mxXoi1DFF47IcsiyHpBdCpwI5Aa4mxFQDSxmCsrUQCbRaPWeuXq1RIfW7tJoFH9YLHterlPP6zrsuyyctc2CUvXQz18eUKkKvqpJDkixooS0RR5om3vvA5fvsf/6e8+8XP8bF9i04BUTImB/ZG0pUEwwEhJek0oks1KiEUucAwTsToMa1Fp4QuiUYJYimExJnK6FxtjYtpZp4CSL3kXfX9BAV+TkynQAilIpwxIbUmFEEskmEMzGMkxswE5CaTXELbghAZtEQaMFuDNS3dbotoFb5MqFQ7QsI4EuaJtu2wjUOqBKK2KeaUFnqkQGmBEoXJDyRfICrknIkHT0mSyYcaIqeqzaSV4zhHQorsb64wbYteOqCElthNQ4qVEZSBEiNzP5OFphhLLFBSJqVCXBhzQpRKkLAKYzWbbYs1CqstXhWkcLXLRWWM07SbFicSGY+QGaEFzYftSlk3nbWWn/zJn+TP/Jn/Kk3rFv2fP2hzPiZXpWSC94x9zRf7w4HgfQ1NRC3ud13Lbr9js91irSUvtLy+7zkea6O0DwEhQQlFzvlc66sEqeW1Vu+39FAqU4nqxpqllmnOiKyStYNkzS9Xw5RLN8kq1flyeaR2d6y3f9v65UUE8S33P1akLn4tvEQcKCBKJQ8c7h74ymd/ky//+i/x7M2vIsaRrEEUj1NgOsf1boPUmYLHSkNUko3RTHNAOYufI/08gSwUZSkkYppQ1hL8zBgioNhKUzszckFJAVIhlEJog8yS6AN9nIiheqUUBTFCjgV8IiKZc2acqq5rEZJUFDfdHmkk2oEgkIoHLSk6YzYatzXo1hJCIsUZwghpqhQ+a0A4coGcCiLnM2d6xQtKTJWkECDNAjEF2iQBzSQS2mi6vSMJy+0pcTqMTDlzeHbHZtvypG0IStI/nLi52rPZbKuaY4ikrPAJfJbEVAgloUpCyZpyKFUlLjcbx2ZbyfbXr11RckShME7S7Qw5eUqOyM5gdx1No0hlBmvJ0oBqP6Rxitou8+lP/zB//W/8NZ6+9qTWotbS5Uvh2+WqniPGyLh0mhwfHpimaaGzVWGrpm3Y7a/Y7ndY5ygUwhIC9wv7Z54rrL6q3OWczzXUSnIoSCVQZtGH1XLxmupcu1xFl6WSSwN0NWCtFxreyvZZENnLHs9Lw1ujgXWtAsyXQNC3tnit1liWmuUSXZTH+mwuGSFBlkyOE8/eepPf+ZVf5Xd/7Ve4ffstCJHiA31IbLeGZmvQrcE1LSEOxBjrYbNtIBte3PakEuv70hq36ZgKjL6SuoXUDLHQh0xRihf9iBCKbWMQWSEkGF1b8VIq5CgI0VMy5KzIWdQCfKx6UolMFCCNwXWGYZrxJeBLZGNbhAggC0rW7qacIpSIEAmjMiIlfBhRcaS1UJwAEYkp0BhTW7JyrLXwix7aIgo2G2JOhLGWlkqSCJnZbDvs1qG2hhcPE9NDzxA8EUU/DjyME2m/RZLxJTHnwNZtYSoM08Q4Z6SwjKlwGkeSiLx21bHbdeRc06vNxvHktRuun1zRbRqsEwQ/kWOl7JEVEkMMM14UlBE03dKJ4lqybtDtzYczTiUVT57c8Df/5n+dP/pHfxDnTPWaYqHFwUubVVxsvhQT0zBxOpw43B8YhoGcHgWIrbXsdjt2+x1t0ywhVWKaRvpDZf9M41hzU3EJxkjWVqwqHUKlDSqFMqvR6cVjLvXL1WuevaO8aO1i0QKqGjQfFHo+k8+/TS3zO82ZuexdLatoF4vms6hsldPxgffefptxnNnv99xc7zjevsfnfvWX+cpv/BrPv/Ymvp/RuqHEwjAPRNOhrxu6rkVART59T9tu0Kpj0xnGUTPF2kvZ4PApcTzMpCgwUtFuO2gkOQ3MgPCBfVZkqcjRVzmTnAijRxQJCcahdglJoUlRMM+JlGuTtnIGbRRN14Ax2OiZy0jMPSkVmkYhVMEYSQgzcfZIcVX5u2TIEe8HnEyYraVtLUo1NE2L1ophLMQQKwtLKFIuSAVSabQTKCspKhHCyDSdmKeBzeaKpx97HdkWhpTY32x5UiyDl/iHI9M8chpmrBGYtjmDT5lUCQhCEYViiInDNGMbgXIK20iMckgNm13HZu+Y44iKlXtbOdY1lDbKYE0lskQ0erNFO00mkpEEDFl+SM9pjeWnf/qn+Imf/HEapzB63bDyUQ1DrGyXC3Z9huBnhv7I8eGB4dATpwKllijMygDa7+k2HdJISkokPxOGgek0ECe/bnMElVspxaJVrsRSv5QLbXBh9JzlKw1G24r6LXS8lUOrpHg0zEVWUyqQMp8FuVhMaq0yLpa4vp0LqKuc67r1jspe+WBYK5Z+2JwjYRoJ/UCOnq9+6fN8/jO/wXgckKWgSkb4kTQeEQ+3NDnW1/QRkSNCRrQ1aKVJ0deG535m7CdUabDbgtYC1xo6s+W6KKYEbz+cONxHYi4U65CmJbmCLhlNRklFNIaHUMslrgjSnACPRJOjIKYqfZlzIYY6kqBI0I2jfbKjGIFuqrhVVxpQGiUKm87inEIbgTAwJ4+xDtsahKnlCqEEttVkYQizwKkOa1qkrPXSVHxVBywVwde6ypRpaRGqRbgW0wiaPOBGSUpHSqxhbRYR1yj2G4WfG0RKROcwJdfvJYGyBgHEmBFS0DiF04Zj1AwxkFBYJXE5YfJE02pM67CNIJUJpQQpJkqCtmswzqBtJWfYboduNgijQVoQiRxO+DDC0pb2oYzzU5/6fv7yz/88u0VxYM018zIaQV4U2Bf6LJRHVYLjofJm59kvUqs1z2zbhv1+z267xVhNLokYPNPQ0x+OTMNAXORF1pEEZ6BpAZvWzhCl1rBVn0NYY+zZY1bveRnargJdC5ldPoa1Z+h0ZfSsDJ/LkBWWPHF5X2f6T/3/i57p5fmLJmyMvP/2W3zml3+Zd7/2NayC/u595uMtxQeGwwNpGmg1vPH0ijeuHE3e8H54YDhOaFUwVoCMCLup4XeoCm45NBipMEKiZGHTVO/oQ+Hh3Re1wz9ngpDMIfDs2fuEGFBkOqMAxcOQ8dOATIGbrcVpixIw+wrw5FzV+1IqhJgJMSGNoGst2+sN2VQ2kSgsCvWGFDxd2yJVwTUW3S7IuCx0my2b3RZRNAqN2u/pY2R4fsRZjeoaQg5M80TOAqUdhUpeUEVQcqXGubZFuhYvC91eUJ5GRIxIJxnHgRIzRkm0EsQ405/qaAYlNBmJX/bqk6stUJsbEJIoFXMIkCO71nCzVey3kpsbS7dtEVYjnaXdtLUhY0lvjNVYZ1DOoJoGu92hXIewpqolkMg+Ms59jejchzTOv/N3/i1+6Id+cOnIuBAWXhg4l5WBtbieYmaeJk6H46IxMxLTIjSlJK61bHY7trstrq1EgxDiQr1aRJ+nmZTzS4joJU9XiIqoaqMrkVivoswaay3WOYytxvkoO3Ihbbn0cQpVRbnKGfi5VBRcDIwLY8vlHJ7Wc2m5t1yWSxakdlFKL0tB+/7d9/mH//f/J5/5pV/EpYgIHoPnamNwSmBKwLaK3dbSmoRi5I2nLVZLHu57pIDd1nD9dId2hpwjsghee+2a0WlKyJADm84yDjPP3nkHZWuDskipMmKCYPSekFM9oIQgC4kvAt8H4hhoRGHWhUFEZInElCoQhqz5ZwYfIimXShoXkUxEGYlQNcUAqq6wFhhdwTfXdLSbjm57jRAB02i0a8kBVFHIRnJMt8QhE48T/W0EWbCdwbjNUk+fSXkm+9rSJlQm+xkpJU4akoqEMjDnARkl94cXxJIQSjNNnofDkfujZ46icqidQaRS67e+ULJinhLTnBmiZ54CeyO43mk++rGO7/3kFU+edhQKc4wUrdGu1spzFjWyc5qiBWiLcg3SWqS1YEwtVZWEES3d7opIi5DxwxnnT/3UT2CdWUCgpVh+4RnW/HEN+apynmfoTxweHjidekKsTBepa57ZbFo2u24hGlT01c8Tp74a5jzP55BwbQNDXPzNBayRagEG9KPHVOvPmVSw3rfOMHmUqRSrvKUQC4X4cZgAPOabl39XlJcH+VSLXdk+5aUZQoWV556Jfuabv/d7fOlXfx3/7DlGQUfByIw2sNk4bNdgTaZtFVKVutGdYrN7wkc++oQQI1ZD4+qBMvcTBE8r6+c7nXqqpkZmDpFcJMNxIBfFzX5HGiLCF4ycKdToJqY6WgAl8MGjlg6YlAp+jsiSaglhUSsPIeJ9IJZq7M3GYZ3CNYLNvkVZSdNuCD4yjz1KNjSbDq105Uk7RxEF4wRFJKY5UqJka1qKz5Sk2Lornr175OHhAdManrxxXT2TKuToochHSV8SKXtIGWkc83wiMmO3Bm0NpjHEITGNM+MwMs8T3kfmqOpwJSGQJZETgOTu7kAYJ0JM+BBxwL4xfOLJhk997+vsXqvlpSkkTFNJFhgD2uFMgzUWIQXTPBKFprUNummR1pIklCKoygeWZrMl5IZm84EBWv+sxmmdRptaDBZiKbSzUvNYCOdn9loltQ8Dx8MDx8ORaeHNClHLFq6r4xJqC1gVWvJhph/6MwBU23rqbJRcSqXjwcIkEueezrX0YezaBK0qmd1ahF5LKi8rG5yngi19d2fdn7Om0JJlLiHK44erRigEZ0Hrldyz0mpr+Lt60AX8Waw1TD3f/PKXCLcvuDKCrZXsjUZLUAayH0k6YroWZUBaiXYK21i0aclJMY0z09iTfUaryo8ti/FXxFqCsRyCZBIdYyncPtxitMH3E8chEIrCyar/NMeEFLUEEkUdrqNlzXtTCgQh0QJirrIeVbVQkpAYU9vE2s5WmUqr6BrN5mqPa3d4H3mgilNLqXFNi1R6ue71CBRSooqqjdBFMQyeFOHhYeDu+YkUFU2zaO9kTVE1lFZFkGMmL4SA8XSCklGtA6lxG4eWis12y5gS9KF6NCnY7zbMMXN/ihTEkudLrLaM/YQvmRI8KSastuw7w2tXLW883dF2Fp8DCIXZbur11grTduhmi2k2SO3qfp/bqrho26rVe9Z1qny2R+55Yb//kOMYan5WQY61gH4u/C8h3Pr/KQfmuUqGHB4eGMdx2bU1zzPO0m06um2HcRahJCF4TqfqZcehJ6UqcLwaJ6sMiFhU/lZK3TLop4bbculsqDVMsaC1tZF6aZY+h7VVmqTml5dzUcQZ3KqRezkTKS5Iwx/kHZyNdLki5/C2XqvVw2ZK9MT+wE7BblfpdjstKwMIjx8HnBY0zuJajWwMxRqyNiStSEKStQZlKblgdINaWqBSKjjXMLlEQPHV5yPffHYiJY1OmVZ5pCxsnKO1jpQiKRaMMRxDoR9nQi60qm5WlCBFCEKAUlUyJFX0VkhN021QurDbO57ctDStxCowCKzQkARGtghhELIap5BVrbCqvmcUaok2ahPz5AMlgjaOfpo4nAas2kHRpCAIISNLIPpKZQwp0TpHCYVpDkAg9hN62yGlQTWKfg7000hKdbpX22aeFkWMgeB7hjmgs0IJgRYSt+gX+RSX/lLHRz5ywxtvXLF72uFeu0LuLFnV8FWpgjSiAnRNg24apOooRaLbSmSo7KmFlJLDcsYriAFZKpnfmebDGacxFd2sXpPzpqsKKeXsMkvORF9lLfvjieEM6KzUN1lH8u13dF2L0YZSMtOSm/bHE2Gu5AQhBTmWs4SkgEftWLmGp/rcLP3Yn2kWYru9UGiXF+LO62vUU6t80DBZDbP+flkmegR4Lm77QH33/BhWGDtDqTW96fSAjJ6rbYNUmUjCtJabroEyEX3G2UK3M8hGgVVE15KloxRFKZKmcxhpySEic0UuS8qE5NHdFt20vPX2M77w1sCcGxQCEzPSFF5/coVuLPd+ws8TWluU1oyyMN/X8FIphdOVOaSlIhVFiqVycGMiZ1HLJW3H9U1H20lunjRYl2k2tcsnzB6ZK6aw314zz2MlDei6zXLKWKuJITP5ypM+3Z0wWfO02yCpTCnlLBSxHAoghKIO062Dj8fTSH8/oIxCO40xstL7Bk/MMykUQqj1XFEkYQ7Mk2eeRC3j5IgpCVkqd1wqiSg17dHbKlD++mvX/MAPfi83H7li/5Er2qdXiNaRSj1sS56AqY6MFLLOslACskQZCRliqSp8JWUSE4KIzAqBB6pg3jQNH8449dLtIc5F/3UD1jrnWT0vpgXQOXI8npjGCuiUZRPbpqXddDRtNUxK1Q4aDz3jcSBNARLncHJVzFuNQIh1enM1tpVcYLRGK4G1lXR8VjnQGrVoHEnxCAKdNWOXCODcoVo+8NnOM0TKWcyulAXcQSBKPN9PUfVaUKCEhRcroUjK3PONL32OX/0Hf5eHt79C2RS+9M07DseBH3hjx7/8qde42mn2bzzFNQKhMkUWipQ01hCRTKFKfBqpCSVx6k/EPuFEJs81X5nGQMyCfiycxjrIV6YJWWZcW3VbYxHk44mtttxcX3GaPA/HI40s2MawNwa1TJVOIhNSglT1acMiNNFtG9746EdoNwIYabva8dM0jsF7TtOJdhsoMtKaDY0xVbXAaKZpIuXEdrfDWUeJnmEYSMMd0zgSTxanHfvXW7IoxFHStBbbNpSi0KIDWYhlADEzDCdyyriuwXSarAS+nxhmj9ZtbYyOhX4e6PuJeUr4IJjHjMpgUGfkXVEoMeJFod13XH9kz6d+5Af55A98H7vXn2A3DcLqpRxSa7U5nBCpJ5eAj6mOrSBRqGlAzpkUwlIqKQRG8AkbI4ERYSXJPuXYfwf7+07G+S3sn8uu4ZWAtohBD33P8VDpdjGm8/3WWbbbDbvdjqZpEFJWb7KwgPw01dx1mRh9OXL+US92QV2NxuhLeZEqwrR6UG30mUSg1rxzHYkgH+eRnEc3c8ZmLz7jEphees0VmV0j1QVEqkhSNdhCpmYAkZIKhxe3fPkzv84Xfv2XeHj+FioX3r7veffomaZC+ca7bNXED33qda6fPKHdarLwKCMIMRJSwDUOaRTzvLBqRCGmxP3dC/AzrangSEy+FrkRtCR8GNAic7MxdK2iEHh+eyDNnv1ux8ZYcpaodKqK6rYhisJpmFA5stGSjZEoWBhZBSkU1kiePNmhbMAnz0xEu4ZZCLI2CAtvfPwTPH/xgkYNVVhbV1mUUzjWElzUFCVpjEE2rvafkpjnAVEK7X5DLoYwCpzeYLQlpozSqoJcU2AcfdVSRlCyIkXJOAXmkJkiFBEJfuZ47BnHhA8JUEhpKWhEASMrWYBcdYOQCalhe2X42Pd/lNc/9TH2n3ydzdUOaXWtxZdyxkGyVBBrb6lUilgqUOfnmRTqBO4wz8hCVVgYB8a7A7kf0DrQPd1jrztcsv9ZjPPb1GFWEeSyzDUZlhF7fc88L2UQQBvDdhlk23Vd1RjKiXka6Y+HJc+szI8VeFkpcevfX1u3qoasOnvSeps6t36tj1mH2FY1g8Ugq7TLWU6EDx46H/x4l5/6Ao0Wy7ClvBhnEZzlRRCV6ZL9yNe/+GX+wd/9e3zts79Jkz3txvDOuy94525mDJYwBbzM3N0dud0brm5a2v0OaRRCZbRQoNTSK1vriyFFsshgBO3VjtBroM4eLbECQlfdhu+9DpwmT9dt2VmJKIHT4ZbdZoPTEqtrYf9wGEHUJvOiG05hYIgZGas+jjNNLYPIXGeCSPDhwBe+9Ftc3bTcvLGnNHvkbs/V9ROUMZxOA5ub1xljpoRbpvFEIVXCyUYhlUXp2hjtXIORguvrNxhOD0v7mUKYlownhIlYMq1zhBB4OPX4ecT7uBhm1ZTyXuJnzxA8PlF5vmFmHDx+zkgcpGUSuTC4pqHbbBiHgehHSgZrwWwk2yct3/vp7+fTf+zHaF9/DbtpkK0AmSmqkFKEHHG2Dr3qp5noff1cQuL9QJxHCJ4yT+A9KUei94QXJ+6/8YzpeMC4zOv54xj7FOk/pDTmtzPSy26wnNNinFM9pfqxqhosTcpN07Db7dgupHbWvs5hoB8G/OxJMbLS8tbMbfXYa4nkHKouhqjP4xIe9X/kKsC1ktgXFQOxjNk7/4hv4y0/sMrKqljrmh/kynKhRihyHUJbInke+cpnf5Nf+Hv/EV/6zd+mHO95erWBh4l431NOAgO0QrEVCh1h7keGU884GXRJuKZGCk5afAj4yQPruAfY7HdY1TEZRxwmap3cI2PiarPhR76/5TTO+BApYUSgyMriBRSRyCh6P/IwHkmyJZHphxM+zRQhKrihBcIoKAWjFkHtRmFagdto2r1le72lvblh+/QpH/2eT5JSJornuM2OzTwT7x6qCJhruN7vmUMhFUlEkTH4Mdfe0faGnd0QvSeXiF1AsZQP3D0/Mbx4Vvt6c6pRkG3JSSzC1pE8J6aY8EWQhSIimOZMSAYhLUZZUhpRSrLdNXzv930f3k8MrWJ8AJCYJrN/reHJ91zz2kdarm8Mzc7WsqTw+BCqcLasg5Fi8AQ/Vph05XiXQg4TZeqJ4wApoFIg50SeRoa750yHe2LwICHlSCm5ji37MMZ52c/5aDgVAALIsY6CP/Unhr6Gs+vwca0Nm82GzWaDtRYhBCll5nmuHnbhza4QCkKcX3c1zJdmlSwdJ6uh2kUHSJta03zZOJeGab0YJ6vnvKh9/AHX5LKOWZvNX6IhPB5OQtU5I6WKc6Vp4s3Pf5bP//Iv8vu//Rke3n2XayXRnaVrNE+0xnQa6zridGRnW17fW/adpXMOqzXI2oqViBQhCJNnOPYIZTFmC1isNYx9IgmFdFuYBTlKSgykeUYrSeuqYHESojb/2o73H3r8XMcRINWi3epJM+SQcFrimsVbxkCOiSISwghco/nIJ57S7A12Y7h+7YbtzRW5cTTba+aQebh/IKfMi3ffJQVPCpKSLUY5xt4jlK0UQKOJQoBQxGIY57Hq4qraUBFLzVE313vmAC/ev2UcB9q2w7Ub4uQZ/MA0J+JcVfTGkEmiIs2FOpmMnLHaLtxr0LrQdZqcB3zoOfX35JDoOk3XSbYbwdVeYa2nP71Xe1CNJXIeTkkSAqEtUmughtlykU4Jc2C8O/Dw7D3m0wmRE85Z5Monzh5pq0jY9skWt98SVaF8B0fxz2Ccjxtzva1Qc03vZ/q+6vpM03zmCSql6LqW/X5P13YoWdE0P88M/Ylx6KvgU6kJdO2aqsN0LqdDn/sjeTnEXYkHK+1OnMe6P4p5rWoIYh3Oe45my1IW+UBb10oPLC+H8tUYV9GtZV4llZCgiiD7yLvfeJPP//I/4Yu/+csc3vsm44vn7Jxj5yxX+yt2LpGmyG5IyDLiGslut6PtFNvO4LRCS4FtOkKYCcMMxXM6HTk8HHDNluwKRWzQWuOswcuE0hrXKeYxME8nnJHkBHNMxAzz6ImTR+mCP804Y/DzjG0U3/PaDdcRbo+BYfQYo4gohikwTyNBFZwF1VqULWQV2Fzv6a42CGeIKETW+Cnz/HRLf7xHloxVlScd5lKbpmU1EG0tRrcI23F7d8LPHopgGk7MwxHnFNapWmqwFmE17dWezkeGaSYjkMIyjT0Ph4HTYWCeay7pyWQhkHJVIkxYpei6HdFPaCUppTZr9/2B5y9e4L1HRthf7Xnt9R12E3Au0TaZqX9GToH9zRNsu6EoQ8q1pKOUwJoNfSiEtEyPSzCOE3fvPeedr3ydMIw01mKcRbqqho+S7G6usK5j/3RLd7PHa4OWH3JWystGuuzghTUTU2SaJ4Z+YBwHgg8LcCCwxj56TecQQhJCYBqrQvs0znV8WilL6Ajk9Mj+WcsoC03vPLJ98ZpmYf1opZALr1bJhfWzSuOrhSu7kgsWosHqNb9tv+X5DFqYQOvMi/L42VcAqBJUMt/86lf5v/4f/w/c/v4XKf0DOkf2bYdAo2XNFWMKNA34oUeXxNPdDt2AEAmy4v72nqg8T8QVpdRQdh4HpnHAShApMvWn2uot4P524vQwI5JCZEmMZenw18SYOA1jnUmaIHhf68xCk+aCjBmlCzomNqWWbbqsyMBxmhA+YETBaIk1SxiXI/3Yc512oDbMwRP7gVZY/OjRUuCkYR57PAmlKllEAUPfo0REB0+WI7sbhZOeaT4RJo8/3XM63BKc4slrT5CmpQgwbUOnBAnBaRw53Z0YTu8x9yM+ZmIWHMdAJlGUIC9kDK0EXWOxQkL2KC0pRVEF4zXDqccaDTnRtg03T7Y8fbpFuBFrJUYJ5jCiTQMlAAmlHTmVqi6hNLOPpCxAuiprEiPeJ9KUKGMinCKmaxgGz+CPZFnY7gRPr55ytb+ibRuMaSp5QXxYacyX2qaW6l2puebkPae+53RahJ1zqBtZGpxzNF2HcQ5k7cEM08zUj8yDJ/lY9WZrEyOl5KqZI0SdZiZFbbvRYiFCVMJ0bZjWtY7EOpzI1J/FY4raYlIJzMDaPlMV8l6OAr5dU7SswPpSUlm8bS61GX+5IVMgTIzPn/GZf/j/5vjmF5DTESHAaIsskeH0gO0s2hnazrDtDK2ubVjWObSyFAmlaMbeVz6oULRtIcURmRKbpqUA05zpjzN3txNhOnB/9IgskbGGpO2mwW5bZOcQ04zUE7oYCgKfamNwDpEQBXKRRY8BYgqkwRO9J0lVZ5mmhJYSJRRKWUSRlbebRJWhjKEqNQhBazVaSGQR+DkzjIGmcUihIfWQI8PDPaSpqlNYQzg+EIQmjIXoC7pAYyq4lWLCqILdNpjNDbd3A35SqH1HmSamkyeJfB6lKBYWGKZgnaNrLNvWLW1oiZwCcap9o05rNDNGJQIzTZvYd5rtNoA7YXctdrNH2g06e7R1pCJxypJiJoYK7ggxgmgpwoIwNYQONedvnWa73eHklnESPL89cuwLYZ7Z7w3lIxNaPJBl5vr6dYS9oojNhzPOx1HoC11vGQWwTgU7Ho6cjid88KygjraKdtNWAabzRV9LJ32dKJyrYa7j61YC+YrCAmeVda0vQB8pK9lgAYfk0pWyEhTUShETiwLfWtYU4tySWckFi4leyIqs8pZndtAy5i5T647rLEuKR8w9b3/5S3z2//OP+Ppnfws9HGmzZfKeojLXVy0f/0iHwfNk39CqCDFDu0heBMHYzyAFc4wMcSQdqtT/kycOowXeFwQJqQzzKXJ8EXjvzZ7joZCVprEthIQgVwnROeA6yxQS3W5PYxxD33OSR/w4M3iPDzUM0CkukYIk51JDqwymaNolZzdAmiM+iUXO0SGkZpoCISekdETvUUIjtFmS+soE2mw7js/vSNHXA17ZpXhfCKeBpA1StDirIRuM2TH7cWnCtwhZv9/NfocPiRwDu+2G6aHn7r3nzGZEGGiuHNpV/u5+v0FRSHEi+RmrYOhPDDJXrVoN0iRunu4RsiWR2O227G4s7VPH9vqGZrNDWbCqQekGITVzyCCrinspouIkwpMoxFD3dgkTMU4II1Cd5dhP3M+ZZ8eZu8NE8okpQiwHfJx4GiPt00/gtplC+HDG+cG1auPMy/To4VRVzFhrYbKCCm1bFa2FqGPg53lmHIbqYdNjDbRK1VfZt8cRfOqcW671zVXVwDqHNosc59JVUvVh9EWe+dgofVkxObOBLmq0HzTQ+sCVyVfZI3ElgwMyJfLwwJd/7Zf5p3///0W6vcX1A0+FIhZJPwzYK0O73bBtFTLUAUKRTKM1Wkq00wSxHHIIlDHMJTD5mbsXR1onMTuDIBJ8pKTA8OCZHiLzITIfC0FGZhVorKFrHae+R0WJNBUQKcpwvyji2W5PSD1xiPgYyDliS9VoFQhEljTa4OeEVNCqGhnk5MneExvFzfaKbrdHaVdngyRQOwe5kFOkKIMyFtd0lFLHIFqjmOeEbTZEP5NiQptmSVVqiJizJCqJ9zNSlDrV2jR0zQbXtqAi5B1WC+IcOBiF1JJ+c2QfYu3tFQJjHEpJjBZM/UQg0FpN4xqsKfhZ0jQGYxXdU83u+grTavZP9qAVuu3Y7p+itSalE9N8ACXYba8QyjGHOmezpkaZHMH7ieQjKQbS3BN8TyTUmqi1RDyHMdGHZWBvn5ahwxnbRU7HAbXrKdLzB63/33XOD5QRYoxM08RwOjGNAylGVpFlYypC2y1j+gqlDh2aJuZpEekqjyPu1pc9eyv5qAV0Sc07S1vaFZVdcku5ek350s8KEJ0/w5JsniVCeEwxv/VDlzMpulDqv8sMknA88eVf+VV+4+//x6T7W/rnz5FjpMHSD3eUNKDUjru7Z5S0I/YTJgl2rsG1itYKRA7oVtBuHTELijLgO+QsyHlm6ic2lhqKSkn0CSMkMmeMotYpc53LYp2m2ThiCZjGgJKUInk4nCi5sN1ecXroOc0JlGO7aYnzRCFhqDXUkkHFghESLQXS6EVAOeNjRDWa7c2ebrdj8oHRD2jrmOaEnmdcu2GafR0GpDUSX+u9IVb91yLwoZCy5GZ7TaYQ01jn2Eh5FvEuKdV5qix6wyKjJbSdBVpmo2spJtdyzK5pKcETxpFpGvEh4JoO1wqkAG1S7UKShlIq2GQaxeZKsbsxaGfoXtvQbl9D2ydIZclxRKW6KW3XIoTE+0BKGW105SZ7X+V2vCdNMylFSpzJ0RP8zDyOECHPAV0EWmiykAhlKQLGMXD7oueN+xPbJwOi/ZAkhHVzX3oX7z3jMDAce/w8V1EqUWeJOOdemjidSyIEzzT2zNNYyeyLaazzP6QSiLIYpNLfUkI5KxhcjnRfDVebZYzf48+aJz9a/xqSf/tDZ9WKPa9cgExc7pElUIaB4cUzvvybn+Ez//Afcfrm25Tief/FCRvhI53F2sL+yTXFCh5OJ16MiRw0jWqZDjPypkW4iCgjTVdntARf2StaW2x2lCKJU2AeAq6tFMRUIlJktMk0Xa5BUDG4ruXJ02uM1SBrxwZKkH0ixIBWlhgzRWiEsmhrsIiFxRQq4TCmJXxPdZiwUiidKBIaq3GiRXYaoQshB5LI+FgwjUYqg4+Zw2lEagtCohWUGDm8eJ8yzQgEIdf34FpT68w51dqrqN8faSYLSYyJFhAlEeeBImr4TpEYI8nZ0G41KMfmKmGFJI0D98/egzDzxhsfpXGKMCn8VBDF14lhWqNUwVmJazVuq+i2At0qtrsNynYkFCEFUhgg9GhZ45ppPKJMS+MckElhZh5O+GEkzoH5tOg3ybUxQ5Cj5/75A9NDZGt1nU5Gpmm2SCJhqmr1D8/v2b+xo9Efcuz85VplLscldxz6nhQi6zR6rRXdZkPbtqil4TbFqglUtYAe+9bEku9Up1bBJnXB/lm5tCtNzy1zTYQSZxV2uead4uVQdu1kufhD34rMXrIQxQVNb0VyV3oehRxn3v/S7/KlX/2nvPmlzxHef87x2S2nnDiGTJNhM3uuNh1bZevcEtkyRcFxLkwiEuLIvQyorUaJRCwSGRMhZWLK+JgY5oQxArMxxEkt+b4kxwpkNa3m+o0dO9Gg7I5ms0FbxWk4MYdEzAmjHa2yaG2IofZkVk4rSFHJByVHGrN06Fhd9WJTQhuFbQym0dUL6yXfNoJ2V6U3REm0suP6+glKWzK1/NIah1YSFUdKSAvKKeiHiVQgFdDWME1DVXCQCqUdQlsUkaZswBq67aYKfuWpDgEqDiGrArvRS1O8aWp9MQtyoyhEuk3Ha0+fEOYTKQ041SKLRhaPcuCspGkEUheU9qQ5krPhpFvcxqIcJCZiuiNNJ9AOpTuELhhjEdkv+/hUha9nj59m/DBy6ie0rO1t9f1JtAFnoegq/XoaZqb5BKLQaBCxcHo44oeRZvshRwDCpdesigXjMhczzL6euqICR841bLoNzjmkqEBD8gE/zQTvSalq1T4qnpdqoIglnJUv5ZtKabSxyzQwu3hUs6jlLeMSVkL8apxSPPJmP0jRW2Ppl2LbVVm9njCiqDNPFgrkxHh3y+/+yi/y7IufRfR3dCqgRAAhMU1LGgeCSMjs8Kc6rGdjLa0xJDlx7I8YW8GyydeQD19w2WJdS4oTL14cmGOmbRy6WPI84wbBZtughEOpmW6r2W+uaK9eQ5gtGejHnqmM+BwwesmrpK2E72EmhIhxujY/+5nTPBKjZ68aXru6YuMscz9iRKHZtTSbBtsYlFNgBD4FfMnoxlRqYQJTFdEoQqKtxTUt2jQYVSh5pJDZtB2m3RLCMxCSKcyQff3+c6p9jyWd+z3bbosSGaMUpEAcEyJapMsIXci5AjJSSrSUCFXno8hWsd040lR5R0UGTG6QRSEJKAK2DFizhLmqDrglR2QGEWdIIzkUshzJ6UDO49LeVkhhJgIpVYHzeRooMS6K7rV/uE21H1grRULh9i1XH0noZuY0JJosQUW8n3Da0jqDEoXxNOKnGVE+ZCnl7G8WrzlPU6Wa9YuS3jITRWlN23RVLU1ZZJFEP+NHjx9moo9V4iNXMOhx0Ho1itre9WiYUmukXsWg7ePEaaXPHSbyIpRdye6XBnnGfrhAaFdNoFLR2CQlRSSQAZFr4p7k0nUyjfjDHe9/9cvcffOrMN/hZCLYRLsXjMeASJVw0TpJzLUvkSKYQ0TKjImJ11pHYyviqRRga9nHGEPjGkDSbiMiVlLHs9uRrdW8JrYkDUInINFsWuzeoVpJFIKYMnOJFCloGlO7SPxAUjMZiTIC0wh8iBibQQhmHOPRE4Ri+/Q1rKgtYm3r2Fx1NJsGZRVRJooRIFwlbpfqXVOuniRlgRJVRE0rUzEAXZXxQlAIpUGA2TiG0wlrQemqN+fDTBYRSp2AXmQiy0gqCUHBxJmUAtLaOvFSyNoqRuWvSqmWPLVyZUXjkB0EP6CVZ9u8VoW18aT5gJgSqfRQIko5lGwQoqCsAqrYliwBaSoN0zYtEYGMIHKmzCNxHInzjCwZpRy5aKLKmEYjTIPIVNArZ6STbF5vaHea67kwe0G31bWFbIwIERfATCOyOHcW/XMb5xrnlVKBnbWTZF70Z6E2QlvnaDcbXNOgtSKlTIj18fNcEdrLEQ1CPIaa5/zygtmz5pGP3NqF/XPxOLU8bi3BlDPZQJxrJtUoy7d8IiGqb6SUpaevsuKrFw2U+Z67N7/CN373c7z95S8i41hztRLZbwySDms9d4dIHmuEcJirTpLVjhAyMQyUHNltmtqaVgpN26BdLS/V6WXVa286R2dsHdDzcFhGGBmmIZNzwLUO7Sx+CkhxqrU+bXBWEjtL9gKrII4zMQYiEtvt68BWKeuEaJ+woUHrwtX1DuNqR8XuyRW77QZkwViD0FX0quSCsrU0NU5zzWlzqUir0Ww2Hc2mRRqH1AbrgFmirEHlzHA6klNEG7W0jZmqCyokpAClp5QAKiFyDYOFakg+M80BnQTKJAShHmoykpVECFNp0noBAoVAZIW0oF1laKoikQTC1MAomWcNckY2LVppcvLEXD0liVrOWOr3xjZnEAmqusc8eVLwZKrie8YQSiVIZMEiqVnDda0VpjUIqylZktC011uUdty+9wIRAvsrx81rHc4WcviQ/ZxiQSpzSvh5oj+d6pSvnM6+r86u6Oi67nEsfKwI7TiNhOAr2nmB0j7KeMizYeoLYzTrhGmtX/KoSusqb7F6z4WitxLa65uuPy+PROCRHQRLR0nlxFZApPb2FREpYeD07td58zO/yNc++xlOL275xBsf5TYJ5tNAc92yaQRZOFKE0xTw48zk6x822mNVDf2QEm0NrjVos2jvlkwuGaMVKXoEke2mQbmWk9KkORAnz6mfUbCowVmyFCgCmoJ0VW282Wxwba03zn3PIU5QMsZUrSalKhlkOvT4aaaJcHPT0rQNMQf6sadrmjphLBd0MYiF/hZzRoqacyIlRSgykRIr29Q5jdu0CN0gpUCriIgw+pn7uweSnxF6aXZfplRLVcNhSZVCSeW0NPNXlXalDMI5JAbTdaDskgoFhCooIVDUYbZ1cFZVxJAFitYY11bKYJaUslzzRiBDbWpfqCtkKZnHqaLTQqGUIaRADLEOfbIsIzFEJWh4TwozJYVK1lCOICqFMRdRFReVQotmOfjnGo2JOj2PxvBR69hcO9I0stu0tJ1BOkES/5k8J6QYGMdhEeB69Jpi8Zpd1+Iah1KKtPR3TvPEPE91XkXJS1vYaiUsBncpWXmBul5IWVaDlefulDMgtEhZiot8s/JoxervH+e48DIqW4GgvIBRcvGckRxHxmdv8+4XP897X/gcw7tvkaZE2r9OzoY5wP19j5CFfk70fSSm2t938jMlw7at16TRhpLmhf+rqJPWIlYu/aUUgvfkFKs8aPTkMCOA4ANDTDSmXtPb5z2mg2vRYoxEmogoCSUb2tYwC0jThNGakqHbX7F98hTjNmQfeNDPmE6LtGUpmMYxTjM2aRKJJDLaGEKMCDLTODGlGR0dZqfR1iFU5eX6eeLh8MB2t6W53p8/CzkwDT3H2zve/9pbWKvZP31C6xq0dXU4EFAwi/ZuIBOQWpOLoRSDcrV/U5VyDo1zmilxRpaqNiiygJSQqo5mkKkOr8olUhYkWGlHyou+cjEo2SFFHZaUY0QIsE3CyC1W7zCuRYR+OdWr0kFK9btN80zxnjj2lDiTlCGYQtJQtEKoqoyfY1oarAUpVscgVa0nN22Dbhvcfgfen4dNoyXzxTiOfy7jrF6z5kJD39ceuBDOgIpSCucsrm0x1oIU5JjwYWZavObarAsXHFceG6kv882VIbQKc61lFGMeZUYe65qPIWxZQCmWss+5ceQDoNBaQlklV/LCnZVZIJJnfPFN3vrcb/DO5z/D+OJ9dE5kKXn2/BmbzRWu/RRvf+0rpJQ4jZHbg0eh2Hcd2kf8HCiyDu1RSlEKjMNQKXFaYHOt1zpnyTlVIeKcSd4TiidNARECBnGeW5pSoh9OtNKQfcN88rW5xrWUWCdexQBjPzP3MxhLs7mi29+gdAtNgBRxWjGMI5MPaKPZGol1isY2bLZ7ijBM/Yk4jYRp5jgc0LFhYxo612FcBWJSSRxPR/rTA92wO0vOxDgyHY/cP7vl+bsvaDYbbHeFaiDpQoxVZKvWptWi2G4wzRWxNKQC0jmKUkhlyIiqu+MzpAmRPbIkRE6UqFGqDimKKaGUJMWJmDPWbrHNvs7XJEEONZdVmiJX0C+hdItSO6TeU1BIlZA5kOJMTJkwh1phKpJSdJXMPJ5IUiJ3Er1xtQumyCoX6iNzH/Cjr+1iCoTIZAHaSYTR2FajWlcJDVLgc+Z0f/hwxgm1wXSaJoa+ZxrHC1W6Co9b53DOobQ610Gnaaod4YvXPCOki1FcjjsQ4mVveC6hXNQ7zyMSVuW81UjXXFM+1jVX31idtODixkcjXd5KkVUcW4hMOD3wzhc/yzd/99eZX7xNawp229J7QT+O5FFy8+STvDgUcpSE3NHPBUpgs5PcPN0xnHpULoQwEDAoItEHjKwgUE26ahglWEIqa6pqYMmoLDBZkpSjZIExmpgDrql5oyx1ZmYpahF5lhSpKFkx9Z7TQ8/2tQ3WbdCmq6iolHSbXbVgrdkZg7GOEKoYm5ESyAgF1jmyn6vuTS5LSFmNyDYNrYL9fosfDjSNI0wjyS2hXAwLiUCy215jNztC1hRh8b42UpOqLpWTMIwjoQjghqyqcFYWtSG7SHMmcgmhKDGS4gBFIotDWYdKFaQM47AciKle3bVrCIHMgRJnhEiLnpCumkRFIKQilhklPDHrmnIpi8w1hBcONFWwTIkWNSX8odbqWxQKxTBFQinECKfTwOn+wNSfkDlwte1QRtemkDyy2c3s9hswAiFqJaKksnTWfAjjLLAMIxoZh5EQwpI31g9nnaPp2vMU6hQicZ7w01RJ1IUlN6i2mdeulsXTrdhNVcW7aKjWCyVv0QCqOUYls5+HDamXQaDzkNqXrJDH0g1wbpEWsBJvRc7E4ciz3/8C3/zd32J48TZWRLSzzCWShhmRC+++9Rbf+MYD7z0f0HZDyIUh1JN5Tpmbqx0yB5iq4rgoAmkkUllsY+oEY6VJCKa5ntAhBBrlFuSxUIpEookykXOVqwzztHgchdAOYQ1ZOZJs6jgCqUD4mp9pB65DGosoudbn+gf86UCYR2zTYpoWIfXyXcBwvCfe3+I2VzjXEQGMw2mB7hyu22CbjqZrcY3BOYXMT+tou1xD8KZxmKZlthv2T9+gdVcI16Gcw3Yd3g+UHIg50GhB9oX55FHNhhRKpe81lpw0WVd0OBWJRCGFQ+lNZeGMIyJmtMhoWdFynSLkjHFqmZQ2Ekd/BnjIqRIaiqUUT4wRSqKQiPEBUQqm3aOspsSMKBZjWpTS5CKJEZSLtMrgEYTQI61hnGbuD55hKoxjYhjr9PXhdEKSmfew3W3x88Td3QP7/Zbv+b7Xafemhrtus4yo/JAtYzElpnlevOZEThkpq4fUWuMaR9O21WvmTIpLXXOaF87teoZVwrXksV/zJQmSM/NnGUSk6swTuSK0q7yiUhdDh1hqIhedJReeciHsVdBnPUsvbi8lI+NimL/3Ob7+27/E8P5bdbpZLOzajnHwHO6OhDkyn0Zuh5F+zuQYqPhcRJTINHlyiBgqeqiNoWksunW41tU+yhhIpeBTJpzzy4J1lpBiVb2fPbLIqo0kKpVujIpUYBxmdBvoNhuSbEjZMPtSUUvTsrl5im432N0VKE1Jtdukv3uf4eEOSqZTApQkpNp8UEjMfqCkQLdtkNKBUbirPaJEdGNw3QbjXP1ZNI20EKBsRUVVVctrGkPwsdYYbzJFOLrNlhAC8zSTgkeRGZnJIYBy7K/eIKuWMAdELpRSK875POJREotGyBZEQ0kjpQQCnhLriAaj6pgDISQlRfzco0Uhh4ASgjlUjR+RY40IRJ2gRoISElHMdHtJs215OCaEdCh3hVCm9hnrQkkRrTUbUegPt0zzxO39kWfv9RwOnuPJk4ukpMLY1za2cTqy7RMiRY4PJ6aTR8nAJ3/wKbY1UDJSO9rth9StXXmxQz+cyycrI8dYQ9O0lXQg64i2sPJo5/lM1VvzvMeRfY954yUYJBZygVoG4K4SIysqK9eZkZKFoifPJZNLvuzZU5aCfIkKBGXB61Lw9A/P6e+fcXj3G7z7pd/m8PZXCUPP2+/3DGPEiSONUIhYZQ7bxmEoTIcj09RjG4vRNbw89j19a7GlIJEYvSr/gbGG7vqKjZQcDkemcUAjMa7FmZpXhwhJaKTVWGNpXEMKVTOolTtiTkyLPlNbCgVJiAJPwmDZbPa0UeLaSLPboVWlyflhYDod8GOP0pJ5nshSElJeQBFN3rUI4XBbjdTQaEsOBkJEOou2DmU0QmuyUMwJohAI2ZJLQUtBkhW5vHn9Y4zagffMc0BrVet/3tfDWhWGFEFldpsdpt2DbPBMFFlb24RQNZTNFUAL04yIibbpEDJSwsw89UzTjO0yyooF8NGkDAIJOdTDr2REKEilzwi0to5clvq8aUEofJxJU8HngFIbkqyzM5NYgMVS67GYDVnODEPk2fsT77/XM46Z2S914BgJoaru5xIpZUKLx5EccZ5QOWDSRAk9GIfuPmTLWFhqm4/dJHWLVyCoWZpG64SmVelg9nNFaC+CzPP8kMVYz2QD+UFW0GOt8+xhX1IzuACBzhgsj4fA5RJr5rnWb2BVWzg8e5c3P//rPPvGVzk+f5f5eM88DDwcJt65j/g5k4eBvdE8aS1GQuMMu9aS3nsgEGlcRym5blylORwGrpxDi9oUXsh4P1FmgZNX7PY7dAiIGNBa07UOazS5CKTSGG1BKKytVLjTwx3DdKLZ7biyFp49oyiBjx6FRAhdAR9pycJiuytSiLTdBlEi/nRPmEfIFYEtC3qphEaovFAsDW3XgUoUlSlFYIuGpBChkGVtWk+lqgEa1WBti1amDpgNE1mAj3khYzRgWrxPDNMD4/DAeLgnTKelpqtqeWXT0l1fU0RFWp1zJBGQWiCUpmRRtXdyQZlaDmo7QZp3lL6nDA+k8ABZQlEI1YBySOnQ0pB9D6Lq/DBMZKGqvIjUkCVeZkzbYkxDFoIYInP2FFX7j1MBZEFKhY+eXDIFyeAzz24nnr8z8Ox54O4+EgOETFX4E1CKqphGWRyKSJjGst82bLcGQsAQkEwUmZEfVlQ6zJ5xnPDeL56Ic0jbNi3ONbV7otTyyTzPVRHh0SyWGidQygWj59F7rgjtqgFUk1BxnmOCEI/NtWfu7CoGJhbDrMSCmg8/Hgt5tekFlMox4Pueu7e+wrtf+Cxf/8qXub29JWYIWXGaIv2swANJcxonxDSx7wTbXUPjNNYqpljFnEuYaJXAuoZ58vTFk2VVGN92DdurBpzGL6TxZtPWsFxQea1Ng5IG026RukEskhXBz4hNpO1atlcbFBnVNRwP9wQpMLqG97ZtyEIjtMM2mmmoIs4SwTyPVfhLVkRXCOi6DZu2YxiHWt9Vlna/B13w6VS/u1RFvbKuvFmpVc3TpEeIQtsYWtdWWdMUKSWRAmRZSMmSEEQU2mr6uzuG4wtEmtHOoLXD7vaodlMbGWSmlGmRpqRKTi7RzYraS6g1YQlZGaRtkLmWpkoYkUqAkRQ0KA1CsmSr9GMgnCaIGaUdbrMhhkIQtXtHWk3KBRULslWoprbaGw3oRC6ZFHqmwTOcRr75jW/y9d97m8Pzif4wcjpFUqpthULWadsCiSwsqVqVXxcy4zqBdtW7l1JIOaBEojEfcj7nNE9MYwWCFjOoPZvW0bTNAgRJYvT42TNOE2FpC6vr8d9LcvoHW7z0S2p5F32ZS465es11RF+hNlfXMPn/S9qfNVmWZde52Lf63ZzGm4jsUCRISmaSSaZ36cfqSb9AeriSSUaKlF3dC4odCJAAWEShkJWZ0Xtzmt2sXg9re2QULgkCyTDLjEjPCE9PP2futeacY3zjyz3RzyL2z9K9UlnOZ374zV/y01/9hoe3PzG/+Wuu737g46cHLrOnSENVjpQrNcTmqAiRGDxrrgy2rQtEihx7yzKvZB+QYjOxCIV0PZfpQqgJZKK767n5+h47djyeLjw/PeCsQ1ndqIWy0dilEejR0vU7EKaZ0fOK3e1x48DuboeWFb3ryW81IQUymRQXZB6xY89w2DexgpSUtJDjyrq03st2HUUpahWktLAskZITUliU24N2VCWoJTdxRglUmRpvViqMc+36qjWyZvJ6IeUV/IzOMzWndiqTqZ3DGEPtB3JJnPNHOmPRumI7Da6Z5rW0DRupYhs6IhBuQIiu2cxEExwI2UQLGUFOhZI1Eo3YCAT+fIHVM0jdoh9sh1CSlNpUPCWgKMISSNHjl4gbdySj8CGyrCemS8CNAzdf39DfVlLX4hqrMcRYuDxeeX6c+fF37/nxd++4XgJhgetlZV1Cg38JELpQUsuwNaZR6J0RKN2A58NOMh4G3H5EdB3CaGQtDPrL+Ku/R3Eu8/JFuFCbplit6bqurU82BEnJmRACIYQvbGFf/PgvXGVfTtDPAgQhv0Beqi+KWG3T3E1MsEUptOXm3/bVbyuTmHj329/yL/5v/1fe/uV/pi4znYiIHIm+IDFtp1gC1XvSslBDQpWK04qhb1TzkAIhJiyVXmt8aQtmq5sKSAnd/K3B09VCEgW767m5P5KAT+8/EdYW797tRtx+BF1Z4gUZFdoqcqxMlwspBNx4S7c/0B2OIBK9hH3NPD59QtbWjy/+ijvscYNpmmZZCdeZ6TyTcwN7SatxtnkJnYK4Xsgx0tkD2kCl4KMgeCC3OPkqC9LZFqRkGwNKK4EqkThPVBI5rIjUKO5CWFJJ5M6RMmipse6ApMOYHb3uWhx9zlAluiqyX8jMSC3I0iG0RlbRhn/i5RTdFD1sZEZJyyoxAmEVgoQpFRVOxDyRbUeVprF8w4qREre/5WnOUAMlVkRucrslLHx6f+FyTijd8fHdA3ffHenvB+xuJOK4nFc+fbzw+GnizU/PLEsBNN575rWtnF4A5rmmNnwUGq1a9orVzU10vBvZHzX745F+d0ANGuUaCUT/L1YMf8fiDMtC9B62gpNSoozGdXYLI1LUktvgyDddZy6tr3uJa6+8pHj9vJt86RS/JBcoKTfDr2yTys97UBqs67PQ4OUyW6E2KZYsTbSMaMICXl7UWsl+4fndG06/+y08fcCVyDBqdK9wxrH6yrImUmw5jTYlrKkMVjF2GmskQmRSaleYTiluOtV24zJjnKSqSqoC1fVNrqgM1RjUYFE7y1j2hLjiLwveR4RSmPEIRiJys3Fdz28I88Lp4UxJgvtvFVq9wlhFVYYiJUOphKpxtamfJu/JYWWdLuyPd00Lq1R7bimJUq0PSjm2AtOCJGrzeZYGdk4C/LK0qy6eWiLKtl1ov7tBu75N3JKHeGK9vEOVBVkyGvBrwo2vSFVzeXxE1NyoBqpn2DvyElD9Dtlb8uWCkoZaM0oW/LogkkA4TfIR7WozIAhF3bTObeWz8Zw295IsHdXsKHZHyVfifGZNCeU6UAYhmxHDu4F0fUZI0fA21oHQpCVxPQUuZwiraYqsdy0W4pUfOL6+4bRIfnrzxIcPM/MsmK6ZXBQ+LCzrSkp5M/YLtJY4ufXTgnbQSI0SoA3YTmL3O/SuR/YGYTRKWiIdC7/QbO3XtUnMPiMrm0XLdq7pJYUgldKyHmOk5K0wN6HC54eC+PmvL6V2bS2yZW3KbZXyxcn6ortteSdb/ylo19v6xed/wVkKQW2NcRvNp8C773/Lf/ij/4l8fmYQBWsEx96y2/fkDNfrynOJzClQZKAb4Ljr6KykN7IRu3NGCNVWSUZje8UaM5fFt3XHupDzzGgtbuxYc0bZjmF3QzceSGiOOZOHwDx7cG3f2O3H5tSYTyyXR6QsdJ2FatBGUHKAmtHGIbShz4VaJU4I5ulKoDaP5NMjVhtqbb26cQ6lLbmuWKPRQtH1PaJ4tCn0Y8/+eI+xHbGAUKBME4QL0ZKz+nHkcLxBGNviBZZECokSJ4gXKKXFHyyF+7tfcX6e+Hh6g9KZcXBIO9J3jv72gF9nyG2o4n0klMxhZ6AaUsxtsNYraqxgoLSS3DTTm95LtIZG1ra7BI2yHfPzAyY1NKY2Gr96pG7OqOgj12niMl0xWiOdI6VMDJGwRl4dX/H2+on5cgVdyGtiOUWUXrhcMk9vHplOiRAtorSdc4oRWTND9wI5VxgpsVq1diO0gZ1R7WGozbaTr4VcI6W2HayUHQiH/1uuf397cXrfFEEvpxgCYy3O2k2eVppKIwRijD+H3X5RmJ9nqHXbNX7h11SbXvbL6PemGNqo7V9ObL8wUIvNDdDWpy/DJ9FiEgBR234pnp753Z/+CW/+7M/R84LeqALkiEwGCVjhGU1kvBXcHh1CaJxVW6YjFCRCdISUkamF1Nz0PaFUzGPh+ZSYritFZkRx9NpQpcb0A914oBt3FN2QldUm9JCJoqWGuc4gMsi+R3Ekmg7nChWN6TtyCazLTGcao6cfhub6iQFZImk6Ef0KOXOW7ao/7PZ0/Y56uEOJhNGKeV2bdnYtLUzo5pZufwTrIERM5xBaNrMIBtM1aNaw6xDaENYKsbURnWnX7xI969Vz+jRzfv+vmafAEgPf/uE3DP0Nfr1iVCGHwjxdQEhCavyhw3iHkB3gCH5CIjBSN+FACijnNrdH+fxOaiPAQs2Bdb6ga6MYJu3IIqOtpkpJSitpDRstEHLNLUdzN+DGnpQgTYGUV1S+shtqa6u6nvFmz+F4g5QW4oUSBAaLth2zTxQJ0tDE+1pgrGIcuiZ0SM2VVKXBKIFWBds7TN+og6JGsl8oqpkusAOohl35xcX5QqartaK0pnMObQwSQS2VHBPBt8yI8tka9vuFKeBzkX3++Yu9ZjO3qs9FylaMQoo2JNpE7i8ff7navogMtjtPExeUlu0Rr1e+/9M/4df/6l/D5YxJAWfanwl+4Voj2siWNXkjMVahrSOlQCmpTf2UxDqHVJIYEqEkYhHozhCpqHFPFgvTNbJkWEJC9xZjNL4mfJpJWWE7x+5wxE8rpgMrFK5rIa85x8ZE1RY9GrTJjYmqKkUEYpwRi8ZuV4Wu7/CyYpJl2A+soiBSJiwT59OZ2/oH7Hcjd6+/IYfmjbwZb5oJISZSzVRpka5HWIOqoNsQnCgSMRWqkBQK3k/o6ii5rWOUEriuMX2ufmW6zvhpJq0LRnWkZSKnlVJfYu0LOc4slxNCG3wRCGUwncWn0AQB1pBKomSPkpmcPaLIRlrYzPmV0ibuJVKyR4jUrri6xx1uaV5NqKlRIKkFqxWptgm5Gwd2N0eE1MRc0TEwlMZRGl7vKXpHd9hjhz3C9Ahpua5g3BOx6froVEW72kKVlGw74c4w9g5ZK8lHYsjU0k5wbUBqie07TOdwtnXPJUWyarbKqhXIX3itzWljkLzsNq3DuQ67uSVSjKQYCdsK5UvhwYu2tRWm+mK405bMgpc90BeTW/XzifmyTnn59Zc7zvYlvXSuLz1mu/5ICsUvvP3Nr/nX/+yf8el33zdXQQ50WqGVIJeEzwXpHK7XaKsQuqC1xApDRRNjRltL33Wb1LA2Ap9xJCmZQwDtmafMw4fCcoFq2ppFmsy7j+/48c2OIl6z390jqqRU2t6uG1DOUmveBAFyM6q3PtmHgE8eKSPUleBbOHEqMIwjSoG1ktubHasRrNeZsC4godbUApysoQpIockAnVSExdPvNaZvALYMGC0pRZAEVKUpoqK0aa6ZtDYzegUpC7kWUmmm6v3NK4KXzHNjxFatuD+85vbuFmcNQkmsqkRRsLplkuZUOZ2vvP7mW7JotrOsC9lHqB5YoRRyEhucazsvq9gMC83d1HWu+SYFaKMpYSKHhRwCUkiGrjmZEJJBjwhjUN0A0mCQ3FrHsHggoq2iGkkWbaeaS+PzDkfH4X6knBZCzBhV6Iyhsz29s80GqNvaJK+JGbiGSK6FUgJVOqTVzVOrHagGHFNSQxXkAqkKsviFmJKXFYrYHOhmyydRUrX6yIWY0kYoy234Q9sv1M28+WKI1bLpZX9vf7n53YSUn1cpL4T3lx0oLwJA8fOp2Z4AmxqovuRuN50s2XN+/yN/8S//J978xa9Zz5cG/ZUZYSzWSAoCYyW9tS2GIHtiiuAUbrBIqxCptD2bUciakdTWb44jZtzj/EIsD7j+yu2d3lQhgpgiKQuezwv/4U/+gjBNfPfVQtfvkWbE2g6tHYJGfBB9oRqFJlJKwGrQSiJDpYqM0xWha5sGKkXJHikyZI8g0fWWFAKpwv3xhn7oMb1CCChFsKTAEgqv7+/Z73ZcrhO5FkKM7QaTM2meWNYF7RxDN6A7hxICcgLZwpIRNDKf0uSk6XY77ugxwy3rmponV2uc2/r04KlhRpPQCnIpTbQy7EmlYCRUKVknT8qZXoHe1tylZkpOKNO92Iuaumu7WVW2wFprGcc9y/NHsm8YFLVl46SSSHVrW40iK4Ucdgip6UZwpSBEA5vlWkk5kqInh4X5upBioOsybmkpeIeuY9yNHI8Hht5RaiKGwOV0YZquhDV9lmwrq+l3PabvQTsKhiItRbZhXamCnAWlaIr8xQwhPk9YldZY5zDWImRLVU6pxZuVkrc15IsS53P9/FxU8Pla+xI2JD4LDH4Gd73I8n6GTb9cW9mE7u1Dn/MyP/83m0Nkff7EX/3Jv+XTX/+GNM0tyVmCGXpM5zCqtpCeGFkuiflSkBqUEWQUYtcUP9EvhOQBw2AkzmjidpWnNhH+4bBD/aHm/uaWd+8u/PT2mdNzQAhFDJnnp8Bvfv0T/nThm2++4/bVr5BDe3rm0t4UQjSsR0mBmhOrn6k1U2qTm9VcsV0B2SFthxEtMBijKVqSS8X2sfW4rm80AwGlJswWp6C05vn0yH48IJRoCdIlkxZPWBbW64Xn84ndzS2u77Ebs0mq1udRSzuNXY81rwjrjCiC/asju1eSNeWWKRo8rDNxmglhRRm5EfAkomqS6Tjsbhn2N8iSqGECYbFW0rkd1naEus0dlAQlEMgWgKRonkxhGvxLGmKV+CwaaCy36b1WBihIrVAFcvZQC7rrqabJ8pQyzdBPZV58mwdUT4kJVcDWRBQBpxKDqeycbSR7ValhYsW31mWdefz0wPS8UmO79dmhw+xcC/0VgpgKpmpQlkxBS4XQmiocqUiE+oXC98+tqpRN8NzZxqMVbVGbYmiezfRCRvj9cdCXNL2fe02B0j+ngEkltkJlK0z5+QoslGpjdaHa59/WJe1r2zIyESAyNU6c3n7P7/7dv+LDn/0HhuoZBsUyF5w1WKObk72m5qwXqiXD5y1+0ICPHuMNWrS4t5ITaktVC2skporOzXy+loy0htvXO3Y3GeEyoayEJTGdJ7RunsQyreSPmbhWdNT0biBqA6ZviBfr0Aa8N6zXSAmFtFybHSsXil2Ro8d0O2qwlNhh719RhCFVMKIlKC/r1ATg/YitlTzN1JgZZUHWzOX8yLzO6N1I0ZpYMmFdmR8eeXr3llQC1bkmRpDtTaMk1ByAQt0ICVr2dNaSUyYJkNZghUbEQj5fKNJTCVhR0aLhbVLJoFo2q94e7BnA9Oy+OmBdTzfuybLlqxrt0LYjlUrMLU5BVhDSgjoirEeUjMwV7wMhepaSyLEwaoPTDTGKAKM0dndL7m/IpqW51VKbwCVfiHkiotjd3yLSjnB5QlAJsTKMFSMHKJocC8tpJmweYG0sOVfEUolLU8V1o8PsHWZ06N5QN46wkhapLNpaitR401PUjsKI/FtK8G8vTrG5BGrTGZotTZrt6hFTwIfm28wlfxa4v9jKfq5t+Zn783Kd1V+4UX7uReWX//HP/yy++Pu23uRF8N7CkDxPb7/nX/2//gcuv/stLgSu5xNhXdrAikrQgk4LfIqkvOKUo3MGIyW6MwgjWcLCMs0McsQ5jTASa4CaOD8/U6TBSYHREqkNh0MbMuRSUMJQs8Of3xLmEykF1kVQhcZqmHTk/YcPyKHj1pk2DEKSpASlkNrhhiNVK6IyhPhADiv5upAmT66PpKow456EQHUD0nVNSSMqQ9chrUSKiJMwzSce3j0wXWZqXFsq5nhgr76Bvs0NusOR+eMT8zQjdAs98uuKjk3/W9hod7UwrTMlZ6SzSCqlZMIa0Cnj+hEjDEk61qrJVaO1Q1tLrDPWKKQZqGZs2aLnZ0IMHF/d0e+PmH6PMJuUUZkNIgayZGjROT9zeoTeNiyeUtaWvWkUd69eE7QiTSfOp2eC9xwOe6QZEKZjONzga9uV913ffMenEylm+t4h/ITImfVyIfuCnxK6NlSOXyKn54kU2hU35kCtc3vtIygs2nR0bsA6h3VtKFhkQ7hKpdF2QGlDEZrLWrGjQQiJM780ZezlRim21Kgtxp1tevsyEEo5/Zyt+UVhfinVe1mT/H7ept5+vamHhNh2mvILSftWltsVtknqX2xgQK2sTw/853/zRzz+9Z/TpYifPZ8+vKWkAjWTomBe28Cqd6WllDkFOpJlpB8Nx+MN0zyQQyZMLXTpcDNgBteGV50hFkHtDcJZejdAlWRfyalipOPbr14T/rBwfrzw6WGGaClFoSWcdcaqM/3Te/b3N01AbnuUFITFkyl03YiyHW44IN2O9fxMfDxxevcRv3iKtgx3hVPfcbi/Y2dEy9jMBS0FqlbqcuXx6YnnH97w9q/fcn2a6I3GGehfebphaLtP7Vp/XEujzstK3mx/ouTG65ESQUKQkbR0ayVqm8/pNqjz0xVdK1L1GCVIWuP6PQINWiEKdF3H7vganxTzdeXy4Q0+BYaxpyLJ0qDd2EKBoP380tHU9hq28AqQNLE7VZDzSq0LYgvBkrKl2S3TzG7sGAZH6XqyVCjX0StNWFsCmw+RSIe1FRE9y+WJ+XRmPs9Ml5V5KvglkSPMV88yezrncJ1FaoGPkegTMWR8aHa3Ljddr5aNnq87C8ZgrEHJEWkcl3nFF4FRLV+mH3+htvbzb1KN5m7MNvatjZDgX/abnwtzS4L+UgH0eUf6c//6+wX7N3aYn9ctP5+iYltCl5e+s1ZEaSbby4cP/On//M/58d//W+w6U/3KOs1ImTjuemQVlKII3vO8XFF3jvv7A85WUp1BQyUyTVfiIrg8eZ6fn3C7FvfQ9R3SGna3r+m0RFnLskZOj58Y3AE/F5arpx96xmHkdt/xh//wNTV/4HRK5FBY5syTmBmUIfuVus7I7LFqaC2DVCjdgZT45BFCofaKQTou5yY7e/rwQNUGoS36OFBHjRwkRigWKciAyglKZTldOX14wD9PTA+X1ksqQ1lW4nwlK0VdwybVE+zubihklJUomVtknywYZQk+cb2cEVSGvgMqJbeox75TLNcrT9MnjBmxbkDISBYRkPR9j1AJ4xyHuwPXuRmfpw+Jq2+guIogbSs5qQwxBXLJGOvagBDdEumKoMQEhfYQKglRI1Kk5kRJmSIqPniEFvS7AaE1wjpQhmXxCJ2I69JiEbUF2TVN7OXM8vzE/HRivUQeP1zwi8SvDVWyLKH9P2dBSYV+59gPHWtoq5+SJk7LTJQFYUHqCjKjt/4fISjCUIvhPJ3p9jdUYdoc55earV9IA19ml0ATSaeUtm9uaeL0+nLfpBHQvrjWfj5B/6bw/Yue9LNU729Yv77kBNUtaVrURF2vvP3tb/izP/qf+Okv/gyuj6j1jKZgpeD17QEpDUpqPn64sq4rKXpajGjeouodWbWrxhoS19PK+dEzLxGpJcEXVl8Yhh1mcGhTSSWiSsEJwTovPD1eCXNmmQLP4kSJma9e3WGk44cfHvj0OJH8wkTloh2Xhyvh1Yy4aTu7KtpVzvUjQimuiyDlSEViOsl4+4rzx0+YocenzDiOfPP1V6R44tO7J7q7b6nSoZVBi0yYV9Z52XSqir7vP2NHjFKI0nbD8/XSpplaYIeORGoxCyJS84KoA1JatLb0w54XYRalkEvC9Y4aJ1yvSCny/Pgj4+4WZRVKtx1fFZFhZ0nR83h6T8aBFox3N9jbG8abPdIIEIm4nLDGQM6EXEm+Zb8IJcg5ImqkZg+p0RxLmKhxQam6mR8yiIqyhn44ooeeSGNVtV21ag+ueUJs+3CpB6iVJRWWNeHXynQJnD5dmC91e4+0BHOt2vu1Fpinlb42QY5SDZZ+vSysOaKspNRAlwzaOVw/bseLIMTM4hPdQW9il47e/kLL2EtilDGmAZ5VG9Gn3FKCY2rSPsHWa9L2Go008F/4hF8I4D9bx74oQiG+iFLYTuBWnC8Wsab+ISz89t//G/71P/1/cn33E+l6xZSCrC8a3ErftbF1igs5BpIPOKcZnUVqkKPFV8WSI6WAU4YgF0IKSGpbhquCGS10BpxBqXZqdFpxOV0J2bd4cqsgSd6//4SWgtevHF+9vkVK0LZwObdJ4HReeCwnzncnDq9n6LrN0VFIGbpxRArVRBBVU4XE3dyx+/ZrgmxG7tfffM04djx8+IlSVobxDzH9PQqBWM5QDaZXdDvLd/03zFfPer2ApsWlC0FK7daT6nbF71oglOsNSmXW+XkTg0iU6ujHY0NExkQucQsh6pAahLBIIbFK09s2BEG0eA2nW1oaopBKRHcDIRXs7R2dsai+bzvBxVNXT6wwX2diquxu7uj2+4ZlyYmcFkqcSOsZXQOqZowU1AyZgCQjVCPXKykIUpJKYSclJUbW52dKzsT5AjTur9SxTa2rRAy32NTD6SM5SRSFoevawUMmVTY0aEULQ9/1SKu5Th5jVNurVomfI97AuHMooaAISmkIn6fzmZQa5V4rw+3xtlEZflFxlgJbXom1FqlUUwWVVpw5pi+utG0jWcVLjHz9LFZ+uap+xpF8Ic17CSWS8iViYUOYbMSFppeVlNpwETWs/PZP/pg//uf/lPXjW2y4YEThepmRFayuOJPR0pJFpZZIrW1KuB8GnNVUWQiyMOfKD5+eSBl+9fVrutsdMkmyXzjcDdy8PmAHgzQC5QxWS4pvoGQhxm0hnXn6OJEu7cV5ejhTs+T+1S19r/jVr26ZL5n5uqDXQq97/MXjF4+lDdtKKVQRWeYZ4xrZPIS2w7S7I7uvv0X1ll47Dvsj1+mZFFeUKkzTif3dH7BcFqyorP7KGq4k5en7gbvjgctDYr1euMwXyqVNLEtJSCNRViBRbRjTKayTWKuxrj2IM5BCE8qLbWAjTGVdIzlEtD0wjpXR3SC1IYnWH8qiMNoikPTjHt3viLV9/4o2SKXJFPJyxV/OhNOZ5XRherrgY2F3e89X/+AfsLu7A6PRqjbZm0iQZ6gVLfvmQqlhA3RJ3DiCEGjTYi1KTqRpItZIXD3nx/cYK+iGkW7I5AJFKvZ335JtIM2S23tPuCxo0ygJUoFC0PcdAss8r8zTgoqKWhPGSrreUVCU3GJLcgYpDVIahNCcz5f2HrU9SmpujrcM3Yj6cgj69ypOaEMgq5BGNvxFLYhc2pV2m9B+YRxoE175hY9zO/iEEm3HtvWdL/1lu5PXzwxaidyyLgSISpWVJEHkRJmfefMXf8K//2f/A5d3v4N1IpYAuSBqRFeoMROBXC1Ca7re0XdN/mWcbH2VEczTmWtMvH51Q78bm3qIwugMVt/hOtPI3dKgVUdvepRKaOVQuuKUJTnLuHf4/ETtOp4vHjFnLnNkDJV+7OmGgcNREH2Ei8eePPhImgLk1sPi2ppJS4EWTa2To2epGTl2ZLVDDor93YHOKea3J1Te3DhzID48kEMiFs/lw3uMlBx/9W0TOeSKEZVrqawxIAgImem6hqFUsolFhNth779h2PeYdEFRSMqQqiSJRCG3612tOAFag0ZQqwN5A7TrrqCgS0KTm/TSHFDjniJ1E7gTkT40EUvwxPnM/OETl/cXro8zz+8/kknkbyZ6A6Ks2MMB01kEEZEyObbZRkoJZS3SjkgpKKZhW0vKoC2WyHx9Ii0n/DUyX04UFkqvUXWzDdKBPeJ2d8zrhN0H7M0zsXpCTm0MVS2kil/bKi6lwjyvsMAatoeULGir0XaToCqD1D1K9uQoWJdEKS1doHcdw9A3ba0U/+Xi+28V58t09oW+LqWkNis3OUVSipvb+PcL+vdper9PP3jRyraJbJvKtlO1qTnqi+3ri95TJc96fuI//tH/h//4R/+C9dMbTJzbHq0EjNa4fYeImbC21OZUMgYwVmIt1CJaBkqVGNNo5YMzuMOe/XGPFJU4TZSuXWtrLShVURKMEo2XKnLrO3Ih+IhUjn7Y8dW3PctZcLjM+JCJ60oqte32RPPA2s5RpSHOiXleWJ6u3C4RO2pw7ZSnQkiJNQTWHKlKcDPu6KksVJYQaLYbhbE7qIkwZYqe0c5Rk2C/f4W+kfSDI4st40b0hCRYnk+YvmfoLFEIogClO5QxuJs7Xv/j/xXDoef69q/Iy4TQuiFRciRnT43NSF+sA9XEGrXKxpnNEXJAlLYbliXQQN3hZTXd4hxqaxly9MRp5vr4zNvvP/D49sL1qeXrGAf6PDOez8jeYEpAG40ShZJXIDYTgHVkRMNeSoXAoZTB7Qw5rszzJ56fLvhLpMyF5fqM6yolQQoB0wdkf8SpEb8uKCPRncLtLFIeKLlwPU3EuJJybsYOqRufudYmJsntlDTG0u0cZmh/frwZQVV8WkgxNnwJjbnknEUKRS55E038guKEdhq+FOdLwTSLWMvT+Cw44EX/WBs8lJ8HQZ9/fpHqbcOgzwOipllBygqyUkVznSghSCnw/NP3/Nn/73/kP//xH5FOHxH+ihBtENU7iVOSGgqBSO9sO6V1RaiKNoVhlFvE+ZZ5UQaG3YDdD8jOoFQLasVCDJnkPUJU5nmiFzu63mG2kz3EwjRNLD5wc3vbhiVSYjuJTwXtOk4Pj22aHRvQ2UhFt9+hekvoNNPlwocffsKOe77qxxavZ1rLkEQCo7CHkfvXX3G4uWGYe9bBEq9nalxBGKQ+NJ0wmvXs8SIiJSw+4ozCKNu8g1qjdpX+K4G7vyf5GVkDVkhEUQgxoEyP0obT0ycul4qKK5oMaaXETL6eCecTcZlRxiFvXtEf7yhFN+pizmQypbaHmBEVamC+XAipgaT3N/fbdkQQljOnT+9ZLzPXR8/540KYNZfTjJIOROZy9oyPp3YSTZf2fgGEqLjeMW763ZIjWSikMQg1YOwOpQUxFtaYuE4BlSU5FcIaGv83Q5hWUpVoDEJdSbngtCWxYAZFjKI5p4zexDC/L33rug5QlOqbQF9WVKc4vjqwu9vh9jtmvzKdznTjnq4/bNo+sWnQ25xG6/8O+Z7ehAdyW2285HS2mIUvvmApmsj1i6L8cpXS/ueacFnIihBtHK+E+sJc3QqzxYxURI28/c2v+X/8X/7P+Od37GxGlRnXKSSaZVlRRtANDr0ThFU1F7qzpNxogMuy0o0CgcHPgUbhrpshWWC1hJqIa6CkNm0T0jR63bQSo4eaMQqU0lA09CPO7dDKARLTdWQlOb6+oyAQSjXYcVoJ0ZNjwowd++MBJyTnVLiczvzVr/8Tobf8avzfInSP0BJjLE53VGvodiNr9KTogUZeSFlSciFXzeD21CJYpjNIge4dzx8e0CWR9wN60HSHXXtDd4qKZklX0nKm5EKSFiMNuh/ojUapQtnIcdkH4nLl+nzm8f17nt+9bRS844HXfwhut0OojlLEZkxocQh1XUAXKB5FxgpBmJ7wRmO6kZQTsgbSemZ6PrE8V6Ynz4e3V0TVzbpWCkVV/NPEqjVoQYqZkgp5E//Pj5HxMKKMwN3sGW46SoWlRpIWGwle0Q8D18eZcRiQdY8Q6ybgbTc3VSV+urI+npFSYc02la0N/FUQyE0SmHNCCYF1TSk3TS0PKJWE0Zpu13F4dYPb7UhCcLleQSqMkBiRccNA1o7oG7PY9MPvrQz/XsUphMDopkN8qfCUUzs5U9r8dpt5a9sgyxex+pea2s/FKpom8MuME9HCesSLrECCEIUaVk6f3vIf/7//lOn9j1g8JI+TBS0suWqer1NzlJiK02CNa4E5xmJ0IaUJRLP6GK3od3uEqPS7xj/qrEErQYyZtAZqbklgqpP4ZcJ2mePNEWs1UpSmJ/Yt91Lqnlwqi/cY1/i0MWYGv6cCy1mxXDKmdnRa43qLHQ2qs2gh+PjX77ksE6fnJ+6Xha7vG0xbS2xvsbsB03X4aaJQkJKGSpknsl9AGErVSAqdEsSaySXjpOThx3esFHbHnuFujxkcCEmocH16JFzObQhnB1TtCAV6J9F4qiiI2oYo06cnPvzuLQ/vPrGcJ7RSzFPCdjtc7+huv0Zq19qYKpG+kOLCcnlGmUrXdS1xbjrxHCOH+2+3Sa6m7x2LrjxeJ+KSUUCpHkrEWYUVgjwnHt8842MkI4lZIJXDWoF6O3HYW3YHw/DqghQKd5As6xUvKkpEnJaM+5G4Jvy0IGwLTCqSFiEyDASfePz0yOU8Y2zP8ebIdJ0Ic8AvnlokRlsWmUglbJ7kSkgrIa+gMp1THO56bu4G+v2A6QfSGpDKMHSOXe8YRotyjigcKQZCiAx8xpz//YtTKbV509q9uNZCTpkYIzElSi6fC5GXXeeXElu+VAvJz9Pb1pNuE1vRWKWldZ9IBCV6Tm+/54//3/933v3Ff2BvJTKDoulkfRR8Onv+8/cP5CTZ7a+MveYwGoyo9MbSWU3nBEpZhIxUmRBa4rqe/jCglEDUgiwCQmY+z0gU3XhEWYuuAtt1DLsdWutGoCue6XLi+Xyl291y+/URoRS5JFTJWG2wXXPbUwsxLHTO4GzL/ShE3G6g1zfcKol4f2oUOqXo+2a+FSSMEqiaWU7PlBDQNbeHSI6E5YIoAes6ioDeaIqthHUFFNYNrLMnXK+IZUbEQOwdsRRSEUzXmfkyI6Vmd7A4Io+ffmA5P3D37T1m7Cgo/GXi6cc3fPjNj5weFtaloIxF9YFh95Hj0TaI9dj4TyVH8nKlrhfqdKYMDt31CDLzutJpjV9OaNeTq9wIjpbOeXpTEXvTQpacYhx7jFKEkDg9r1znxJwr2J5SIjmuaJHYD4X7G8Mrv8d2ioOIVN1WKEK2qEHjJLtjzyIKYQks3jMMB+zuSM2VklfiHEjXwJoC5w8XSqlYqZmnFWUMXTeQwpZPKgVVNKqB0pV+p9C95uaVYTxIjE1oWxCxoFVBVE9vRg6joUhF8K3NkdsGovwXd45/h+I0X6R9CSG2T7bBvuoXqqD6+5J3+cVJ+TIcakW6uU7qF2L4zeNehUYiiMvMxx/+ij/7o3/B93/8b0mXJ4zu0ap59+rGIH14fCZmS60D0yoIsbDMEUNi7zKKxOAquq+4liaHlhByQq0rVrbEYmohBc/1+UrX7ZCqPfls16Fp6P4QAiEGrMoYrXh1f48b79jvbyh6JOT2gMmyogRYq1FybCE6OWBUgWzQxrb92iDoXklupEYqyXU6Y9cDrh+Qsi3ISQVdCiknSgxtHaQlzhlKaTxZZNu9ITIxrchiG5irH0nzAlmwXhbkkpgW32RmQvN8iuToyashTpksA7Vc6W0mrT0JRZw88/Mzy3ni+hxI2aGMQJXM9enEcu7Zf/2r1hKETFmuXB8+4h/eYEVgcK9RtaVvGdGM0uSAlj3YHoYdqZu4ORTW0xVtM8J07O937Pb7JvN7+8x5TpymwiVXasrEkFBVMJr2wBsHRY6REhdSOKN0h9WCtEZUgSIyyIQ0FZEVQ3/H4eaOrt/jp5XptGCMReG5nK+E0IzvxbQvmZwJ1TdusxK40dGNepOxwoEe5UT7mArUNOPnCqmiaiD6gF8EqwVMJUXH7q6lAOSUNn7wLyhOpdkMogopNCk3GVNTnNCOyG3KWL8YDInaTsCXNUkTDrVYBSk0SuoG/aJNZ1/6TH995s2v/5Q/+5//OZ++/2sefnrLOi18fSfoDxZtHKFEUsrkkFGp4qsn+2afElXhlyvSLPQGfJKULEhR0A0tSEfmTJSRNRQuc0DJyqAL67xi3QEhW8iMEgqrKzV75mvztSptEMJQsRgzNrdCKFjbsfpIXBYMGdkZkJbOKFgXVI0Nu1kUaW08Gb+GNsnLnuvpPePNgLWiycoqiFTRpQ0PYlzItPgEaQykXXuYSaiyUnVbD8W4YDvD63/wFZOtOCKqZlISrFNhmlNb22j32flSrUDZ2trExXNdJ0JssRA1r2RVyKqldBml0QpqblA3VQuiZnJeqMuV+Hzi+ukZRKSagfvxgIhNq+ucwWqFKYUiIwqNEoZ+UNx/40B1dONIf+zJCtKnCh8qOUMVHf3OkSTEuLRMT2oTbGRBFg1RkwqI0g6HsHpK8AgJwS8UYDzeYd0R67qmcCoKPUyYNZJls5mZIom+kFLAyNay+XUBWdgfRobbATdqum4TpZQMpRB84PphRagF2+3Q3UBdVrRpG4mSM8bCYdfhbJtztPSgX7hK4YVWQIuiK5ts7+cr7c/Cg8+eypcedJtGSSE/A7peOERfmqflJq72jx/4zX/8d/z5v/wfyadPiLCgSqZzGrcTqK6QSU3GV9u1I68BoyRCJXrXQ4yfh2paK6zTaGtbMnPMdAHEKNA6kWTEX1vAjh4MTkn2g8OQyMuFWBJq10J/TH9gPFi00ayLx6+JmAT4tu5IeWaeV/zskWhM12+DIUssKzG0j9dUCbMnBo9frtS0ULRkuj4xnQ8MQwcKSs4k0VirFI9IgZQ8JQTICaFBGYNxDpEWSombI6hi+p6bb17jbMbVhbSsnJ4iS0yUBCGuuLFjP/SIkol+xSiNEh01wxo8c1gYxwG367i52yFZMbS0L9vBzcHSG8U6X7HOIEWmyIK2CukUMTbaesuDyQ1UVgI5TBAjiUQKM5UEJtEdFHYccMOAsBprFGMMuMFgXKSXGtH3LDmQlaDTGkPCmg0uXVvkAcIgVbeJWlYu13NbeyXPsNvjzIBWDusGvJ85n5/R1jDsdxzuI1IszGdPiAs5RYyzLeaP3AaPe0u37+i24pSiaYKXeWZeVqarp6IZ9woTJNfrSrcbyNmCbJPffjegjCAljynD760M/17FqbVu9IKXSW0p214nfr4rf+arbybo3/Ny8kV8nxCojUbe/uA2WK+Jy9Mjv/2Tf8lf/Yc/xj9/QoYVP88opVrP10mqaslQJRe6ruf+fmC9zpTYrs7WVvzq6Xaam72j7xTaCnLSCBRh9QTvITc8hu00N8NAiR6ZItZZnJaIOLPMEZ8jcIcbDyi3w4wHjB3ABrK8Er1Hipa0HZIn+ZmaMsrothqSkkQip8x0uZDXhEZT1oifr6Tkcb3GdQapEmE9E9cDqmZEkchaWviuyGjRAl1rjI2ToxVVJnyYicuFGFs2je0HtLaYsSLqgK6FLFa61NGPPT4kSo50WtE5Qw4zMa6UVWEWjTsMaNvcJd3hwHgjscPIze2V5D01JYbRMex7hrFDyJZkprVEDBb17R3aBNbpzNA5YpgxRjL2Di0yNa3k1OI9QlgQNWF7jewkbjDYXUfYzBPDznHzes80FcxqyUqQ50x1EqfBCEFvBa5rCM0qNdp2aNs3LKX2nK8/ES9LA6kN7fSqOuPXicv0xHy90LmOruu4//o1Sp7RemnMqHVp71FVm0DESYSFqgto0UKHc97mLQrtOlzVUFRDlXjP6iMBTz9F7C7gyCiRkcWT/dSu9+EX7jm1MiilP/sqX6Lcfw/kVfm9vvNl4PNCNBBi22l+0X+2a7JA1cT50yf+/E//HT/82R8j/IwomXleeHg+N56L7uii4HC8wS8XUp5QNvLqlaFGh/Cy4RVlJHXQdYqbo8NazeoTp/PKPENOghgTGtgPFVEKtSZKzJSYqSHgZ8/uuEMrTRaSjKGqAdXdINyhCbdV484oFdtUeUOl1JyoKVNlU05pY1DOcsnbAG1dWGZP9S1kx2iJ0Zp+sJjBUcvK5fkDpTtgpGlRChvioypaslduV8nwEsqjHUKK5ic1GmtdG6nJFocwdjuSX/BXz+EwcFlnakw4UxEEtG0ZnImMT4lcJf3xgNWCftejjcbuFnb3E36dqDlitGmT7vGA6AyKAikjVcWOHYdyxOpmDJDULXWt7QlFabSEPE3ILb3a2Gb/QimkaXOHHD3aSO6+uSGjuTxl1gCuH6jRYSRIIkMv6HpBvzNI/UIVMPiQCalQSsuh+ZxIV9M2UFqIYUYB5EqqGaU1bugJa8asGi06fIxt324VatBIo7cbSwc54teFznUcjzfc3Daq/jKvPD4/UyiYTiKcJSFJqSWfEZcWDlU0+DP811vO/9a0VjdekBCbhrZSaiaX1AqybNAlxCaAbzaxnxe2bVDSNHx8Lta29kxcHj/x5//uX/KXf/bHmLhSU6SEgNIKuaEap3lGS0enLKUWjLRt0a0r41AQalvmmkZMUFqgTWm93LQyTYGSHTlBXCvRKS5PietpwdjaAE2+gC70NyvHbwx2HFpUvO6Q7oC0RzIdZEmIiTWAqi2GQoit0ant6r+sK5TKoFrK9xpXlNXsuhvUIXF5eMBPHus0prMNneI0IUfm6YRMuWETa24TQadBKXLZ7HhCUHNEqmY3yjWjbddE1kpDbZjImHvQEd31SBMxXabbC9JaSWLaTk9LbwZU36H6ATWMqOGA6Ry6txt9cMDsD7gcYDM5KKnacMuYZt/KhVpS4/bk0nCfFNYYScmg92ML400ZHzylgO1GusORLBSzL6AMyo5Y1XCkBc/9tz2Hu1fMp8T1vJLTFtKbAilMWFcbsPmmx/WNau9j4+v6mLm5u8cpRc5rY8ciyLlJEZUQlJC5nM9oN6J0h+t6xFEyXyfWJeIjxFoxUtJhoGgkhs7tULVAbPlBxhi6cWy3QyPJRGYfuBmOyO4W6XYUYfBRwrSy1x5nB0qJlBR/WXF+Sc3Lpakaai20WLTy+b7/5Y9aC/BlnMLLuuQLeh6Vdbrwn/703/G7X/8pOpzRJRG9RyNRRnGz7wHFNC2okphOzygJSwloWXFO0/c7YpnIKeC6AesMVdQGsEqpQZZlg2P7tbnqc1TMl5aE3I8Sg2SdoFqBXyHJgXH3uilATDM+V+UIEUQuxBBZZ4+smRhWkGCMREqLkAWExA0d435knSa60UFXuRl6+iqwujJdNFI3kbbpepQxWJ0Ji+d8fiSZld1uaMwa0WDMTT7Wbi5aKYRUGKVxw4iokFD4IsgionWH6Hb4OqF3B3avJdZ5XG8wi0CpFgPQdz1md0D0PcL2VNtTuwHZdQhrPj9oX5RcNRcojf0ryc0NIkAWScjNAu+MJeSmJb5crkhruOlHzGCIMZGEQO12dMc71HgEaRmFg6rIpSCKB50bnsRpzNBRxUrMC3GNRBYQAakiUhfM4HCDRts2wU61UIVoUOxxQKFY12nLX3GAbhmwaK5lpfrMmla60bIfD6Qlsqyep/PMvERiqZg1caMUUhtyEERf0cYw2BHImwG8tuutMfRDjzCGQI/Z3Tc6vdIk1WIjfdGUJFDaoH9pythnjk8ru0Y9+0x03+zq231WvPScTeX+8wm5id8lLx8XVL/w8Yff8PY3f06eTzhVyYsn+7U5G7Kn30bMVdfNYyhQylGKwvvIOge+/eYOn+btaxXUnNrpXCUkiZWWzlTWOiFqxhmH3jypoFAYqJBqaA8R23iubn+LGQ8oO5CqJIcm+xMFgg+kmJo3Uhhiip8j5U2vUcayOx7arq4Gyr6jeLDaoLXi8NVr9OBIqcGcjbUgFTWLNhgLM8TcJIVWgXJAWw1AS7g2RlNqhrCirW3hQTSyO9K0dY1s00zlOkx3IO5XhuNCHyakqKiYMWqj4RkDxlF0R9KWas2mXBFtMtpEz7xIwGSNiFIoOZBExQDUjMqVXvdgBh4/nnl6ujDe3VBpcYXKQLfr0G7fxPC2pwiLUB1KGPI8kcO1WcRqJc4rVcTNBTVzvZ7IMXLYjxjTkaqnikytiUpuURJK4QaHLAaFQGNRdqQhnw0pZDotibMn+oxfPKkGlNBE5bheJ+Y1cJoT0xxJGUwS2D7R9YXoE8t1JUtJiZ5amhWxpozpu2Ze0KrF1+ctnrHbg3T0xyO7fY/pLWhL1R244ZcVp5AvsXytwGpp0r1aWjBtpvw8bBKb0EBuAagb67WZjvRnK1hNng8//obf/um/Yfr4Bi0iOReST226LCqiNPpCLZmxd40zu0ZyrGjhCLlyvlyQPFKWlb5rrFGMIKyZdSksc2FdMtknLBKhwGiBNQLnNNpIOgMpBoQrqFFjRot2BuU6lNtRVUdc/BZDECBD8P5z34y2jedjHJJNOjj29L0lLVfmpwc+fv/XiNjSrQ73r9gdbxGuY76eG3t1C3NNIVCSoOZKKp7lWpBGUDmQRSYvsckHu02NUzL4KyXbJsYWmnG/pwrVwGRaAxm59cCqj+hjRPlLMy1PKzIqMB3KWZJoqyKsJZsmGCGVzbq3WQNrROQVkTw1rtRlZkmRZHTrs6HFEfY7mji/p98d0NqhpGUYHVUIMoaUKzkkqhSUUFBoSlwpJZCBKh12u7VFAsZeGAaLqI7dbodUsPhKLoEYInqdQeg2BMyJkttaKkkBdkQqizKWeDqzhJlpWplmTwqtr/TThRwSy+LJ203ApybckDTSRKmVEDz1WtFSEZeVElf2g0XTVjhmdM3sUTQpGaIv0Fc6Y3HDgX5/gxn71qoIgbLulxWn1l8Mg2hX1pJzSx37AltZN+q6VC17UsjamnAJVYg25pYCReL69JHvf/0feXj7A3G6YK2i5IyqAqMtSorGJMqpfVwqhHT4AsFXqJlljpAlz09nelWw1pCywFhHrS3CPUQ4XReWJSARyCbtRZSKM5p+FCi1oruK2DvUfsQddihjt9O/mZJDaEbtvHpEzpAzNSdiLfTjgMwa6yxWG1ItCFEI60K8TqzXmcvTibp65GVG2Z7xcIPu9lh0w5MYi66VnK5Qrm1VUjNxTcSlsK4RKzTEwjjukUYhosBJSGGi5gXpdgghGbuBLB0pt/1tCHMjCIjAywsi1xm/ROJ5IvmCdBG3P4BSTSxBG9SgFJKMIm5iiggpINMCfmJ+emL6cCaFhB06sizYzlD6HlEL475D95rXf/A1WasGiVOm9eXziXWOxABKdSjjSKUNZrRpAUtVsLGStyFcP+FqQQpFFbLRIra3YA6ZvGacqUjRUCTJB8zuiH11Q5IGtCWXijCSy3lmDiuplgawS4VQPbXq7WaiMKpiGlqJftDt6twp0IpYaht6akXybUAXYrs56KoQsqNKTa4N69mLBvnKuYJoNHvTde1Wqv47tLUvUQy1lJ/x+Ntttm5DoZ/1QS/7y7oJEJpRmm2Zm/2FTz/+JU8ffgA/Y2tGhIIqFbWRr7XUm8FV4pcVpTWlSkrKxJyJKaOkYHcYiH6m7w22Uyij2sQSi02Z8zQxR8m8FrQQGCkwuq2DlBR0XYN8VWMYXMfw6ltuv/0ON+5ASHJKeN8Ks8TMOq+IGDBKt1DVnBGi0PemKRdzIAZPzKHxT2uzA71+/XXDVEpJ53qE0EhtcLpHG9d2wCWRtrxPUTKiVpRpmaDX8wU/LVihMc4xmlus7SBte0IKWvU0SGBE9iNGa6xt2NG4XppoJHqW64nLh/fMj4/408wyZZTbY3crqpPcflPo9D1a9A2NiWxYkJwQOSKyR+ZACgvX50ce3z6g0HQx4UZHEZUkGwFe6dowlGSs60gIUip4H5nPFx7fPXN9WBHV0g07shLgBLu7A+IgQLXXQMaGXZXKII0lx0wI8bNqSkoBpV1gSwLTteTuGAL+emZ49S0htLiQnCslhkZlTAntHOuyknN7TxsDxmr2+555WZGmUpTmeL/neL9jPDpc3yOlbt/v6HEWSImQMw5FlRopNclXQoaiZDvRTXs4XK8zRWnuhh3G2hZn+d9dnPAzyPnFRC3EJn7/UkO7lapopyab1UfWxPTwnk8//BXL80dknCFHQixoqUFAiBFrNMF7hLMtkkEKcoxIK3DSEK6BftezHztSqI2QbsAMgiKaOTbmzBoj0xJYfcYq2Z5yueA3jmo37ugOhqQDenfDzXe/Yrh9jenGJsVLuQGdlSJry7pNnruhZ1AD03QhxwWFbp7JNeJjY6jWkrFKY4Vi3N9QtcMNPW6/RypNlYaKQiqHAGJYiXEFCkprRKlo43CucXRyCNRc8DmSpUC7gSpzw2lsgTpCaiQZRGlgKyXphp5aV3Iq5BJIcWJ++sTlzSeWU+RyhUxCugl7BKHh9X7A1h0KkKI0ZEqOWwSghxxIISCkxN0dyKmidwPHuyOiJkqaSX4lx4RyPcs0sx92aKlZUyGnSrpmnt6ceHy3kLzE9TP3396zux8pSyDZtcUq5kROFVEFNcUGS7sGwhqoKSFFxRhJHQ2paMLk0W5ocwVRmecLvPldM3pLTUy18XaXdUv2VkjVoidqroQQscZwuNkhrGS3rFSt6Y873GBxg6EfBpx2Lc81FPbjnhTjxloaUNpSlUHoQhYZxAsovcXZLz6g1rBtOv626vs70vc+Fyn1ZyzJy7/b0q1+D/1TBXVD5YlSUTWDD5w/vmF+/oAsHiEymZZ4nStf5HyuVFGxgOtb0rF27Q3tU6WjIKwgC08/apQslBqoUuJjYvVwnZvAOaZCKqVFFdZKLBVdK0uOZCMZ7o4UK3DHV+zuvkZ0PQX5WUthpERtxcS+UR/63iBlASJpvZKuV2pKiKwwFLxfSKWtBoTp8TGzzjO3fYeuFVUyUjdQVE25MVhLhC3jxI0DSjR2q7SSnW0hS9knhNV4SusJq6aarkVHKIPUol3/ZABZKVU2WoFTBC/QvaHnBoQhLqWlgnnd6Hv90OBgywzZo2pur1leqf5M9ldE8ogcoGRECQyj4/jqa+YltLDcrqP4mRKaBjYsieoXetPjZ48zHeSKn1aWx4npyXM5J/wqMctKNya6PlPxWC3IIlBqRFRBDoXsM8sceH66Ml08Ask49gyDQxqD1XvW6FmmQO9agl2JnuvDW4zrqNoy+8ZqakAlRRUCbS0iSXLMn00d3X5AjYah7klCfAaNSaMwVmOtQmZAlram7C3G9E2DLgUYjeskSnmkbg+KmDK6bnElCEqqlFQ3RtEvKE6l1RfALT6bqV/WJ02eBwi5ffylcNuT4sVlInLi9Okdz+9+ai9ybW90bS0KQ04VUmTc78glbwQGSRVtOiqE4HKdeTjPVARDr4khk51sSiAtqfXnCHhQpFiQosn41IbaNJ1jf7tjfz/i9nu6/R1q7HGHV7jxllA3ofm6iQRM9/kJOww7impVWzcnfshLs1LVSt/dNJapUq0F2EbrGEvSmsu6UIzipuuwG64FCkpUlBGU3lKzRUmHsyO5ZHxcSALs0FNsaXECRrIUjwK0HRDWEAFkReuMsZVUYxMXlOagQBSUNdjhhv3dE5fhQjpFipXEErGDZn8Y6DqJVBVBoqaVGi4QTug4UcPaTs9S0bVhKCWRzmqsaS6Sea2Ncg9IFD4U4hJxOyipeSXmy8T0fCasgSUWplCQqVB+eocPO7791Q2lS8z+jJAZg8BfVpY5EAKs58h0DgihSYsk9IIhSGp6AA3iODCosbmbcma5PlNKj3Q9YEBZpDYIIchTKxqjmkY6pUiNgV6PbfKqJLEW4sbgMbaFXildETQhf0mxEd2la86jXKGYbY1YW37nC/1DSLQ222qyoFJjAf+i4mwxfRuy8oviqy+w37JNaF96TlFaKpUoiKqgagSC+fkj7/7yz3n84XtyuCJqm3BSQFbRQMZbIRlpyDURSyLlCKLirGN6iJwuGR8LY1/oZGKxgrsbw25nCCUipaa3lrwzXDpPXD3FNKG91pLh0HP37T33391yeL3HjTvsOKKHsU2lQ6LmFghMjnS2cWUT8TO2BCy5NGK70yuy3zFfnrmez0jTQluFUC2sRoDuOw7qBkpFaEu1HXa3p2ba4CIuyJzoXYeqTbyNFKSaG0BbaFDt+iqswbqm2upcT14VqndNx2wkShmkNpiSSOvc0rprxmpNmC4UCm7X0X11xx6NOE0s84SUj4zHrzh+/Ro17qgaZJkR6ULJC5RIXCdEasGwKVfWJbI+TpRqWhDvzR6jFdL1yCKJaaaqihAWYUdkf0TGSBafSDk3aNdeM4w9YfVcl5n+UvGrIUVJSRFBJqZCuM6ENZCqQ5s9qSzkKpl95nyZ6S+R7nFB6cr6esTUSje2kKCQK3G60tWM7o4tXU1JKI38mHOmhoSPgZgTVjfinu0H6tZiKNGg2lYLtBIIkYFAqQuQmgjEaHKGkgM1LkihMJ1FdQ7d9yhtG+9I69Y2bdfaFx/037s4eYFv1c0itg2EGjFuk+xVPoO6PsO6REIRULlwPZ/4+Ns/5+mv/5JwemrBtqqSlUSwhQqlTCclQhSMUeSQ8X5GKOj7js4alFSUIpvwfJ4ZDQQlkbkgq8LawjgajK5YVxgHyFFTskBph+0dbmexo6U/NOBwjIk0LxjVo6oip7Sti9ryP7uu2dRSEz6QM1WZ9gBJK2UthLXy4f2J89NEP+64ff0abSy1Qi615cAojdQCN+www4jdHyBVasmNiVNapkatLSI+Rt/yRURtBadaZLruDLvDEWX6ltTcOcqLtE+UFsCUIpQ2rY05kxF03YifZ0pKuOPIzR9+x/jVdzx/fGI+feLVqx13390zvHqF2B2QSoBfCSk2HE1o6wqVUivMkLnOHh8Vbhy4+IgIEUHFDTt6d2QtH6mxoLo9yu4oQmOc5u72jvXwAbtkvn31in/4T/53vP3hRz79+D29TihbkKatHsKy4MOWAFehFMnsYQqCxQdKLThjuKwr8umKdTDNExX41T/6Btsf0fNCCGdiSqgUETIT49wyfnJEiUpJzcivlKQfHd3YY/qBqgxKNOcUxSNrQJERJZHySs4rnbWMu5FC11ZDG7alqEq3G3HHW7RrrKndeCBuM4+cCvW/Hs35dyjO7UfdivFlStlUQu3uXMXLgnobygqoZIgXLu8f+OE3v+b07g35dIbgtyFHJuW4RaL/zIbJJaArKJXZjYamjcmUlLg7HrheGvmPKhiMJK2eh48TRhl2e4FSniAC03VFyEQ3SHIUCAW7Q4cbHVIVKg1S1kjitMwPLJVCjr45bqQkLoqaWgKYsRolHSE1feb09JHHH77n/e9+4v2bj9Q1NMo4mptvXlNU85gaaZAbIlKYDrRpT1BFWzuQyasgptT64hS3mIQtJ1NpqraofsSMA6rfYccdVptGR5ivQGmrDgHT6QOypvaE7ka0HTFdhw6N4tANcHQ7lOy5+W5luTzS7xRu7NBuQGjbiDPGtRtFjSzLgveRTgi0NCidUUayO9xx8+o7fClEEdBKUrQlR0nt9wz7DtcfQTpybAC33XDk9XffEZQDO7LfJx7dxN0rxW7ouPnqhm40xAQpemJOFCBXyeoLz6eZ0xSYQqLUinMQY6CzmvvdjqdpJf7uAZ/hH/2Tf8Dx/juen9n0tBHiRBGS5OdmL0wRlQOdzKhdx+444A4DwjqEcfTGkkvAr5maAqK2g0dIMF2HMx2ZJutsel5JEZpUJKYbOd7cMY57xvEGpSyrX1vLs1kp/yZE/e9ZnD//4bLtN1v2ZusrqW1QJF7sYhVyiHx894b3v/5zzh/fE6aFMoftStBw9V3fsd+NkDM5RhQFJSu1JrQSKG3JJSOlwl88Kmv2nWZwO5QqOCWZzlcu5zOtO1f4tWEkoi+QQW+9XUOrrDihEKqgRPuLAp2xyH6kakPwgfwydpeSqUZ2Q0cOK9Mlo8wB3Q0s05UPP/2WH/7Tr7k+eD6+vdLVSI2F50+PvPr2a4Q1hJhAaZR2KASFNrSa1pXROqRuTgpKIYRARrXv5Ka2KqW2PZhSBCGwxlF1I8wVNtSLtlAC1Wf8MuOfPlDSih739K8GdN9TqyFXR66t/9T9gNED4+GGeL8DlckSSq6ougFjpEF1I7IW0rwSp7U5U6Sg290gBgHuiLQGUwu5ZkqOzMtMTY1ztKwL0yWziAvf/uN/graGKCr98RWvhGZeF66XdzgXMXeOV6/u6PZDC+2VNEGLarS7mDLPzyuPp8ISFUuuhJK5Zk+qmUEqdJLEOfE8rRQM3/2BYbwdcGFhvWQu5yvUpQ0ao6fmQI4eET1CSrrBYcYO4WxzuWhD1/UEX1uERZFo0QBmUndYtwNhmOfCtAZyejF8KKrp0HZA245h3OGc23JomyLIWMPfpIb8guL8eVr74g6rn4UHBbVJ8hrPtqmHnt6/5/1/+nO4PGFSbozPSGOqDD3dYHFdSwZep4Vp8tSY6DvDbu8aJlNISq6UWPDXmes10ameLCPjrmFIDuOew23FWtV4r7FCVsRFcr16KmCspeQWP4BKDAdDChN+CRQlUVYzDHusGSAl1piIfkWISmcE67XZe2pMVOXp90euT488vvmJ9fnMfIqoKskhUpJuhIESsLan01uYT0OjU2i0/Ol6pRqP2cTqWWqKNGA6RC7IKqgiUxCNmyoVyjqGfWP3xOXScmukw2gFXpB8YH1+Jnz4xPVyYvf6K7r+FVVHvCjUJFFYBJm0IT4BtHVUERtYy0fI5fPqDGlR3Q67i6yXhct5ASrHncXsdlTVSIcqV2pqGMziM2EqLKeJy+PM+RJhd0N/+xVf/+oPiBm6veCb3cjDxzes/oo49Fh3Rz/uCMkzr6EFIReFqJaUPatvcrp5KWRtKApSraTctr1KaN49T5R1ZTSS8RT48O4Zd+gQooNqmM4PUMTmHirI0kJ9MU3nvHt9h93vEMOAECM5F5ZlJYWVFALkjFRt0JgyhJTJFbwXLGslF4k2DQmTlaQzA64btsJsx5fWbR8vpNo2HL8QU/J7IUP/ix8VSgHVVDfUTCmR86f3vP/Pv6ZeznQU5uRRGvbjSDdYdscBZRWn8zMlV9IaWS4rYQo4I1kWj1QZo9upnGImnK8o5ahiRaiAqgGJbnjLwbVrd8j4JUGEHAthbt84VX9+kCTvietMCC22XAlDTh21rAh6nJVYJVlSAiVQCkipLeLDis+xUQDmheX5RA2J4ld6NyKUpB81/aDJZUWbG3SWpNreAEKIpqwqmVpS84KKSkEidIfQCSETlUQu7comjEZai+ks425oHkoKq7+QigE5ttPVL4T5ytOnT+RPz5yeHqhY9l8F1JjQVre80aKx1iKqp1LJJSFp9H6RNVbpz1pVWQSiKEx3QB4ky3lBTbHFchzuEMMASGquLQIyVcIc+fjTA6eHhfkc0NUwXVfqnPn444/c3+2RJVLIlLRiTEbrtvQX7oAyPfH0CFUzXWbC1TOdPH5KrCtUaRG6EF/yPVVLWNfbsGy+zJRQ0EjmJfHjD++xB8vxIPFr5vI8YZXFKIu2EoulDC3NfP/qjvHrryn9rrlIqkbU1B5WuRIWD9VjekMRjoxu781YiQFiBKRiWjNnf+UwfIPtR4zrES2FGEGb+CpjP2sGfjHgC14EBeKLFcrPFHcAUcsWLpRZpjOnDz9h0hWpMn6ekDpz+2rkdndgGAYShcfTI09PTzjjMKI5+qtvn3CZAsFPuM5yPN5wd3PLOSS0siQRUUZibQYSOW0L9ypACbSWpNh2kH1nqNWipGmgY1mRCBSiQY9V/byvrMUT/ISRLa7NOIvSipoLRkAKkRwCfp1Zl4AWluPugFpL48wIzc3ddxzuBtyxA5FZliupWuLLVXXzFZYsoZqmopICrSxKGaJPVLGQsyKnpqxyxtKNI3YcN3P0Qi2REK6Iohn7DtNZ5jVTlGJOiadPT3QbkKxSMZ0l1EYjL0qjOtHM5z5SUyaVRM0ZkWvbn6pN51gLJSWWlMgFxrtXWNu3fqnrqMaScuMvLctKjYllSjw+TJweAyl22KrbZNZHPv3wW37qVsadw+x2DJ3E6CbRzFJtkYINO3I9LXx6+4g/r2RfSUEQs8RYx+FoSXNCKcHsI742OsayrG09oRu9YA2Rd+8+IIbK/+F//4+Zp0DOkoIgrIkYwEmHsgY19IhhBNfcOUiNqAqhagsNzi8qJQVSU4Qi5URIgRgzq8+ULChkPjydKHbPnRuwrt9g7C8xJM0T2upK8F+f0/6dirN+rvAmgN6mgmJDYkqx+TkzdZmY378hPz0R54WwXoHIYd9zc9wxdB2xZJ5PJ+I8M0oYLVhTYRQEpymxpUvJpLndjdzeDqS8Nv6pLlhVsb3BOLGhPiaylDjXI6rACEVpSUrYzlBLo8JZBdQmrhZCgeqwuyNKSWLJ+PkRmyN2uEMajbaG3khkTmhRqQlk0XSl4DqD6nvc//ofEf9h4M0PP6GEZPzqnrHvUSREKcTnK1koUAa0pjpLS21uVq+XR5+UklwjqQYymSorUoNSEmPavtdIgcqR6iOpBFKMyFqROVNjxvYH5HDHr46v8RHy6RPDzY5xPyCtIq2FgkHKTMyCUiUKiazrJrift/7HkLNsJ2cOyLQ2Rg6aqCSl70jrTLye8CGjQmKZTzydHzCmx4gB240c7joe3y8EUZGj4Xjb0w0ZVS9oWlIY7MlFUNh2sTkQfGa9Xnl8/8zTu5m0FEQWlAxJCLISaCHRpSC1o9o294i8OKUaeVVJQz+OnE+PuE9PBP8P8VGDGggpkk9Tax16QW8an3aeLGb/GmOa4KBKhZIvKp/QQpukJWGhSGIoxFUSYiakSt6OwYRCuB3S9A2J6dqk/YWa8bk1fDk5fyl9r92Tfw4mlfJlp1k3/H0jChA8l4/vePzxe5bHhyYoGB273RHXWZTQLHPk8Xxhnq84Cp1TtGzRjLKK25v9xthZKckxjiMxt9PKGoNWsPgFKSRGd1Dr9mtLjZkc2gspSkWLjQbQWUSBFCNTWMkxb/usQs4KgSLFhBARayRKGGRdkTmzXC/kdSXOCx/evOPydMZqw1d/8B2vftUz7Pft4SUlVmvoNapAuiam5yuPj88IZzHjiD0cUFoCFal004nq5iYpNZLCFclMbzJSR0JcoRRkBl0qdbkwXVNbgncWqwXDOJD8gkJg+j39/oakDP+b/9P/kaff/ZYSC3n3CoY7pC6IeUHEayOT14rKiRqvpOWZECaUcRg5kmshp4AoCyIu7Y0kB5Atav56OvPw4SPzFLBZYWxb5EOm3zm++u5rnh4eMVrSdUMThWg4HDrGfdcW+KrhHarQhLS2MCA5syyVy2ViWTNVOAoZHxNKqLZmSitFNsnmaT4jrOP1/ZGrX3k+nak5kkshykIIipQj2SvOj2dEVkRfyNuNwWmDthWdCiplRArEMCNKjygSgWlrkdKoE3V7T+VcKTk2ukVo9sFa23RfaYXQhn7cc7i5Zb/b4TZL4Jf68xejyN9+qf279JzbJ3251soXf19pjnyVAsvTA08//Y7w/EhZrgzOcRhGtHasc+R8eWJefdO1+oXjILj9as/h2CF0bihJUUghonVbbyiR8JeMqgrjdIvkqxVnLZ0x7Q2Ua4vZU4acV6Jvv9/Jxtp1fU8umUU0nKLpmlWslMw8TYjaTlalDKUqUqrUEEjzleX5xOXjMx/ffOTh04noM9pophWq6fnaOvr9wOtvviLlFpt3fThx+fDIcrry6eGRbjewuzuCKuheU2uzdFWlQZkGK0sRskeEmfD8xPT4SPJXpFQUbyEIUrhQU+Bwd4s63qDdnhwmYtV0RiNFZlkueH9lefwdu2OHckeuKTPkiogBlT1pcwzldWF9/gTzMzlcKClipMYIMNpwjRNxvSDi3Az3RpF85undI9//+rd8fPtAqZZOOw53PcfXt+xvD/TdSPYBa2/IYUYqjRsPLKunH0eU0aTc3P+aBvlenhZKbUqgsHqyz4jSrp7XawTa7YGcMErT7weUT8RS6I97xuOB+MFjSwEh0EZjlMCvE0ZLVCmsl4llurJc/aahLayqEvLEDsXeDlghKaKFQclq0DW30hGyWQKVgs0y5peFMC+s09Lsk1q3QKpaMdZxe/+K4/EGY8xnnbmUP/NIvjwtf/EqRWwq9heL2MvJ2axjFZ0T/nLm8c0PxMsTpka0aUVQheDj45l3Hx6IuXnhJDA4hda0oY8rLfVLtRu41RKyxk+Bx08PLNdC9oJOC1Ja6ceOvutbmCrNWhR8W0Akn1r0dWn7y1gCw4YbEVpyHI7s7nf0h54cFs6PC0I00sN+36NVJaUVv1y4PDywPF14/njm+rQiUgunpVbOJ8+P3//EsO8QpqJ7B6oynx94fPeOT7/7yHpa2sDJGfArMns0tQkHxIvYoH3dxESeV85vP/HmN7/jw09vKdlzPN5inaIbCp1NCAJOQO8cscC6LLjxiJGF5CeEqyyXB3I8Y4d77GCZleJ0euCoNTKvSGUpRRBnz/TpmeXhDTlecGOP1h3FOdAKVTMxBaK/4oylCs3Te8/7H595fOf5+GMkJTCdZ8mJ+3/wLd3+BiEEYblwnZ+oaWZ/2FOFQOpMFbAWyRrWNqVcWzy9dgMlFWrUEAQdClsEIuSmRdZNpVZKRQsQOaFKwomCqYUyz/jnZ0wuaNnaBlEyNReslRghmU4ncvCM3YD3ieADBYdPkuv7iawK471DVEktrYWTVNASKRxCQAhL4x/V1qcv68J8vSKFxPU9Qil8DEg5Mo57rHOfoy4/Q3u+yA16Oex+8bX2815T/FztjXiwidmTZ3r8gD8/4hTY44gUPbrviUnx+HzhOkW061iDR9bIq5sDX309MvSRlFdENWipkVu2YQiJyyXz/v2JuoIRFtlLhrGj65tTJaV2jdDasKwz0+I/W9NyrMSSEIJGGh96jLXYwXLY76miMM9XYk5QFdoqLieF6yy235PSCoKtGCUIh+k6QoLrdKUTladPH/nwxjLsFcq2J2OcT5w+feDy+IxIksN4y27sGsxaVGQtKNnEFjVHRKIppJaZ5emZD9+/4fv/9CPvfvpEyIldv2Ct4O7G8epoMSahpEf3E/1Rk0rCGkdar1TpCdMJKzLcfI3odhS3w+3u6fs953dv0KHQOQG5kOfA49uPnN9/gOK5/0ZyvAcpWgamkg0+JnXLCAl+ZZomnp/OzBcPxSJFT8xXTNehOkvVmlwjVRXGQ48oGmN0U1qVlWXNpCxbfIVRGHtEZ8luvCeunulpIi+Z9XpBZM9hNIzWtCuzymDlNkRbGFx7w4f1TA4LB5mJMlGF4nDYscwzcU1YqVACZC0M40BOiXVugUvGDMxr5ukxgFrZ3U244x7X5ybZc5tXWRuqrM2ckAJCqS2EuBHblRCI2uDZIRWyA7MlaX9O1pPyc0at+GK4+jdNJH+v4vx8en5R9S9id1kzYTqznB7ptMAoAwW0VOToefw4EybPaEdiKhQfMLagdcE60KagNFhjMXYg+sr5tPD+zYnrsydPmk5IhARtDaZ3aGfIIjWrj1T4lFHOYaVuQuo5UmTBjT1dt9/8cuBM3/iqVbJOM+u8EFMmxoobHMMQWK5XChIpFN04ootlfyNAZmYvIFZMymidiesZf32GvODUwOxXkr+2QUctG00OUODTAqulzwFKQpSIyIJaAzl74vWZ53c/8OGHH3l4/8TlArFa/JrQFMKlMn9aub0z2JtIHxMHbdFCUmNzkABEv3Ac96D3FB85vfsRdRvZf/Ud42h5fnqPLAFFIfsrl4dH/HlpILFXsPoFNXQgzGZy16AsIQRQhvvXr/GLQKaKcxPLWtDDnvtvbrCjRThJzRLTG7Tr0WIkxYBf1hYePEceHjwpC7SFr77Z042Cw76jppnlesYvF/Y3HVrBk76wLoGuCmxvtp693cByqdxVy9PzQoqBw72gYkEYusHyMV+YU0bLiDKO480ekSNTWBpuFMXj4yfOC5AF09VzfnrkeLW4sSVuvxABob2W2mrAUmUlV3BjJK4RWaBW0VLMEujRYF2HUmZzcjVCglI/950vhfm3nZr/zeKspWyZhJsvcwNDKyEQOXB9emC9ntCxkcvnZWrBOijqmhmUAyxr8ljXcf/1jm+/3eNcwLqW6KSUZb4m3vz0wMPHidNDoAbFqFrWoRIBqSTzMhNL++Yb0xNSaCecaUJwHzLKtqAlrSymd4ht5F1ywSJY55V5nlnngA+FUgXOKIqvrJNHmdjA1QiG45GaHYmZ6WnBKEPnemo5c3M8MgyWmj0lNY7Mbtfz9bev6M0BWQy72z1rXXh8fMKlyPjqKygZLcBJkNmTlmfS+gB5preaoeu5uEqIgkRCUllWCTFR5crrqhsekvZil5KYnh9QSuH9Srw+c37/wPnjew77PYdvf0Vc3zZL2XRiqprduMPKzPHQsXyKTRdNi7trb5xCpSFLcjD4ELGD4/71HV03sOsl19OZECtuuOHw+oZuP4CWLSrDGNQWqtzrHfM58fb7E6fnytt3CwXLd//gjoePbxh2jm//YI8WFzQR4xS26zHDnioL10tbjx1uRrQW5BIppbVUYS3sxp7asFGkVIhZ4f3CYaw4Lek6x/3X99zcHUjzhRwl1kpSqShZELLNUHKKhPVCWntIO0g95AqYZmesoI1sTNqiqU4gDgdUkcyXK37xIBUxFKxULd5BNY5WKWXTAdAke/UFgsfn6+0vKk6VKrLUBnQWpSVgASZXnj994P33vyWfHpFxbXzVCsYO1FxBeKSqRB9RpTJ2hoOzdEqijUR1ilolwUve/3jl7VtPzh3TvFL8it05MH2DJ4nG1xHa8fyQWeOE0M2cLXPEKomStJ2hbOP51a8IIVvuiXMkUfBpZQ6RNVWyaGlRUjdSesmBEpemfzWaNXm6u57Oe4YrFB9YpomQPfrre8avf4W+uWP3+p749EB3c9NWLPczORaUNJRroVt6qm6pz4aMTjOKFREi+JkcZoSomM7ges2uTy2aIhW0biGr1Ul23x3o7u9wu3vMeEMtC3W9NBnamigXz+l5gWtg/fiAWVeOvSRxBXtEJEWOJ9YaUFJx+/qG5C+A4Par1+wPr8iqDUWUEqisqdGSlgVrKyUlhNH09/d0h5tmBuz7Ruozm9qlzMRUWK4TXa+ZlsjlYyBeHX7K+LBiDz1Bj8xTxH+4sD488urecrhR6IMCWrJAt9dUbZBC0o0OYQRayuYnrZBDIq2J7As+JNa5IrKgHzWdc6ypBVG9+nYP0lPDijCRbigIrXBDR3fOrOuKsbVBuaREyozIV4SfQUg0kooGYRBVIGXFWskqegSyhUsF38wJtTB0DqtlaxFaFSGV3Rhc7aFVPru75C8PMiq1idwlkloKslaU0pxPF/7qr9/y+LQQLoGeSicLg2uJWXZwVKnw/ooPc9uLWkOqV6ZpZjzu6PqReVqZrheenh8oUZBCYj84zn5CGoUdBMJUfAoYYwll5fHp2nqXNVJSy/vUEnaDYxy6hkdMkRAi2lqcOXD/7bf4FLlOz+haGpcoZ6So6M5hhwFlW9BsVYacK9oaptlTVGW87ZifZ/Zfa7rq+OoffcXtd9+g+h7hbun3FlkkcrkQORN9oKSC2Sm+sr9COsNxd4uR4C8f+PDwhjxf6JxFG0ONAZEjWiRUXZC5cHPY048jJQZcJ7n/5qZNPEUPQpFrZfUBXT06t5iH+flKOiXCVCmDZL6sjWjw/2ftz34ku/ItTezb8z6Dmbl7DIxkTrehqmpJL4L+f6gg6EXorpKgVqO7a868lZlMMhjhg9mZ9qyHbWTdklB5u1kiQCAQJIPubmef/RvW+tasEfYE2pCVoijF/KtvGJ8ulFLv4nqPcgNCCfb1lf24kQ6ohyCKyDgqjLb4U6+glFLUJru8UGv60s6SCtzWHas9LfWb5eXrC7elExhLTDz/+BVZMuU4KC3gZERLi5INJfpKwg8G53tgkHW22wgznY7RQDXd2cFkaslk2diOjVIr0grmyXN5emA8mR4HkQR+1DTpkKFRmkI4wRAs2lQengbcqJAyo3SltR49glBIpUH01Ze++5Sd1lijsbJiJGxbhljRtmtoS60/6wNag5LTzzfmT4jYf6Tl/NuHM7dMuXM5uxtMsLxe+fs//pl/8x/+yvd//YwohQen+DgprJak1CVOftBczg6nBXpQfPz1hdFXcl7uJthETn0A8fDhhKgBrydiiAym4LwlC8O676QMzoJzgsu7R64vB8ciqKEgdEY6TUqC2hQtVyod15lrIdZCAoaHJ+RpwpzP+G0hbAutFYbHM+50RloJ1qOMJxyRcT5xeTcQnyJ5j1yfX1luIK3hV//k95w+fCQ3xXXTePeB8dFy5O/JslHkRigbtTXMODKMZ8b5wuAd+/aKZsPogFWCWiXeGx4eJsLWuThmjVy+OfHh0zfs6xvKRs7vNA9PZ6wf+NmfJ3tSVQ6J9Rb58sML7U2xrgfj+MB+KygVuVw6m+fttnaSfe3BwePjGbRm3w6y1Eh1IkpNdYb4VliXV1pWUA3bGlHzyDBOFNkZxipJWu0PWX//S6S2KO1pVfVJpvf9BSQyrQpUUxy3ows8WsErgRIe0QzpaEQTMU7jjcc4Q66ZSugtVkxdkSYVUiik6fJBrRStRGI6sM7y9O58b04K4ej7T8jYQSKswzRBrA2bK85ODN4wTKCt6CZyYxBC0VKktNKHZKoiZC+pa20M1vfQpKw4nGTZMqkmaqtUITrYDnopW+vPA6Gf7slGN4H8rbbzH53W0jqqkio4tsj/+D/+K/7Fv/wf+P7zMzH1t0gsPQPCK3BOkVNAtsbD04z75EEWxoeObYi7IobIvkdO5wu/+/1vCHvgj+GP1BBouTCNA0eU/Ie/v7KH2rElNjHPEiUyTnb5m3cOaTPWaGpJxJg7Lk32w9laRSiFHSfGxwd0zTRnEc6AFrRakH5AjaeOQhHQtMNOjtBAS4m9TIyXiW9++4lc/hmpFOx4QmiLRhJiJu8ZpwZSddTmux+zdDy/NR57OaNGz1EqZph5ev8tablRasH6obNVzYSeJsZ31w6XspbTI3z63Tf4CdxkcLNBG4VQklQN0s4oZYnbjWMt7NdAeDPUrHh9DpjRYkzk9vraD2UGYRs5FkI4EHdrVC6g3QkzXWjNYMzIwTM5FMZ5RgyONUaGbDlNnoogpfizKuYnxltJjZwEKUJQgtPwgPGQ6mfk3Vy/bJEQN7RUPJ0mhK68+/Q7fv93H/jy9T9S8g2lKkYorLLk1EFrHY+k6Gai0gfpUmGco+bCOAn8eMY6gzaS17drJyYqRdx3WutOqPEykpUgUUAorHJd6CHuwbhYwKPNgNKFUhNNtE4vKPekNwnWiK7VjguQgdIp/H5EyT4crbVXnv8ZqIA7W4v/SoZQK5WaC5XKclv57/+7/wf//J//33j++koRmnLfgWahucXKD9cMwEMtGKtZwsGxv1HrwYfV8+7RMfycM7FyTW9YYe9ih0SpPwkC4Icf37geiiZmEIJriHy5BrSqnJ2h7CteSkahKLVH18fUeriOESjTF8PG3dOute57sBJJWYPu+kntR5QdkMYQckRUgbEWY7uuNcVIrplDVkoxNO3IzaCqpNFoJbPuK9cU2ZadtEf2NZBjwVjD5fKEn6fuIaxg7Yg7OaQ4cVuvHDl35Mh04mGYMeeRFA+k1bhhwg99nypUo0hBlqCExQwaqQeIgXBtHKGyr5FUDM5NvF4D4it8GGe265VSCsN4otZuZm85st8y2nvs/Ih2nsE5bNP8+N13PH//J7SIPaZhMkx+ABqGDsmqKVHQfSopNfEIpP1gfXnj9cdn1NMDsnTG7zAPTJcz+59/JC0bUPDGMp9mQrjyti7s8YI0hnw00pHYVSTulWXdOfZAK2CUxCiBMX3iK5wgU6lSMr+7YMxIDAf7eoN79mrdIO3df2yNBRzj7GimD2TzXtheF5SspCMzqbmXws3SaEhlkUr077n0FDulOvcp7QtlXxG12xytMRjraUJSWqPUel+XdCcL8LMA/r9aIURpXJ+v/Ot//W/57/7v/z3/y//yb1mWlZgbUna7tRIVlOR1O1hrJqSB5WgkEThqY1sPWtj59sXzT3//yMd33d5VjsIRduIS6YrIzgVVGI5yYDyM1hKqpgB5E6QosVLz43VjNg1rKoiGUAJzjz/PpSBEw3nNPJ1QstPxarmDkWvuE7I7GNr6kSY025EpDYwEUGg7gHIIBcpZjFFsxxWjHTRJWFNfL9VMOhaW27Wrir5+ZX1+oZXKME1c5iem6YJQXZ2zho2NhNWW6fQeaRQpFpbrQs0JaTSTH3HzCesvSD2inUa5TnFTyiK0p0oQJlPEhvRbpwZKw1YrpdSe0jZk/FUwG894mqnWg/YIBU5OnZRuHWqYqK1xe/ncB1+f/0jbvxBDD4I9z4bBjqSY2dYrQtUerqRk/2xi5FhX9tcr1y/PrF9fee8HUnxleb1ixUHcI7YtXHztkk4FH58st5uCsrJeP2NMAamIR2IrEdkay2vm7XUjhIhSMA6a09kxnFwHsA0WN47M4wOlCNqtsS7XfsPGTFjgODK1KfZ1Rd0i53rh/PGEUIVaM/t2I+0boRXMeEYAuTRqy0gJpgtreyhShVYTadso+96T32Lul+fd1Vx/stwB3CNBaO1n29jP/+zuVPpFh/P6+sY//7/8X/mX/+L/ycvL0sfKSNrdYC1bRZCRTVOkYi/wEiRbzNzSwUEfAgzVMW6az88JysZlUlAaolSO605MjdPjE61liqi4QTFdDKoqZm3ZQuS67hyl0YSnxMLD7Hh6P2FE6ONq0fo4Pee+ay2N27Iy26GHxdTMT1mRrVW0tQy+h9cY48nlIJWOw1RS3ZfMva8TCmJJaJ851oX9BmTwxvd/NxyQdtK+8Pr5M7evb+RUUeaGkSOGnss4PwwI2dj3haM15vmR2Z9woyKsiZfXK0YXBu9Q2iP1iDIntO1BPVL3AOImBYV6l8JVtBtw48T5wxO7UZRQsfOIHy0hRC7qhPEDbTwj/aUzcCXd6E5D+Im0HaTbK6ZmPDunQdCs5TQabMvk/YowtnsRTfepgqbGQow7cV/Z3t7Y3q6YKtA1IVNhVJlfPXnerqE7PeRwl7oJRLtxmeHhQWNl6IOdXAh7Yk8FsmW7RpbXRAgJbfqFYbTCTo7zMOLOAyhJpmC8I79lSq3kVEhLJASIoVKpBJEJzze+7Au/079mOtOnr14DhtP8wOXhglKCKEo3kLeGog/B7g5wcoK0R9a3G8eyUYpCC4ezvu/W+U9y19YqtfWbs/YmtB/Pn1V7v3Ba++/+7b/if/gX/5J0y8iiMc4ipSSFndIKQkBNjSIauUIRgk0I1mrYS6GUhBMZYWA7Ej88K0oGamPS3brUSuVYenBpLhFlJKfHJ6wurLeMriCCQJYeOlRixiiBlp3wbY0hl0rJlZAiTTS00jTRidtFNoRptBaoLYMsWK/xk8O6iSZUBzfvO01WBjMiRJebVeR9KpkQSpFrT/qO+8Lx+sbX1xuySc7nR7zMvO4bYTnYrolcFKllnp//Da8/vPJP//f/DSVNnB8dTjtutxfeUqW13ptqJThfZvbtSoGu9WwJKfcezFQag7/QhKbJSqsN0WpntzqLmh3n3/yK8X/3T/j3//P/m3L7jiIKdpjQ4xnsBWVPSDuh7IhAIVtFhp34uhL3WyfskRGyMJ5P0FofQMnGlg+G+Yxyjyg79ZIwZVQ5sLpHE/a07I3Ba1LNYAzD7JjPFswrwieGeaBpS9g71d5oELqT7XOuHKESNohbosZMPgoldXatuFMdsRI7WVCJFDNCdtldaI1aAlYrktSscWc/JCF0Sx3Wk1vh9uPK+PjKND2inGb65gnfBM5PuMs7mvF96FTotPta+0tRCErrlVjaEmmJHEsmpozyDuct7s7B7f1mT4oT6O7bbfysFejnst7v2l9wOHPupANZE7JWjFKdsk0j1dpxlKL73LZjp6bc1xBKd4mf6HW41YLWCp+/3ri+Fmqa+XTRDFKSYqNVzfq2sYWN6ex5ei85zwOQuF0X1h9vmNhQwtIknIeBQXdtqhSFwSmiglwlpQrsOOCnGeU8p6cHrLcI0ZC1IltFi4a3lmF07OvB9vZCjgdVgvca+xPhXOxoQY82CCsqR/KxUY6VbXnlh+//StgCv/3t7xnGmeXrlbAEShKUomnC8Ha78ff8gB88RzgT9onpYmnNsNyuvD5/xRrP+/cfGZ2j5QGKoOaKVPkOkwJRLfWnYVet9w+/Tw6hIhRIWfn1tycexv+WP/xPV5ytjA+PuNM7xHChStMX4z8rVUBIjdAGbWyHaVWBG85IPwPcKQ59OGZOH9HuEdDkHED0ECktJVqJTrkYHA/nE/OjZ3SedV2JIWNGzcNw5nx5QGD4/P0ruZROGjgyznhShuOo5CiIG9TYRfDWORAaYQrDyXF6mHCDRQlBjunnNqWURqMitEQ4RXOKXEX3fKoCpjI9nPndt79mepKY2SN1pxYKbZHS09xMlRZajx2h1M7Vl53cjtI0kSn3YK5SGvt+YIRFn3rG6E/9Zl+X9MGPFP85vOAfUwf9o4czbkevp1NG1ULaVoSkE8lyI8ku/E5CsKeCLA0jClYJrGq03NAIWlOUJkhNo4RmDYrXG4jB9sa9REpNaKFx2iJpDFYgJgWpImZNqbonftXK4AU9oT332Hg30ITEC8e2J6oUCKvRg+shNSnemUEFmRNQkSVSj7WjPa4vncamJek0oRBYCaSNFA9iDhzH2q1xry/kEGkx3VEameX6im2GsgeMkHhrycUyP35k2wPX68of//AHWnpPWifMpBgfJrbtxvX1mXk6MWpPcZ5a8t2mt3dHkBTQHG5qvSQ+ts5krQVJH8hJKZlPI7fnz1y//BtKPPj07SPWO8zpAaZHmjvRlKUK/fNDUu4rECEkTVqkmzBaobXuJX7riJSGQBmLHD6g3Ln36jWwvCbSnTKhteD8MEH5yOgd3kFpBXSDXHGTQ0hDroXblxsvXxe2tZJSQoqMc4pSFLc10bLA63s8eziYvEUNBeULp6cRM1hSzrTjnmR3VzlZY5GykUtCTg1dBtwsacnhTifOT9/w7ptvePo0scdnYl4Jpa8KlQSjNaE2VM7Immi1kxBKlwh1oJdz/fdl3/HmWvsu1txXO7LnqHZCJQilfr715f8XpPY/5d7+gsP53b//A7pkLlaztMqaQndhILCqW6/kncpntekgZtFQSvbkaHpfmUpji5FKRVnPy9bIR0Hj0aWyrwGlBMY5tHIYoWnmfuWfDLOUCDQ5wxEOukWuYHTtxHMJRioKFWP6rZ1yxhuDVIpw7KRbxxo2UZBaEnIgNNhf3jjebuRcGB8fGK1jdI4cd/brK/HtSjw2Xl5eYQ2U2HsaaqMmgREjaS8ke4DIGNcDeYwwjFPjfOqIDG8rRmVEOdhfE/HY+fpy4/XrG9MUCLfGNDukKAhVOT2+4/LuHYaGbI24rQipenmkdB/Vt3rP+ahY73h6f2G5vSIVjE8n5ocPFDsjhkcwU3+rSk33B0n6ok51nu59Mn/kQhENKS1NSKpQaG1x8wVhT2ThqK30UCfRIwKlqHivKM3g7HtKzljbiMeOTBI3WYweqE3x8npjPRJvSyRmQyyGGCNDbYzjzGvo0+7RKZzu/FpGzdPkOD8ZxskR887t7UCrLq7XSiFlRZuG0KoHKklN85azGylKMj088vD+W5RxTGfD/qW/ZHOqlJCRqqJ0RcnQtbBk5H3KqpWm2n7J1JwoqcsIfxoSqbt1sbUMNWNNT5yrpSAMPZLy/hKB/3Rr/le5UnyNnGSjyUYskdAaRgiQsiM9YoXS8yC87fImWsEZg8iFHBOUDqpKpVBpHKmQQ2ZJAVLhwUtKTnjhyUfBbZXb7cBaweQ93miyjQx+4FgP9i332LdaOs1MGlrpmtqSKkZplFJoBKpJKI0WE3FfKXEDUXCD7RmXuVLWlf35hZgq1g6UI5L3nZQPrj/8wP58hSb5/OevrM8LWlm0tazrRikZawzzLGjtDT971KBwMWHsCCLw+E5g9SOfvnnk6WnuuMcl8fLllR8/r9xuhevrynYtzJNGyohzgnT0wdajMd0XeByUJpHG3oNt+40mhSTWRlMKM024dJBy7XkdbsL4C9gRYcc+z+gsNrgTI3osu4JcQSZqShxp7ZR8ITteZZhxWoLSpFwpJUI+Ol1CCbSCKqHILkO0wqBEZV1uNNEY/IAUltstEGOPkM/Ck6RDOsfb9pm3644Pirej5+esccVqyegFo6JH3BtLQbEejWPv/b8UoieplYLzFW0UdnaMDzOjkjQjsfPI+PhEqHRvaq33F5SmlUROvSUQtRLSrV86LWOVwLsextNKgVwphyQcKyUEtFJ4P7CXLnxvx04JW28DW0E09bNhW/+vY+n9rz+c6+fv+Dhaokjko5dBSEHIGW8l0+CQVVBKJUpFVvKeapxxAqxSvT+il0+VHgve6Lj+W4gMzqG0JJTW3RDLQRMV5wQPDydOpxkxTP2t/vKCkD10ptF7gFYVKRVK6fFw3g+0JhApk/cD43oqWI6BfOxIWWmqUquCUjG1M27CEnkWL/jTV8ZpIMSVr9/9QHgLKDny9jWyHYZYCqXuvSRsomeLPG/87rcP/OZ3H5kd1BaptRGOxK+HJ96//8DTx0fcbDm2nc9//sIPX/7MuhZi7n23olBDQ8seM2HUgjVfe6+3bSjr0X5kPJ/BKdw4d8oAILShJEWRiuF0QSdBlX0fa5XDuKHndkrZp4e1UO9pa0JISin3Hkp2UFvYiGGHWnDeQ41EP+D0jLcjJRxU0cvpdDdEpNQT1mIGP4zI2tGe1lqsMdxuB9t6cCyRmhVGGbbYtc/KTN3mtR33l3j/qxMIFLkV9iMgRF+DhZTJqYdopZjRtaClQIiV82VkaILT44lCRolKLTvhWDiqwwnLth7ECCFq9lApReDcQGmamDMlF0xLCFFpqVJUx2QWcwCCfV3JMZOOTDwiMRZkldhWqcdKXK5IZXBG06h3Gay6J9P9LWje/4bDOQCPl4lkAlaBOTIvubLGTFOWcXD9EIgGTRNKJVb6OkLL3m+W7p+T9+SnUitNdpxDboUmFc5PlNBYX/d759yo1TC4ilIJZbtGcw+xD4G0RhTIRSByIeVOs7PWopBsW0AaUDaiKv1ryF0wbYRENRClkPaDY9nvIOnCvi4sr88cbxPLuvDy+YW4VqzRHLsgNEORitdt7WKJKgh7RLUMf35hfnzg148PICXOGmgaa0a++fXv0JMjEinrirlGmjY90jD1klzcc5j7gAz0NaDUM2k/utTLWuaHC6fHC+5xwA4jxp+QbkJIizQe7Tyt1D7k0J7SMqUcqGzvvZn+OfEqt0qpgpRTj9prvffsAceJ4/ZM2G5475nycafWe1AH1ISWraeQ07Wn2g1Yp3HWUZtge33FeMfgHa10DawyI8dxZd8y2wJ7UGyp4bSnmUrYDwyi+15rwyrwRjDarsn9utxowlDQXNfAFjIxZLyQTMYwWI1ShZAP3KwwE4jcENlwXQ7EcKEkQcqN2xJZl0gImYYipQgiQq3keJDKhjdQVaVKMNKSVaS21ivCpsghc6yBFDPeK2Su5HXhuL4gjcM5j5QWJTK6dhH//88G5ZfK9y7jiMgRmVYeDTjradfI3go5VW4EBBm05hYLtwSxdG9nKwkdM64KlOwNd2uFVumcmgpNSmLu/WtKgZALpiqaNGg7EjOwRljTHUjdk7WNsRR6v1VS7cwXpahFsm9dsztYjTUGZw1NS2p1NNEHW6SCUHf4aCucHif8RbHnSi0bYX0jrjthO4hR8fb6zO0t8LznnkodIpenR2ptpNRXLte3yp///kfevR8Z5ob1HmNPuOGCf3hEOkcMC9oVxtkxnyzedy2wkQIlK01kYu12NrV1blLZD0qqlCY4Hm60TwvnPBONwk4PjO++7eloXYiCFH0tU0qgtkBkpZYN4y8IN4KylNp31dA6FrNVaLJPFpEoYxi8oxxXWlzIm2IRnfmj/AVrB7COVhpCKITu8fWiRkrOcI+0qFJSrMO5M7O6YG47+vNXhE40eqQfpXbtbQi0GKAqtAQlGlZUBq3wWlBzJgXYUuZtD2xZkJHEJJC5MKjGxTakOnBjxL/Ak5/R1lOyJIaIIbKFK9c1sq53zIjo1cPRtp44khNp3xhUwj4MqEFBzmQg5EwIiZ6UoYl75rj1cCdEpqwr8tgQ+428jazKUEbBOCta9ffh3p1g+RNRuv5SwFeJ5BS76gbBedB84yO6CV5K5boumGHsPrcmEK1SUiSGLmoeAf8Ts7PVn78gLWW3drUuz2pZY1rtWYut81RzzaxHZNkL82gBRUV0QBa6TypFo2rB6AdAsR+BbQ8YbZGmUxNCjIyDxzVPqomwdhPvNHqE6IDf8dzJ6FtKhJLJJO7WB/Z9ZV+hZYk2ljVEtv3gIhTODlSXCftKrpK3551wy8zTgJIe6QeENxxpw2qFEpbBjhzOcr44Hp8cg7/HwtU7WIsCtZCLIFVF2Qs1FCiKo2yI+Uy1G2u8If1Xas3MHz71KPmU+mETgpYzraWO2YwH5IiqF7SfEE13BlQDWXvGiWgCKF1cojVumhFkSjo6bpL+4FrRUKqvxlI+yK1Pv5WA9bayv92oqSBrRvm+b67aMT1MKBpPTyNpS13fe3SdbckSS0FqRSl3M78AqyRGGUS961qb6cb9+wskpow0ktwye62ImFHrzkzllEZa88DUwW770aMuauHr6xvLulNzuQOeJTl3TGfLmbQf2MmjqkN0RSq5Nq7XhefnBdFsd91kiEdfTAkRcbxR5oF8OiG9JxmL9wOS3IdFSH6KMaH9fE5/2eGUrX/xTVsUAqcaH33l0+XEn9bMf4yNqhTVOJqR1BDIVG5L7JECznWubM2UeNBaxci73UY0Wk7E2tlBzhmcEtQSSVRCTkjVJ79FSI6YkNIiWmM/MjSJ9WMfAFhPbaCEwqAw1lO1ZAsHZQd7MkhjcNOEFI1yHChjEaaB1j13cTTM0tD2AFbizcDD+yeO7Sv5KNh5AOEJ1xvOewbvuwxx3ylSda8efTKnhenSLFnJIhILtGgwdkKpETtY5kfPx2/P1GSQ0rKvO+vbQt4aNTaMEj8LpqXUlCyIobLdIlIWQlzJ9g3zMKFGT0MiiiDmhDMKryGlgxJjj4vvuwKEFsjWxSCt9v9Pq6VnVSqBtJomHEJUvBTksCGUQQ8zxhqkaHdWMUCmpoO8LKTlxvf//k+8fn6mxMzjw8TDr9+jH/0drF06he+hT7dz7muUdem/bk3QlOh7c9EHi53gp5GKzq+tEqUNg+lCDpkSuRXWLRD3HVkTa8wYr0CMxGi5boGG4OvzTq47qTbebgtHOFBWobTC2C6wl/2nSImVrSQ2l2j3WMRCYT8yIVS0kPzEoZRGdSullnirIEdK2KDmDhgzitIKusauMJK6H9CfxPB3ksX/5sPZhEJKjdQV1SpGNowVON+oTWDfzXyNghuRJhRJZZJqKNmoNIqUVK2pubDniGk9zs9LgeuobpRsFCSpgTWuR7AoS64CoxRaG3IV5FhxWpFTIqbMNJ14+PCJJPsSWEjJ6BzD5ScWbCWkQBW9fNbaIk1nBYlc0UJ1GV86OvrESZQCK2XH+yvJ9HjidEvU1G/35ch3h/2EyImcM2FZuolaJ+aTp4hI0w1UpbadlhNJGEBjjEMbiXaK4eR5+vSIrAPDeGZdD55/+JH1+YW8rmjR0LJiBsc0XTj2jiNdSeQl4kfPMCvsMHOUipSOaZzZU7eqCdlQ1K4PpRMYhBTIVnpYZgzUlDpTt2YyAu07EK1qDaIDqv7hnrPWxrosON8YhxFROpxs+fGZ2w8/8vz3P/L6+bUP7I7EcDlzQt7p8p37ZOcT0zvFOUMoBaECOQmENJ31NOjewrTut3VGk1NmPwq1CmpVVAkpl747lBIhM6X1FVauUOhf/7JWfvj8lf3IHfwsBCEljnRgXA+UyrlfADEW3D0uUhC53Q6cXCgnj7OSpgUCg9K2A8B0Q4qGH0y/5Y3GOksVqhvlGzihehaoVIhWSGFHGteFHa2bJlrJv+xwhqo5DyPkgsxdeKx8P0DvLop5Hpiukb++bLwchRQz4ci4UklA3HeWGFGtIBs4KfGy4URhcharLTEnUqnsudf0EtFJ2zREg+1uUlWyqy2OI1FpTN4jB48UDUpGW9tLXnkHL9WKbhY/db2jUN1kbZTHKIu6s3JkOohpQ6gComCdIORCbA3pLcPDiXXJrK8B3SROFIx2rM+fSUdA5ox3mtNZ8c23F+b3E/5xQJgukhAx0cJBNf0BEIOhxb5LKwVSaVzmE+P7D9jTxNtfLcfbV0ROIAunxzOXx/eEAMZYjv3K8XYFpxkfnjDjmepOXQYoDcrP5HQQ8wFNkFKPyTNuQrkZqRXhWGlpRaS+t40pUbXtgcVmvGtoDbIkqhAd2SE1tEqKCSkUW20c65WyRZbXjc9/+crt+WC/VZyfuF0zy3XjvCww+H4b6wH7MDDrRBSeICRm2gCFtSPDPDHMBiUbombIibQfbMtGbZ7SCmFJ5NgIVVGlQihNjomaI0KWTjQQghAzt+3g65cbt+VgPj8htGPbX8lIlLdIPXCEg0EPCFl7pAR9gJmA636ghKB6g/IGKSzOdQO1cxKl+sujmyoye1H9Vq0NFTM+J1rJPTXg2AkpM8zne9R9F9+0FH7Z4XxZE87R95YkmpaoaUQ7hXGVqQq8l7gaOS+Vq9Q818ZLk9xSH3vb2lBImjR0XnZj0JLz4FBawd7INVGa7JmLOfYFe5IUVXofWwvT7GlGIKxnHBz2fCLIrovS3nUq2j0xGAnKGKx2OGcQSvQFsOxdlXMeYydSyqjs0NFQ8wEkJAKpCqUWhIPpyRBCY91+QKWCKhFZZP9eDFijuJwH3n078tvff+LpVx+Y3s/E7YaIAREicdm5xRek9h2pKSumKo41sR6V8V1lnizT+ye0qeTVU/JBEYmHpwem8cx27Q6I2iolNZquPUtFGoQ0d1d94/zuI/vrC+1oSFlouYKeEHaiYskpkVNEtkQrOzVs5BgRwxlBQaj+WUl+umkhk8ilIun2NmrpvfgRaKWyxcrz287z9aBmiWzmHquw8eW7v3D++B53+QZhZ8x4wp0F1Y6ocWS9vdFqu1c2GjcYtGio1nNolhbx1UI1lNxvvW1J5JgQxtMKtBgxNAYjGL1mHB1Ka9brjRASMXaYWymZVARHhbwlhCnEWGjXnRwTRoJsdx2vVcRSWI+jq5h2hXAGIQXKSbTRaN3TwsLRSKkgVWMYPNPDA8YZrGgQV2ItHHtAO9/NIi1S89HZw/vyyw7n2y3h1ca7EcgJ5TqqsEhBEoXz4BmGyG9+NfK4Gdaj8rIkvnveebndg4SUJjXYQsG0wmQMk+tm6Rhit9TQdZFKSEQTlFhIpVG4A9CMItYuOJgfZubLGX+5UK1Btgq5y9iMUqQYiLWXfc4MICOtRbQQtFKJqWC0RfsRnAHV4+0LlZZ/4rt0p72QEnVSPImBVCVf/vADPguETPiz7tgOK3n38YH3v3/Pu998w3ia76YDQUmBFna2txuvrwHjJuLuGCaF1QotGst15/qy4+fHvnp4fEReRkpNCEUPa9Kaqt4o241c4exPCOUQZujAq2NBCoW2XXqolKIogxATqioKuiuJYiSFKzW+ItJBvF1J+0JFoM0ERXREaetcJQEI7ZDS9KTsuNFKpsaVeg+Iktog/YkoR4SNOFNBZLwR7NcbrzYynGamJ4UeR7AjNMn40JDGYsaZFO+3hxBo1/valmPXt5gDZRvaVLRuWCUwNaNzd5TEnDC14Izk/dlymSWnk2Y+GW6HRq8KIiQRKaJxenogvd2wzhOOzHFEbm8LksY8jIjWUBKsNUhTqVoTaiZtEZMz1pvOxkrcE9M11RbWY6fWytk4pmlGe4+RjWN9Yy3PoC1P5xEjCjntrM/fkbcXajp+2eG0ylFi4SUsjCbhvEA0jZb2zkYRKC2wk0Zb8LGiTUY1yaPR7HvpE1IEo4TJOJ4mj9KKGCNHiD2aoPWeVt7Jb+S7qqjWHnSkLEUoqtIIO+BOF6and8ScIQVyO0jrSjOaEFYyhXEyeAcp7+SUUEJC7QObnA+OsIG2ZHo8fKiVEhMlRXJtCKGR2iGUxs2Wj7/7DU7D7fUNicBZi7WK+TLy8O6B86f3zJcZpSDn/iaupaK1AWXQpgvW037cifUCL2Gome3HZ5ZhYHwa8SeLNA4jO71cWQNKYEWXo2lvIEdS7HjF/fW5276E7ZPFtZf2zpwQstLE0INdY0SJRtk7zrTuB3FbSWEBo/H2ERcbapAI2cg5IGpFuxHvR9aycewLcbuRY8Q6S9MDQp949/6RX/3211ydo4UN2QJWNrqcQCGU+RnT0YNwG8r6HlcsFPLoSdNGa8bThJKCeOwIunJpj5lS915V5YQTUGUjxgVZCl5kHk+Wj4+ecWjYAaZZcy4jiYz0XVlVmmacz6x74Dxf+OGHvxBCQNQuv9tqQCKwts9a3ODRg6HliNOmkzc0IMrPGTKtNbTRCAX7vnJ9e2Y8nXCtUEok1X7DPH38NYPV1PTGfvuB5eU7ckgYMf6yw/k0D8TthZRWtHPdha4l53kEKbBaUFVj324Yp7FOQevi8rO2HDZzhMpRC8JYRmcxUhNTJsQdkfqe0SmNkRU7WmoyHEcg5kxukELGyC4Z1BYQCjeMuHnG0Njffuy9hoFGYpwcxirm04AWlVwiKR8IumFWKQ9KUErqe1IhukKmFGLOPSDpCDQk1s9YJ9DGM5xHzPCRx/SILF02p5Ti068+orRGDzPz2bPvz+S8E44dJyzWGsYTHMcrotb+wTfBsWbi247aNmL8kRdRoL3r0YGmg7C1dQjnKLVQ7R0JkiolPnN7fSOvkZIyBUGRE5/VhhtmfvXrb3h4/4A2sOw3Sts5jogIb6yvLzx//5njbYOScLZgTx57ahgUula2sNJaX8insJJ2hawBbyt5D+R8631TjYQY8OaRf/rPfsN3Ho7bG5LSpY2Dws2W8fLYcZS1hyw3uiRUWY0XQxc6HZ1A4cYRozXGWGQp1G0lhMj17YXlFsihYdBIK5hU7TwjrXj34Jl9ww8CdKHWA+8F33wc+fD+RC6a59ed5faMkZm3ly+sS1+naCWRWpNKQ0nR/ZxZYO5/twJGCISS+KFnrLR7wnuIEWUUwzDSOEjbG7fX72mqI05CEpwv75ncREuBsP/IsfzYb/zhE8Z9+mWHU+YdXSPGdBEBrfUl7bEjtWY9MoLU94VK9wU0Basbykp0VuhScbVhvMA7Ca2xpsxkBKdhpLRKqgWpRGeKaihZcMSCtCN7TITbilGK7DI5xvsIujKdBlpUyOLRs6HFiKiZcRz60joWaurezPJTSrKQ3QNYdxqaQk+SFnQHe809aTv+dPMphfQdaK3nd2ip7il5PW0tWaiiIFomhJWUruT0Rskryp27JM4Z3MkjauoQtFxYtszbkqhZ9CSqGGB5o90KiJFWR2od8V4j1E/Bu5BiYXsNfPnulfS6IHKlCMPLdmXNX/jd3/2WchR0070c9BLvB5bbK+HlldfPL3z97sb1xwWrBecHyUkVjtdXFvs9NiykdvTAISSphP71W4exjjY9YO0INZPSQYwLocLoH3j8ZuLmC9Zo3DCjjMD4HktRpO7M1np3u4jWg3/uqkFtuyZaaN2HULJDsrb9YFlWwnGQc6IWgWgSbxTD2eAmg9ACoytGZobRIZ1Bq8Kk+82WQmO97diWUDWhW+T1eoWser5o61EP8q6VlUqSa2Xbc19GlojVFSnB+YYxjiIqKSfykTHNUgFtHMo5hDY02f2/CM3l8oSoG9eXg3SspKIY519h598gzPtfdjhV3XFeoYW5TxcbpEIKmbJ15o91Cuc0MWRy7qlLtfXIhFr6W8cZyzyNGKXY9x1REl7BMPZhTa4J4wwNxdevN9TdDVBqpZZ6dz6AyIlwu/L64w9MZ0/UGe8UullESuQSWdflnhjVlRztLiYQQmK8o0RJTgGlUk9/kgqUIsdIPGI3Sy9rj8FrjeIdkhGlPEJOaK1R9HWREIncAiEGQjzY9wj5DVF2RDkQrWeQSKtwo6MmQW094t2cJuwmqKkzT9txkF92Dl6pq6OeL5j5jNEN5Qdk2inLK/vXL7z9cOX5u4Xwunftp0i8HQJ3es+f//hnfvhLh089fbhwehwZLp7RDYSmyBGk8BxhIcaCnwz7Fsnlh54n8nTGTgrl+6BJiYJsAq1OGHNCiAd0LrQSEdtnclk6V1hF/NmxF8sREpfHC8PkKLnvvPMdeCXuiVzynr2jlEErhamma67vn3W+R9lLo/DzQFg2ShRQ+8tbmYafFadHi/MaSe5GaycZTgPSG9ZQWG8H63VnXyq6GC7edbH7rHltlRA6sEtrgbMeaz3LulIpJNloWWBkwUiBoCBlR+Dk2DiOrZsvqiaWypY6pUFWh1VnhuHMZTozzRPr9pltzSgxM55+hbucqWYg/42Uzr95OD+8GwnHDrUhlUBITWuat7eNY90xzjDMnlobJQda67R1tCLUg9o1emil+sRX0N/AF8+ybWgK3nV8fSiNI2Rku1t1WiWGiJa9lFAUVG3kNfH1LztSRd5/+w3n2dFyoqZIij2yr+SVWu6CewFo2XNY9s5otV6zpoMQI1IbjOuL8mM7uH698fb8zPnkGK2hxY0UFqSR6NynfZnGNI/M55GQBUepKFk5zzPr1xstC1ooJHaMMIicSNtB3A60UT1ewhvsaNheVpb1lclJ8l7YRc9kGffAI5CnAVUzdT/Yv/7I61/+wtv3O9vLwXpt5NSINVC1Q8XEcSxorfjjv/4jNf6GelfHTGePGzxm0rwzDwgl2NaN6d2ElAdCZGLc0UGih5HW+k0nhEHJASEnmprvNqlMqwGZF1Qp3dcoK9pYLh/ecbuuLMcBVvcsUimoJVNoGHomi5DiZ+eL0JqUEqUkWkldMOINdRrQHx4ZTEPWjLUb8aik1NBOYyfN+DBymgdy2FjeDkIJuDZ2iNy2k7bK8mUn7JVhMPz2d9+gvhTGk2W4Jd5eF2qVGGPY94P1eYX7blZQEEViB4kWAqcF09A15Qf9TIDAOE8tHZUjxwdwTyj3nmG+8PA4ENOV5XYjFs/l3Xvc0yeU00BB5l94OJ1V5CyQwjKOI9Y7bsvG85cXakyMpwlkQkpPioWSA14rJBqvLXs7fk79KjGhncHaDsg6jqNHN2hByz2aTVTRSXSA05raILZKywWaQKEhJUQqpOWNtHiyOVNzpoTIsW6QCy1lWi2YodP3Smss1ytHTH3F4g2xxA4gvh9OoXSf3q2BvCfw+v69FGra2d4iulXiGtm3yHR64OO3v+bh/ROpZUq8srwEvJoIa+K45T5FrYLtuvDy+cqxRqZ54OHjI9PFwkNFHQPhbUPWyjDOTO/OHKJiLGhtqbmSlo3teuX28sL68srxFkh7ISZNqgaMwHlHCAsxBrz3PH+5IfmBnBpu1PhZ8e6jY34Y2deNh28nPpr3PD7NpLgSlhtFdJiZMBaMR5oJKQxKjggxUYWlyM4dqiKTUTTp0EYjjUE7Tz0yTx8/oJXjttyo9U6DR/ycWidrL2O1MffsHXU3JzRySl2AoCTWG0R1KHFG8pHt/Mq2bcRcOwjcWbRzWGO6HzNYak7UVqgxEPeDcFM8/7hDFpAOrs/PfHz/RLHwrkAIhZIlyy3w9fnGl89vHftZQUvQQuKMxluJdxpJZVuXjmFt7Y7rEQzjSDZgxxPKzNRmMErT8sG2fCUEyfT0geHxEUZF5p4nm//LDpV/xGTmkLaRamKjxx3sxxvbLULTTOcZIQ37kUkhI3KlioZR3funvEHWAkhyuKdriW5UNbI/UCBIpSJRiEaf0AGCipKFUcq7m7zjBhH9FnTSIVIjrQGpJKXUO4Cs/7fGStx5RCpD3iLrcvD6esVYw+lpxI6q93ItE8NGa12FRK2MvmMitZLEcEAtKDQFw/G28+Nfn1n8xvH1gP82EVqHRIfSzeQ5C0rtJMCwr+zXjdvXhbBEWih94OE854cHJjeTrq+EEHCXJ56++YaUd0pLuHlCCEU5NuK6UkKklkYr4o6IKUjjGOZT78MrRBJHrMRSyCZw+WTgEOzLDiHz6bcfEXrBiS5/nC8T6+tnclxouVJS5dhq934qiVGeWnuwkiQhtERLQ27+vmesaGeRUtBaphJJte8QlWmU2LW7ythOn28CpQ1NKGIVva+nMGgNCZRRNFHQ8p5paccew9AicCB8xFfQxqP9jNAWWqWVgJk9rah+8yVJC7DviZYbonTUawgH3374FW2EqixKWnKqvL2uXF5GHh4dr28LR4AWA7OFcWicZ8fT4wVEY7kuCCVwzt2L0oKiMDkPppPhFSDizvVYeF137MO3nN7/CusnWr2balvrqd6/5HAeawLTFRcxZHZ1Z7Pm2sXs2iK1p9ZAOASidAE39eiWKSHQVvc9ZoV8RKxV0EDJ7gNNe8QO7v61FqTUfdckwGvJ6fHCum13V4KgqO4eF00xmpGUE94Y/DCTYybXTqUzziGUodQ+bfXjDMtOrI3SJxA/OxJareSQKTWDaoyzZ5wGjNakmEhr4Hp9IyfJ9S1weztYXePzlzdu64IdBO+ePM5pcIlWI1LUu12qkmPhWBPHUihp7w/9PDI/fWA4ndhtwdYJOZxJGkCiWscwytYYrSVZzSJA6S6ls0ZiYibWzDQOvCxX9OhJ+0HJjT0mUrvxr//Vv+fkFVY2li/QCFx+dcGOA6lWrutKTo3r0rh9vVJzQzvL/Bh4+NAY5oYyidwE8vD4aUZbh5YeP15Q1iEVtJLJOZFyQmtFbQVrLbFUYgj0KZqkNck4XihSsKWM1A1KIuW+xkDKDo0uFaUMZvSkZIj7gjAWWWy3HRpL030AQ+NedkKLBzJBOBItFXSOXEZBTQXnA9M4YGzGn2aKGbpcT2mGk+fyfuab3z7RMIzje/a3V14+/4Xj9gxUQuvx9rkJvHb40RNSYt8Do7I4NyCdp1mFEYFjeSOnjYzmMj9g/YySsmck3ffEVfxCV8rblzfM4Di/u1Bk7jBiQDuN0B5Ml085Z1luO9fX/WeuzuWiOc0D1I7AUEJB7XmJJXaKt9CgnMK6gRoSIQZi6gwdozXT6UylkUJAa0lN9OlrgecfX/HOIzwIoTg/PlIaiHmixgtadgxEDJ2YMJ4vPElFERU7uR6+Q+2Sv1bJ7NTSQKcuH2yV6/XWS7jQwcPLW+HIgpglR05dcRJ/4DQ5rt81zg+Op3cON4JUFaP7kKPUK+GorLdMTmCnRAoBIRvuMtFkz/sQyhJDYH17xTZB1jt+6rs1WQvj6EjnkRYrOYeelBYqX798YWuN83yiiY0jdBrC4CXLyw13Hvj46R3H8h3Ls2Y8wTi5TiMvguV68MP3ga9/fqNuCa01w+PB+9/CwzeZy7sL1g/UFMnH0b2zg0e6GY2n5EBJAS0EJsU+hVUWpTyyVBS96om5cho97Y5A8cPIOI+sLz9SQ6V/wD3Z7qd+11jX4/L0QJWeLHsWa9UDWVmEcXBPCzdG08JGXXZqCShRmT04IRGqoSeY3yuGSTJMA9XO3eOqFNIa/GVEKs3T+2+YTx/54S9/Znt75Otfv2N5e6XmRA4J0TxmnLuTKd+68Vw51j0TwwuPnxzknRCulNKYH3/DaZo7XpMuUOkkhvbLXSn7lslVIPXG6WmiyYpxBo/ryokWUVVTkmY/CnsESsVbzTCf8LMlpR1R4TSdkLWxvr6xhYNW+6GSxoK1tNLIdBxKBcZpRGvBbdnw1nRmTy79Gyxwfb7iveP0bsK4jNaGcT7RaiYeihoPyJUYA6XQhxXvHslK0ORP65PWUTqt4KS8IxYzSoieYBx7vlzLPXNTa8vDdKIZz5eXK2WthFukXA+MgbIbtHA8KMd8HjHOc9RMkV1AUcjk2pfWxihaSzQadhgRtSBKY39+RYTCy5cXaklc3veJK6LiJwucaeKGMLpH4r02biGDnjmiQmlHawdOay6jJ203RE60dNwj7iOtRlreITeOmHn98pW3L1duXyPlaCirOJThnfsVWQ7sh8RYj7ESWiWXDVEawhiakEjVA5lqjjS6aUAYD9Vz8gNSSm63NwS6/zmmV0pCacKe0HZAG0XcocYus4u5oLTo/BM9Iu2Jphe0kzQhSVVTqoIme+kvOl8YVTnSzrJv5BIZThPTo0f6hp4N47t3XYXlTxhzQRrV1yC+32JCSaoSXLdXlJfM+oSffsexPLG83livC2/Pb9yOSBEGZQdUEdy2yHUNjE8D1kDYnxFxQYiJcTqjlP1ZYsmdhvBfFWTUtGKLiXRdMJPFj5qHdxdSGUhSIYSilsT15ZV1u909cZnpNGEGS6HQZEVqTTOyW5VEv9YrAik1w+mCP59oZmWIB1sO1D0T6walInVBCcixoyBLbuTS8NOIRGGk4t3loZMORM9eXJaNfbnRYqKEgkBhJ4GcHFIJsmi9V7j7SsVPHlMhkDWTt4NYCsY6pnnq5ZHUDINiORJSFkYrMcVAkqTj4P5NkWNPcEY7mnboyWNOG2pesLFwmi2ndyNuNjTKz2WSysC2c/z4Qgkw4nhdVzaz4idLU7WP3S2Ys2S2nlwlmYSrF5I48bp2FIuhokQlLs841d0pby+fsSNII1EaYtopVZH2neXllf3ahxzH0V9+ZoPrJjm9P9Nq7sow0aj1DrgSCatOCKUwRnc5Zslo6+5PjwAhGc8P5FJwJXdjRDqQyVFR0Dq1oeSMVNzXWgZluvRNqBEhDDFHtgR7kixLJqcCwiJ0waba96itYo3EmhHlA6lV9nwg3MD7Tx94/PRAMgI9XpDzE0KNNCxNKBqtywbvtI5cK+W+3suiQBTUUjBSchpHaiy8vNxYt4AfRpT1rMuNWARnazFaEY9MSxlhJUVaauvYln+IKOlZnb9wIJR0L1tjTewpomvl9Nh1ps1ojLLsy8GxFqwFLTSg8aOlyUIiIFQDJdlCQGTYUya3hjaWh/fvmZ4uyEnjTOHJPHJ6d+L1deHtZSGGvaMGa7/hukdV3LH2jSPuxE3w9fNnpscHqhBo3WPAR9s9jst+41g3Uko4ZsRkEUbc9a89kkEgelqVkAg/EmtDVhimEX+ZUE2gtOTrDy9MtueCeJmYT5ZwRNYKVkNtiYJF2Ynh9B7sSGuV8f0j36TGcn5jGuD0ccDMGmE1RUgSsO0b9fmVFgsvn6/EJdBkwc0j4zCTVCTuESkd8zCisGh3kHjh6/cLpjZ8PhC6L9WNFky+A7KkbBgruXzzwPTugj8NPQio/pR7UilRUKqiSNnVL7cr3/3P/xPxdebbX58xaWaXGWTFn09Mjw84eQ8Vug8Ca20g1N1koBjGE7nUjtlUEmkk63ajURmmS69cikSJTnIU2nTWsTZ9uFg1KXVOUM6CdW+st0KKqSNBlaASwTTs6BnGgaQUSmvseaaWiBoN9jwiTxec9wgz0KQHock1oeicI6VE//NqQ7VO1rdGoUrh+esLP/z9fyReV7z1VCF7lZMCDYX2hnbPhHl6/w5lHEJ6XrdXjBK88wNVdJBAL2n//8AQiqoxjp6SQBiJn0bW7UaV931mqwhRmCaLU2dqBaUU8zRgPZTaMEZRCmy3SFxiJ6sD4zBghwHlLU2LToTTcJpGUA1tJMt1Y1l2OivtLo6XCq3F/bA2Ygx8/fKFH1++8vj0yDiOKDo2MoZIWHe224bNCe0V2tGX4EpgtKXV3NGZW6Dl2lk7TaG0RGjJEUN3/bfA+OipoRKXK5PTKAkhJMazxcrEeBkZzhfef/o7vvnNb1nzRlyvmGng6VdPTCeFEgf+1HNhjhRQSjE+vec1ZNaYOfZe8pecOwMod6TL6fGCsP37FnLC2JHpkpDuM1J/x/b5hgg7zQ+EULBWcJ4t02hoLTOdT/zd//n/hJkKRSw9ntA60tG6CdwIkDtSVVrJ6BCoX1eYZ+SHSNt3lvVGiBEznvn469+gjSZRQSuMH/paRPVBoFCSYZ5YlkyTDal7lYUo7PsVpRTOzRjT4wtS7tT+nDsH1jhLDHAcCy8vr3z5/JnXH1+5vb2yvK60VHvPrAvKNebHM+enC8Poma3i8u4dkzc9z9N1t5JCIMRdiSRE9/FKaPeDQ+0Y0tZah3jXnrkZl4P95crLX7+HBsPlEWVHlLxDu6rEDo754R12MGxbpDbDePnI02/+CXa+dE/nPRZQ0Ckg/xhW+m+vUrTlbV14evRoK3h96cE0UsD8bmJ+8BShsY8DZ2s64Fn8tMLNSCTUSjw2tvXK8XbQcmM6TajZku+SqBAi+3qw7wuGkdlrrBw7qFdJnl96pqe26m7ABqNrDyt6eM/b6zNhX7m1yuQtqUSOsJPDQamdedtao9VEy5YqJVVITNOUsLJ+eWZ/OWiHBCEYTxZ1NojWEfqlZk5PZwbtWd+uvHs6UUIibBE9d66RHSb8+cz5V7/l8ff/B9zTO9bnvyB0wLSGmipNOSoSOQ6IpghvOzH9yAf/gYdPf0dbC9uXBdqKcT3mTs2OJe7opFBmBO1AOYp2SGt45x/AT3wZ/4j6/Jm6bDy812gnOZ0HrJGUAA/vZoYPI4gMBZqoTKdHpDnzzU3z5U//Di0KSvb4eKUr44Pl4eHC5XQGmdm2N4gJEXd2J7FeEWXFnUe0kRh3RjlHKpkiNOsRqaKhtEA0QRUFiKQjEbVFVMGxb2itOm6ThtIaoSyFXrLXCvmorF8X3n54ZV8C2y1SU+O+eyPVwnbdqclRHxpq1jgtEMZTcuFYNmgZXw8YErFVhJkRVdxvsop0GqsV8dhIIdJa97qGdSGFRKuyg8pCxLiI9lOfMbggegAAfsJJREFUTdRMqxrtHVVklvWZIwJy4PHdb7h880/x/ozQkiYl5c7bb6L3nvwNEt/fzueMkdPkMVKyrwstJfZlRbSGmS0TEm0t2hq0bIjWqKXT3H76YWsh0EKg7ooLqXvsvHUCqRupBGI6CPsBqbC83rBG9RCh0mO84wk2dso9kExYSbuHC9VWMVZj7UwqkVwLpfZVwrbtHTSlBcIIqpVg+yJcFCC3nna2BpbXlXhr0AQldgaQdeCtR+uubzWj4Wl46oCwWnn9+gpOoFTHoOh55uHDB4RzvN6WzpWtdIgVosObpUZEuH555fWvr6zHX/jD/+sPuHFgtnC5OGZ/xqiZqiX+3QymcuxvKOdotXWygxqQxuMmyTe/dzyczixffiAsb5R0MI6O0+wQNHLKjOcLuSScM6QieruSM0eOpLLifOLxQZGOnuhsveTy4Dk9TswPM1IXSnwkXq99X3i7snz+DjU6ii4UO3Wmk+h+UKHU/eFrP09f293OFsuN77/7A99++zukch0QZlQvbUVvOYzSVAXaGEopLNeV9brTiqBkRUqJWhs59zZpCytVNlIeiIdmNIKwrqSQOizMKh7eP3L50PAPEjM18l3dBIK8G5LS7Ovaq0LlyTFxrBvxCNAE8+XSva/Ooq2mVogpQuo4kpwbIjW0Gbk8fmI8vwNUb51Uf/F3sPRP5W375bHzk1a8O505wpVjX4nHTjx25mnGec84TmAsTTRK2knH1rM9cuJ2fcMIOI0jBsnkPM11AYI1oHRDqUJtAaUrVgiqUAzOsCxLd4xUEGjmaUDK3mSnlKkalNWYwfN6e+0DC9HuScgVpQ21SdYQybHx8PjA/OCQXlBEpdbOJpVFoDBYNVLrRjgCNYFWinEytIdegozzhPEePY44rXsm59sVRoe2BudHpPaoYUT4jkMhd6eLEo2YAiUdXVihBMRKeDu4/fDGfqtUsVJOA/ZBk95Z1ABqlIyTx4yC0lJXrawJ4RXgiPlATg5lB7SfMB81/jSR0gt1X7CtYEW9v8wUxk8oP+G8p90qOI3UmvmiSJ8ulP2RsCbCGti2nWFyPDyeePfthfHdTM4HenWoPFH3TNgiW3nGXQa01rQhUG1HbijjUeYfIDgbaN0xLZJKMoV6P6CXyzvUwyOpgJSacXAgFcu6dWxlrUjRh4etavY90ppBG8dxBFJppFpoJZOS5PrWe/ObrOR9Q9QeflVz4eXHxOmHlV/9buX07ozyPQCppkJEdqmeHTCDZFsWQghs12vfEmjV816HgaYVerCICqXc9d9CofUEcsD5M9oMPUzXGrSWtPuhbO2nodA//PUvOJy6VEyjex5TJsWId5ZxGvDOY50npn4Yj20hHTes6nmEk7WInEnr3rm1teGMIeXUD0eN5CYhR6x3eGPYj0DNFWc8Wz4oOZNaoRqH9gphFC30t5VRBuMt5ILzYx8yjD0K73q9EY9IS6CE5vLwyDArYttJpRCPSErQDEzDyDjOeL9xmIq4w61pFSX6wGgYp45+lL7zdWQGB6qazpgdRqS2aG9JClJLvR9vHV9xxJ28r0yDRTbNfgSu685tT6SocEahs2J/WRFt5fTNhJtHlLv3TLnhpAY1gBnZDrguN2w1jA89HFhrhfQarQxKDchwIEqktoK2Hjt6rLPEIyCkZjpdSLVAg/PHM9P572ipkI/MvnWa/TiPnB4fUc4StoQaRtIaWdYr+22DqjmnxnDptHQpShcVqAziH0xtOwqrJ6QhsUYxjpZ47Fxf/opRAj+cifvByxJAGarStCqIIXC7XXl5fmVb+1pMSofWjiM2jnh0MYnWlKK6kL1CiAdxPTDKI4oihsr1+cr1eaWlzG9bQc99Qp+2wO3lhpaWaX4knyKhKNZ1Y9s2hBBM84lD276CM5Ime4DT+TSRChSpeuBUkaQEuYq7J7gHQcGdtsd/fiB/8eGUd3Owc47TdO6JYUZxuZzxzvP1xy8cIXZCXlgROVC1QBmFF5JaIYVESIVWZXeOO02TfbKnpKJp2I+dkiMhJWJK3X7WRIdPA02BlBJpBU0ZbNXd+Hon0F8ez7zdroRaqPvGbd2IqaDuMYDbEcE7hJT34KQuK9tzL5+G0XN+nLsrRUXcpDvLRje8dzg3IaRH2IdenomE9AotRmgNaT3OGYzvidkxRawQNAo1R5QUnB4upGOD1tBWoZymGkXWipQy8fXG7DNmMAgMbjzRhOHYIjV0jfL0cAHpQBT8PVDnWN7AG5QBQepc3lKQSmH0gNQKM44dnBw3cqkY41HKgaVH3SlBKZ4SAzoG5KGgNfwwoP1IFaaT51Nj+XJlz5n1CMgqaK8b4usz5sN7PIVG6uDq+60gZU/janfpZWmCKi3Oj4i8cqyB/eUNERUxNTIFaV3v6bRFy9Y1rqoPU1LK5FJ7srWyaOOQRiJUz1w1pvN/S5ZsW6blAy0MSuo7oKzw/Z9eUcIwPWmcVZAT6+uVWjtM4Ngz2k/s204KCSEV2jrcNJNKpslCbZlcElZZvPdcYyXmgrWSkAtnqRimqT/jPyEwfz6kPXmslP8yee8fPZxh3Ukx8vjhEWOG7mbXvYxYbytHzEitONZASxuDhtE6vOkIy1wKqhiO0jhiogowuud9l9zTg+3gyK2SSiWWDE0gSiO3RhEgtMJ4Q259paKtQpRG2g+uR8BNhr/8ZcefRtIRSctCia1rZWlEGsUY3OnSBRHErrhR3RB8xA0zOU5PA40zMSROcy/33CiZ5gFtLGY4E6sl10ypiiYsyhu07qh+I2WfIpveZ2klCa1zUFvosGQRA8o3UILTyfN09ryGwFESfhw5P85on1C64zJBUnOXstEqMSdqCvhhZtCW9UiUvFH3RjwiSqQ+pBBQlMIMXVtshun+AlyRGErtD5LUDuO66z/EnSNmlm2j5sT5NGOHEZQHYdDaQZUM54W6J1IqUATFJIouVFl7fmkxiOJBdVGH1qq/D0uhVEgFanMgKjVvHNeNL396wbmvXB7fMTx4/DyRaiKn0mWQg2EcHaV+Zd13UhawH103XTKlJObL0JEuFmo4yCajlOXYK0UUnp4u1Nqp/vum+cO/e2U8Nd49TjzMDoUj5Q4216pQ0kGrGalEJ+7pfutrLSk10Kroh1brPgvIB2+3K7MceHw64YcBpdVPLIh+QMVPN6Xok2Zx//1fcjgFcOyBt7cbw3kk5EReD87nGV0FfhwppXBbVpysGGOQ9xK2pMix7r1xb70RLqkQQ6bKhgiKRGMolVQS+3YQUkbQB0bCmv6DcQZpdPdWlkLNlRA2KIUjZexJc3q8MJ9PfH15o6VIiIlSCllJlBsYn56Y37/n7e2ZVFeEzmibqCGxHyvKZE7TyHv/iJSSwQuUiCgDqPvoO1cKPVlLKtmnrvK+vJYKIzXKCAo9VFjQ9cGyFcrtxi0FnNNoozBak53i/cPIkAW3rWEnw3g2KK8wo7/TBCXNRHJp5JLZw5XTwyPGaY4jIkloCWm/UcqCtwJhRhCCKLoSSbqBYjyyNITsVPOYM6oJahF30biE6rH6xOUsUKKntWltqXIA5UCAERr5bcZqyXD2vYx1AnmasINHKY9qI6IN1BK7zVB0mZq6ByZp48g5EePG8rbz41+/8PWvb4TjL3z67W/49p98pNDAdG10SglnNd/+5hPL9SDEH+HoZgptLUorjliwzuG8wxlBJtMOhVGS9pP7RQtaFsQCae0v+te3jfX1IHyYeHgYmOYLxg/405kjRNC6K6GkoLTWZXo/79oVSnWOrjQaP42IsKOt4vxwwXl3fyEVVFUIRS9zRd9zivuk9henjCEa3lukUKTUaE0TjsqNnWmSaJP5+vKFY1sYHs8gDeseSPHg2DeO/ehBNs6hNCRRqUdfeIetcHtb8GNG6PutLx3Kaqrszn+hNcZ77OhQ5v7G3yNNCQgJT2V+OjGcB2KJaCNRWYJqVCVQ44QZPXbyVAwpS4poYCUKg26JHDM190nacJ5xTqO0hBqpZadTdQOKiJYjBUkVuj9souCM6sN4VfoBbQpq4Ugr1B1ZDwYdKDX0DJCWeqzcCP6jRY8av/Z1gx4KZvQMlxNNe9Cyr2F0xYjY1yj1oGLRWpCOg9E4ntcr++0ZMVi0Tx07Yj20Rk4CLQDRsZfKjJzsGeVOVARNVFquuEGhxBM1H+R4UEq8Z9pMaOX6IZYG/UQ3uB9nKDvee4py6OHSX6KqIsg9PrBBbQWo5FJBNpyzqCXz8uULP/z9X/j8px95+3pw7IpSvuAfJpQbcVOXUtYqMN5z+XDh9//sNwit+I///nuWt51jOVDGM05nrBppSSCtYHAa6QTNC4rwhNr48vpCRrCGA7JCNUkrHRIg5YYw8P4yY8aZ2CToASl7enfPSy3w08u5L18oosPrVCk4a3j/dOH07j3z6YGmNJXSU9FF5zpJcU89uN+Yrf1X3JzVCMaxc0xza4QjU4rseI9SAIHSmuE0ob2nmT4V9IPFnk+o67VTtJXqGtfWQczHUdj2QhOKsGcKBanBDo7x1KkLWIEdXX/QphFtHbU0lD1QWiBL6qXkPPaHSFTc2K1aNnVxROcGNV6fnwkhdxyGs4gmusLJSNLRFTGl9vg47T1SSWSztCoptZByQupMZyxLcmk9w1FpjOoKnCJrd/cDtSZaC1AjiP79YyxaW0rub2IjHQy672ofFYJCZadJcK4bnrXxNOc5NoCMMB6hZ0qRnTJ47KTrjdvXN5bnhQXB+Slxrg0rNc0JUutDmtPpgdJOaDfjxgcK9yi8WqgiUe/xDa31FOcmeiknXA/c7Y40SXMKzYAZO/QZunka40EPFPkTXEJ3CJboGXM/wbmOfeP6w1/503/4e/78x79y3BIx0RVo+8K+JWpWtKr6kM1ZpDC0Znl8191IosF//MP3LLdILj3pSzCS84ZSmsmALRKVPesV3t4OUoItFkLoTFwj1d2Y0AhBcn0rDJfK5ZOjqZ5fKiqo+2RVKWgtI2qBO8o3/4NYhZQySg6c5hPG9vmGUD3ct/Gfylngflu2O/X9F96cZhwYLyeakCy3jRQyCo1MiiwSWggeT2dSDozjwDg4xmnADZ4WA7U29vWGMRIjNTkkpBI4Z8m5UKrsYOVUCDnjYiM3cCfD6XTCeNdhXOKnH1IvMXseTLvbvbqrwFlNboG9Hj1DVAhyzqSWUOHATyestQgJtTSqLGjlaFRyiPesT0mVAqns/ZaDVndCCCgZECagpULIRm1glLzHEzRULciWOkGwBODoB0qANAOiZdY1ICko3XDDgB9GhLO02ldRNR6Imgn7G2q64MYnYhDomok54IcLubpuk7OeLG68vj5z/fzK7cuByIrjGim58WQ82jeE1zSlSUKDmWhmJNEZN3ZwHOsCsiJ0D4QqRYC6TxhVJ5Y3oSi5UYQGLVGtzxSGaboD0RYEGtF6TqrQGlqXRUIPIRIlIfLB8fqVH/74R77+6QdEUMgqOY4V5STKdUGAkIpaBbJprB1Ryv5M1Hj/7pFPn95zuvxb/sO/+yvPXzdCiKSwoJXH+8o0afADWlWEFhRtuf3llRYVVo6keoAEawUUxbp1JI6ZEx9/J5H6vqOVoKUFan9umqSlHt4l6fDoWispRF6XjeoKH+5x81JKtNZIpSm1ddC05OdStrUOOpPyv3j8/vbhHMcRoURPVj5WVCloochrIIWDNymYJo9VIGKmKkn4KXQ1RkrMiFJpKaOkwA0O0Iijy7XWrQ8WGh30VGsPIRVNYbTtnrcChEKOB+RGXneO205Ngagkw0mhRo/0mtuPb4R169VHabRa+rSwFXKO2KbQ3ctNawIpDVbehRRaU+lmawX3iWOfONdSKSVhWkQKgxCyy69aNw8Luk+z1Y7ZiGmjtIil9zbGTTRZuL727BJn+gertcadR1I5CLmSaqXGgxg2xD0hq+eZ6DvLtduqakyEHMjbRni7cryuHC8FLSe2Erk8VEQUlD1hbI88TzmD6GGx1hhondCfy97p41RyzpTWc0610T1PRtiuKCq5p8S1CHknHwd7uZJjJO0LwlrcOCG177eE6T/X0uj5JwharaRtYf3xC2XZkMmS9oT3Bn8ZePzmgnKwh4UkCpPVKNUwBkQzaNWARBOOf/p//G+Yzhf+1f/8R77/61fms+PxaWSeBafZIKtDAKVltLP86U/PKBSt9YtCG9EljEpzpE5eNC87y7pz9jOlFWqrP8faI+hM5Z9iFKqgFEGqkJsgxoByvXQXUmCMQWndrxPRE9yk6HzlBj9D6v6WhO9vl7Wisu4LtMLj04ndbNSQKTmjrO1A59If6FAilExJGSUhLivH9YbRCqog5tz5qbmy7Illy8TYEFJ3JIUEbVWn3QFxj1ATZrCgG6op4hZZ3xZuL2+UnLDO9ODZciFcF5br0q1I9HyWJiXGOpyzvS+sXTCt7kR48bPovceFh5JQsdJqBmXRIncxRCl99UNGt0i93wYxHrhxQFDIxyvp2KhxJ9fQgdi2m61jhRwSOcNy3dlrJI2FUkT3i+pIpXTRuFBQE7ImSAe36wJknDcolZHCo2onLGwvb8TrBqEhqyDsR58PbJX1ZWGQGm1nhHVAv+FLixx563Hz9zxOeV91CCG6+Vh2G1itqiNiquigrnR07u3zZ7bX597alEQ5Dpx1nN5/Yv74a5qS1CZ7e6BM5+c22QHiQmKUxDvDNUSGSXP68MT84cxwGcFq1rDy/jIzTpaUOltXNEUuHdamneXdpxE3zgzTyB//8Gfmk+PpyTHNMNxj+y7KU9qNr98vDK5CasRUqK2Q4j19T//04srEHEkpkXOk0gUVpfXkNaXUXfNcKLlRc+5JablSpMQYxXia+r5ZCoRWnfpYW/cNi75Sqa1LDn8qbX9xz6lnh6KiFEzjgD1r0hp7xohSyFaRUmCNJJdIaZV5cEDl2FdSyp2i0GqnZudCSJk1BJYjUavEe8V8mkhtw/kesR6Pg+W7G9PDmQfpGN3Qp8Zfbjz/8MLtulJaw4+WqhvW3jH5ttPJU4Hcws8lkpSq54OWRDoCWteOMdT3oJ6mqFmQc6CGnXJIUtMYLVAajHUYbfoOMR/U2jjPI0sptLxQayLvr4SX555R2QpVaLQHJWwnPaSKlIqaKm/Xlf16sC0H4+IZ3zsuHx7wj+/Yri/sty+EGBnooux13Ykl4l2FlhE1U48VkTK6SryxRHNfPShPq/D25YXruvKhaewRwCiG84iyBmE776eJe+rVvZroutqu1JHSkAuUmroAJQdy2EnLwvL1hZfv/kpLkLYNVSN+cOSjMD2+I+0CXSVCGaQ3dwSpJFfJMD/w+O0nEoIyLPhp5ulXn5CjIwvY9wwpE3LhOLqJu+VEqYImDPPljPUdKjefBb93n3j/qws5pY6TUQ1nJFUHhA48JAlJ8HjWyBx7SHCTpCKIKSNERsiKdmBHjVCNGA96+VXJOXbukbbUKu9VVG+NSi7UnMF0EYix+mdDxk8Hr4l2V9N2W1Vr9T8ra/+WQeVvr1KMZJg81qvut/MWNWq88ShlyftBXJeOIqkSZEM5TYw75S4mblp1eV8R5AKxNZpWqKGh0Tw8nJnngS2kn7GIMURiLrz7MDAPM0oKjmXn+rrwdj3Yj9b3ojXhLvAoKtY5tHFULCW1O+IjghSUVmnpQJeK1f3FovqPqkfjSUOVglgCMR7UPUMU0ArD7Hh6/w7ZCnG93hmyjbh+RSp6Dovohu1SM6o2rDbsd+odNSH2FY1EW4mxigNB3BKagDIFO1XC4fHDCe8m8nFFaYO2jvGk2OLOHgIQoSTytlCOiDUGbS0xvyFEwTowvlciohXevr6w7Zn58Qk/T/DNI/48oepAkxahe3ZqLd0LK+7OjFozrXD3IBZCCZ0be3TbH82x3hq3HxfqcWApjGcQPrJc32hT6ixXoRFNkFsDaZB2wJ0aT7//PWKeucSIsQ47TXx9u/H1+cqXz0tPrCuWbY2Ie36otY7pZLuTqGlKSf0BtoKz9Ry7pOTuqfXekXJXpmkXMFbw+ORpMdJKxciRkBW3tAJdd3u6eJ4eZ2oOxPWgtYIgk3LsPydbEdLdh4T9Z1NaAdFB1ELJn4HZvSTuSqVaG+jOsG33A1par8T+lq72H785KRhZGGYPquIYeop0ldQs7gqNiRrDncwuOcJOTAcoAVb9TB1Q6r4vcT0h254FVhnmYUDWjE59S5tS4QgFaTXaGZqq7MfG8vpKuAXiIcjNUFqmFoFS/WaSVqGsg2b72DsKRG2goJAptZFThAxWjQjdD5JsCqkcTShqCMTc2K87YodwbKxeMCiJTpmUN6zp/VgvfWA6nfsHIw16uODuFUWVBzX3lYqrigYYW5hOhrhotj2TUsMJkLa/2Gq9K6eUoqWD7e0r2nSawFYaIlZqyuzXnXVZOI8n7Owx80qumUEPTO9HtNcIFDnc+P6vf+bhfeL8+NQrDNUxj2iPEn3w1SjdKJ4zNYaOTJF9Wtq0pmXIR6IeERET+5ZYV3i+HogMNVSGI5HdxvDDF9zjSM4OhEbJ/rko7UhNkArYh09c/IVT6xrnYzvYb8/89e+/8B/+/EJB87LUnvxdM1ZZPn3znt/piWFumNZlllIIcqn3slPex099h2iM70Z2E3Gj5+lppuxHH2BlqFVirUTKyjhYHk8TD9NEXg+2uEPLGFF7/IIzbFneDRCaRvexomVXKiLgHpfZEMSSEPG4K5ssTXYhRuv4j//8AvwbV+ffPJy39StFj9iqMIPBmD4cKKlSa0KaLkg+akA2QZONWA+aqijfJWoI2XuTVjHGou7EbGccqoHImeVtQ8geYV5rQTnN5emB0+VMo7EuG+u2U3IBuhNBiB42M7r76sMbmlW0ovpNoBVS+A760pKUA3HbaQK8FBgSupl+QykJWtHcQJKOH18/w9oTnPd9Z7lcMa2Ra+wlzOh6iZcTOQSGacb6AW0t1jukaChnyEdgu74RW2YYh15yjRp/sqxLIMmEufdNRhtKCtQYIGdSil3qpwdiaqSlizRKzHe6W3dQDMYwnQ3KgvEefzEMwwhV4MNB/Hzj+7++sqxgBkUoO0/qfY/XsJ6a+j6ytR6jl8JOSQmpNMoUlLKUEMnbSj1upOuVr999z/a6kpJGKsP1iFzDwSGfEU7y/psLw0cQREb9EWUNSiqqdFQJ1Ra8MrSWOJYbJWzsb195/v4HXr4klqL5/u2vYBq0gqqC331aSFlj7MR5thgNgkKrhVxFfxFo0Yn8socwA10fLRTjNHK6nFnXK+tzoJSM1hHnDP+f9v7s17Isz+/DPmvee5/h3hgyK6uqB6pJykTTzQGgYRs2DYgg7AcbhkBDf6glaIA5mIZk2aYNPlBqts1Gmz1UZVVlZsS99wx7WKMffuucG1lqZonZBsSHOoVAREVGxJnWb/2m7/DwILzS08vK5fJMTBuH0XKYRP2jGUWqkW1FFAiNoSJmu60Ucm6gM+Fe7layFrC8ctJWQZcLuvFFlUar7xjV/qrgdKNBucZWZoJ/wA2GluUG0C3TSmNZEtu2iWW6qnivxaGqKkHCNEHNFKUxg2F0Dm8spol3xnpZSfmKsRbjHNlqJm94+4N3hP3A9XLiepqJyyaHhoZTBWMUkzfshwFnLc0bslVUJT4cNlhRLA9BmBlFeIwxbyyqoLYmrBJrsbsDyjoYAnMzlLWxPK/s9yP744RB4wDQMnVMGdsE71tL7v4ginDYobyh1CQtBgVnhPVQa0Jrgx0tx88PwqbXhsO7PS54Gapdz+hSoFTKtkKGLS8sc2Y+r+TU8M4zPr5hGifSfCUEgwkjvnnMNPLm3Q/x1ou6gVJc5sLXX1+4nM/88R9u/Di/ZZyMqCMaT21OJpAI3a+WSK1i1CMSKpF1XlkvL2yXJ/JlRqFwdqA1xdNp47wUIJOpHKYTZkucLye2ZZZMowRr7V2gRLA+oJsiLTKIUyUy6MSbUXGaDG0xvKyNuErJ6Br82b/+mhHHu8OB8OMjuCIDyaq6wbPDaI3TDU1h2c6U5UJNMzlegcLxYSLlylbPtGtk7y27/Y4vvnjP119/4Kuvn1iWFVqjHhsOJ7ttCyjNuizE+YRRDm1BqSou3z6grUzWm8xHERa3DMG4iXl98mit8p0N568Kzh/8+HOWvGCCLP1KLV0OvxCvK6enK5fTQk4FRcWaJqWCat25STRn8Q4/DZhgGUaPaQ2VM3WLxDSD6dC/YPHNYJTBjIZYEltKQnbNBd0KVlWmoLBO87D3TLtJ2Ba6yagfjbZKmnfExtwaTfABNY6ka5RVRUroFpgOe0zLsixvlZorGsu6VHJcMGGibGIP+PD4KA5ccek9v9zsKWdi2NiZI6pWVKmdhJvZ7UZqceRWSK2gDNid461/xBjH7jhhjSJuK2UVo+FaCiVWWirM54XLS+RyWlHK4QOs+Ynjw07wnkbhxgHjoDkvzmlNgW7sjoHf/p3PGPeOl+cZ0yZs1SxPHwi6kq9X3HBAaS/eMMgqQFYm0LSlFM18EQPb9boyn6/UqjDThJsV8flCzGJIta2V7Ro5p8T6vLElePP5j+G4keJzV5HPWDeRE+KbUjPBaz5790D97YVpvPCTbwpfPhdOS6HGhsegauH09dd8/NmXPA4b2RUMjeAGzDCiaqa1StWNUjfS9YWyda3ffKEUcSh49/mO5isPq3j8PBzf4r1l/bOZ56cTNWusdazXwkWvWKuZJhkIRi0+OrVmjBPwidYK05xsA0oVMEt7DcBaBJxy43C+BqdUuN8bhOB3IzXBuB9x3lG2Ql4y8/PCV19+4Juvz2wbUmYp0KqgWiIYhdMKYyCMjuMQ2E0BHLLAVQ2U0MYKBRuc6JVqMM4SphEdNHOcmeMiyKNJFNsoBTM4hsHz7v0Dfj9ivMOpRKvdJSo4UJV409fVYJ3B7AcGXzHV4csqzAYS1A1dLbomVKsi3FwNa26cTpGHQ2AJkemNY3/YcX4RBYcSK0orvDWkbiFIRkSqS5R+zhuslqV9TRsoLSrqg5YJsBeEUl43yrZS9Q05YqnNUJImbo0UReNGG0WZN9Iu4AZLNJrgPWEUO3NvHHFbyNsVSEz7xg/MyJu3A6bsqfVMvly5lg2lPmL9hLGBZd0oqqKdAtPxnxhS0rycIs8vC8YYarGkVFjShjKJEOSzaqUxONllKusFxpjl+xgHw3l7YV2SDHRUo2WxyEPB7njA6oKxhTdvR95/THzxTeSbj1cul0grCprl8a1j9I2WN9YUxfXOaHR1EjC6YY1iXk/UdII8Cw0uLmzbjAojYRr5zO+oWrNFEfh+eXpCafn70LBad3HtSDp4aGC0EVbW4KWlGizOgGqVZjXNVLY490GV7I0FoiduZLfA/DQYvyswf2VwxtJwYSQME1CIW2R+mXn+5szPP1zZsmWtGtUc1ELJCaNgNDA6hbcNO2nRiLVSHmilIItuT0xRUChKGmllYDxMUh4aQ6YRphFjPW4cSNcVDIzHHVbDw+FIOApLX+WGLRW8ongLBmzuZjm69cGCw+8O6BIwxWMRMeOSFtGvrelOTUIbamksa2GeE9cQ8XNkOh5R1pNTpjQlQwJrOK+Jy/nCQCMuF7RpqMnRukxkcA7TSkc4GShVhLLTRl1lXdH6FM86L5q8zZGTQZ03moGtJFox+NJY15U2TlTrup5wwSmFcRs1XWhlwXQLRK0KQzA4vZDWq2SZ68L1ciXGb7BWgNlZFfByuSmlyalyORe++mrlw8fI7viOH/zoBwwBLqevMCbxeDAk61F4jKpYrzGjZe/BB2H/tCaqjDUnIdXXq6glknHeEVxAG3Hr3r3bc/is8IMfRZ6fT7ycr2yboirLw8MDP/zxG/a7ibTNQKZ6Bc7gXcDqRopXqQCMwjiL3TRUTVwyRlfcoPDBM+xGrjEyzxE3KHZ7z37nSBqclcmrMYqa5dwTHLVWjDPYwTCMBmcUFmjasOrGEhfxe2lFpr3adK1acWz784Lxe/M5U8o4b8nrQk4rcRYvkXWpJCays6xNVFFyLqRYGazpLBTBu9ZqqK2SW2YwgpuNGZJSFGNQTipv7xx6GJgeduhpIlcwVbEfd1ilifOVmlO3lfPUUvBOmBu1ZmqtIhAssEic0jgjkztF7dZxfVimFUoLAil3+JW1spMN48B0nDh9jMSUKVsRTuWbHWGcmJcFTSNHWS9ot8dgWdcVU2e0gbptNNtotmGnSQAbzmL61LY1kQ5Zrhe2y4xpmv0g4tG1RLG084acGm5yjA+BpBIpyZywGSWq9WZg3B/5+PXP2E4zg/U4q0nbjDGSAW4DtFIiyixYJeAMZRyqbMSXldgK49ExPQyo0aCDRSnPRuacZ8q80q4RHRTeHDBD5fgQyH7lcJioq5SXOS1oXTC+Mh4Uw14RtwslvcE0GZYIVFIQNsbItB2t0MMOrzW1rCibcENmPI68WVdyaSgz4Mc9u+MRowxFZUoSc+a0XtgZhQ8jLWkwDopF6ULTmZYbJFhO4qGjncLogg6GcTegVebh/YTSEK9VrD9SwelGU424JrJakE1Nw9Ad0LUS1QutMLXirKwCa6uUKtRAPlE9+DQYJWj/Aj2npqFbZb2eRG5/U2xLYl0alcCSCuctsZVEiYmWEjkbilZQCnYUBEptlVyTaJUqjQ4eZwy+CbWsFaEFKWewXm48imIYLSVXdC04b2iuYp2oqavgZEdZK1prnB4l22kJAIMGJQptunuLtiLZXSGlR0MJITsXvNHYYcBg+c3fMZQIH778KLzUHIXWts54F6hppaUVSqLF0r8cwxgmnKlsNVL74VExo00kxSi0oyZKC3FZSctGKzBMATcFcgKlK6lktvlMKpoQ9uzfHzCjZZkTaRMbwYalFYsuBtsUOS5c55VpGKhW2CCD10wukOeN03WmNhj2B8K0RylP2s6o51VkPKoQD9whYAaPNgHnG/NVMw1X2FeczSzzidFbHh4c5t0bBj/SErRciasnbmd80Bw/2zG+eYMbgrjNVfpZaNi++tBKcLtK6a687kibpdaEcQ037glxFRU8bcEGlGvkuFLLKvTANZJKJS1n6uGINZqUNkrO1NJYU6Gkim2emgPnj5mqImmJhHcTbgr4oDg8DvhgyUshx0paBO2WUiZdIm3JKO9QDprKGGWx2lCsiIQZrRm8DKWg74g/iaUbAutTaZKbwdP3Ck7vpW9MsaCaIi6J5RJJW6OkxjIvUk83Tc4VVSAW6cNsyxz3HuUrlSSAai1lj7aeyViUMizXK9u60FQBXck1onKEaqhb4fJ8JS8zNUe0LgyDIUyesBuFkKyd2ANYS22KmJssexVCe2rSpEOhkWktUqv4d5qmGNxAUYX5fOX44NjtBswPTWc6ZK7nE8YVisqcXr5hF94IRC9vvY9MGFc5hBFrLHoUh+9tnakxkrdCLTMxZ5Z1BaTX1GhGf2T3uGMIjVxmqBVrPWkrXK4XUmro/SjQN8wd55mWyroupCQi2E4bUJYUN+bLyu7NjnnbMKWyN47gPa2dmLeCmwzj9BbrRtKq8ceZmi3DcSTsDphBiwGVlh3lm7eZeBmZAqzlyrz8GbE5Pv/BA29/8J5hnLDaE9eVuF5Zl8A4Oaa3bxkOD7jpQDOeRmbwHhDV/lJVz0E37xwtiBvlMN5ilKxDmmqyqqCAWiFH0nVhfTqznma2LQGFOHjK5YIfAlVVIXM3SEmjGNBKc70Uni4zpW1cz/BGFR6UpvVEZHTDTpZsM1Zp1tkwz5llSxQq40GGU80oyA2y4J0VWtBmPUv2cHwNTESp9haYn/74rsd3BmeuGyopQd4vmW1JtAxWW5GPUA1TRCGbXDqtCGF2OIsJGu2RftOI5J3SCqUFpdNapWlF1QYQY9UaI7XOxLXx4Rcnnj6c2C5RVPxsZb9z7B4HVAF9lLJCW4tWMmVDN1IRVJDWltYUpUYBHDfpzVqubNdEXguLykxDoa4NXRR2FMb7w5tALW94/igsE+M1Ja+sy5XgBDNZayWtK954vBnY1g1/3GH8KLKgRQK0xJVtE3Ne5waaUVzXJAr2R0s9gFIbUFHG04w4ZS2XK+vpIzXCugg2t2TLZV5JsTBOK3VbeXhwaAXbWvjFzz7y42GiaU8ujaK12A1YS82wzgsfv/lAGPZYr3n/o7fEFNk/PBIOnqKj8GFbE1HrnePxBw9sD56qNMV41tTw44jbHVDeMU57JnUgbjtyOXZDpwMmDDQjDBijFMZoWhPKlbHCPBF4rkx7a+nDxVt5uKUuIQmtZlqSFuT81QunL585fzOzrRVjG2/fPZLGhB0Ch3cPqDBgvWW/U5y/vPD04crpnCnasyWpXnbnSB4iWYlRbykZraQdU1qI0jFVlqWAhnFHX6tpTAWVigh3KcAIuF2GQO1bv/6uENTfQUv5bq+UdWPTFUMjxkLOAgJ2zjG4zN4btnVBZek1nXO0XNFUlGkY6zBGLMunMWBoUAqlLNSYqE1TUwfPa4NWorS9Xleevr7w1U+fef5wZV01g7U4VagPDYrCKKF4+alSisEpWakoY1AFUodLqa5lVLLs7FrS1KLJqyIvmm2NlAEG45n1GZc1bvBYC+NkKHkkLnTbNiMIl8Hhpj3MC9t1hXJi3DXUOKCURWmPC40UK9saBYSfGxZPWRSny5Wnp5VaDbv9wrv3jsOxMkxGdqetQTGUubGcZuICMVZiVhRgq0YmieuGrQ1bB7SuXK+ReSkc31fe//hzVI20EtmSAN2drShVKduVtSb2D3um3cTePjIc35LKQslQW6ZWOaxFN8KbA14dRDmwQcZhx0f8MNJUE86oUtC9T7XRAtF0RkyIWsIo8VmhKfFsVZKvZASgoCmBwfVzAJWsuqO50WLLEFfWy8z16cTp6xMvX61sq5gUtTRjvUGHhdYCD1+8R/sgWfK6siybeM16w/HwgNYbTjnymkgUUs2C/NHiJ5tykguY2kXNhd1jmsJiIMmfUU10mJoqQsyur5NZmlzgSgid8l61/v9P5lxSYwwercQs1TiLHyrbsmFaZDCF46DRa8ENIyEMnC5nUc7Wmpw0tIAzozjA5dzBwkmoYIjWZ6ughgEbBhSG9bJweVq4Pm9sl8o5NlZV2RnJ1tZU/CDTwJoaxorFHcFImWttl+FADhSOZqAaRTWWeblQ4kxeCiUKxG4OlWnY4RkIOOYayWmmpMQ6Z+KaGI8Dw+FIMANuH9DGk5N8KTHO0henIgfUNrK+0rp/S60a1Sznl8gvfn5lnrWANJZE2xJtNah3BhD7gnXOrNfGfFbEVeGHR57OL2y10KxjmxUlVjwbk1U4p8krXM6Nl5fK+NawG/YolyjqQtOe3ZsjthPf3eiYHvc0F8B4olbkalBuwNSMoaF1ARXxxuPGiZY3lsszznncXoSnpXOPtNr6IbaU1tjmF0onP5M92nqK0ii0UNAQRJnqgYiqMsRSAPKZmlY7aFwW+UYbUWTPgpRKW2XbNAVFUzKVdoMiDDuG/QPeT6zrjHOaECxKOdZasc4xTbJf3lZR1m9i3dIZI5pWtTjFDZoRg9FSapco++qmC0YLSkhZi/GSLFr/303xAF67SmPk8r2Vt9+VNX9lcP7rP/2az98f+eFnbxh3hpouDE0TvEhztJZEGq8rypWWCE7h3YCtjWUuXM+Z46HgnJQfNSW2eaamLHo2qYlxjZnAGGLJxEU+eJro8yS9kXKSaR5K0DbKYVRGl0SrivWiME6hhj0myO2bkxCuW5NyxGpP1Q2rN2iVnDb208R+NOwmCL5iWub88QNLzFyeZ66nyOnjxvWcMZeIHQ+MD0ect1gUflxI5UopGzoF8royjHusMjjnid4TU5QSaS68PK9czo1cAutcWVWkbg1dLaDZAc01Yi7MW+U8N+ZZU68zT2tjq5VCQiclNoEJtrlgJkuJmmWu/NEffcmqLJ99duAhwPWa0Npy3RrOZYw3EAyb0Sg70owD5IK1WkHJAmmsmbieKYB2FkEBZVqboTpRLGgNZR2tK0NoJeT5vDyjq+8lfqUhbI7WNGRpC7AeZa3wJZvQ4FqNWC3sj5oTqmZAcLTOaLy3wl4aDMoqqlaUplhihroxKktrRZTb1yCgB28wThOMxZuAHwzDgIAVShYOJlqgdyXjjEMrj/OVsfvktKrQSlYruVWMqaIGWRqqyUDIGofuNLxbz9l6dN4wv/Dt7Pm9g/PDNTHsM4+5sHdasIvIwKAURcwKZw3DMPJy3thSJlhHMJ6SE8s28/TxxMNRMwYrUMkENVbimoid42hcQfkJ0FznhbRGrPMC0RoVpIw2BR1gOBgObzyHNwPTg6gIUBt5u7KeDd4YzOjBgM6RWiI5i+aLcHULLResFlNYP2YObwbGSQSQa04s2yYAg62SzoXl1NhmGVCdjiuffZ5pQd6MdYYE5JiwIRPXC9visF4OvJadPik3rs8z6yViTGDJheuWsC3TEAMm6wo6FOwuI+TnwmXJXBdDapWtKpSzpC2zU1IoDMFifSBlyLESYyGdV5aPMz+9XFkeA9Pg8M6JQ1gFYxzFWJr1uGkvoP8cBV9sjFDS0krbImW7knMhv1woOaFNQbWMSifhbGpLaWJ4ZfSAM468RSgRr4KgpfoQrtHIMaIztGZQ4QAhiC9N2khP3zBfntEgh1xrGQ4FR7MapcGFgD/sCG8S/tzwTUrLd4/vWK9X/M6AV2QSz8/fYAy4fcAfRs4fr+R1ZnRHbHXgtOzZgyc10d5VtaKMRZeKLgg6SUPJolNLBl1lH6D6pLnkio4iuSqUtEojI1q95q6M8JpDf/Ua5VcG5+/89m+wn0ThfM2J2qKoe1NxoYnL1qIwm6JOlhFHbYocux1DScS1iKT9OGAHhQaB0qGk1KkNtCHXjfNlFXu3mnHGoNVCjAmnYBgNx6Ph8x888IMf7nh8P4LN5Ay6NPKWKNtC2WZMHkWOMW990NBIqUIp5HUlLQvKWuzgKaqIIvo4ELzDGE1NimVbMFWjq1gXWqOpJNbLhfl8wg+yJ8zbwpaSaACpQsoLl1kTcujqARXnDElLsDkXSC1QtwTGdOuIyOmSMa5hd43DYDB+oLqNtSZiaUyHPSpVlBEN2aFu7EbFOHrG3cQyL8SUJDsthZ//9GuGnePt448JowOdGKynqcyWEyomglYYb6jNoJRHGQtGY5qhJhl+xcvCcll5+rBgrMPvNGao7N+6bkkg8iaCJZaeVCQjR5wbAbGWT+sqwP4c0Wicm9BNKFgtw3J+4unnP+X88QNOB1Qzoq43OMJhwE+ipO+NZZpG3nzWoGjCeOV6zbQa8c7iXK+gdOF8emb0AVUrb98+4rXl49dPLNcXvH3ADo5gLW6aSK3KEKwrXGyL+JkarVDOgOt9YtG0pKg1k2qlFStqH6WimqxVaAi+XEk/bazGGClhv50x/wKZM+TCg51QZRUmmjUcDiPzvKC2hgsyjfLBYL34Zq5bYqsJZRODUbx9mJj8QF4yGeGFVgzGT4zHgPVerOJ143I64WiYqmhp5fNHx2Aa86Z5OE48Hq2w3Q8e5cBPI3rVlHUjxUiMkaIURSuaE/Gmkho5FmoVmYmmLbgBqxzKWYyuRBSpaQYbhOWQCmlNlFhQpeKN6rdxgbZQ8kxKgZI2Sk2oUXiLVRwIOs4yyy1MxQ8D7D1ujLzbv0W9NH7+9DUNTa2VtRZ240Cqli1q9i1gR8P02DA/22hzYVvOeBcIPhAeJiY7ENrK47sDn3/xnp/+6Z+BatQsJsQsGT8OVGWJGLSqGG/Y7Q8s8UqzhtIquSRyzVhlsVZ8PXRtQsRWclF985MX/uxPn6lNs3/r+fy3HtkdDW430YywGFOR4Ucu4IcDuID2gThfhfc7n2lpobWCHgPWDCiVyBVRm9gW1suZeLmIqNoiqxblFft3E28+P+ImD6rJtHynqO8dw+6Bj79YePlqJkeBdipdiXGm5JWPH0+03Dgc3nB8PMqEPW7EGkkRdsMBhRV37pwpRRQQtNKCcS4V7y3OWwnc2jCTxzRPzYlS5ffosjivAWhEkEDL0KvRPil5b+3oXwC+d/3ZC/ocGYLCecWWZoy2onqNAAJkIhUZVUNbz6MKbGuWm6QIgKCWRFEQ10ZplVRh3O/YD2/wu4E1LtQaGaYdeV4pOhKUInjF7s3EmsCHxsMbw+6gwWSWlDF4lHGs25llXckx43wmn67iJK00qYg0RUM4l1o57ODQrRD0hLdKbBJUJddGnlfWeSWnLNQzXfGDwlRoRuEnBbqIsZENOO+pqYkIcV+BTGGUYc+WSGUj14L1AT96aIbHtwfGb2aeXq4yobYe7XeUplk2KDXgTON4CLx5DNiyCOB/1KAi7754x2//+Af84qd/zMPne3ZvRsJHh/qmoA20lElRc7mufHi+8vij3+Lh7Yixhdoiu/2OMHqs98xxIWew0xFjOn5UO2JZySGQjGWdE2kDZS3rWilZaH92GLvm8FU2ecrJ5WQNYTyyrTNxjcTThevTBwyFECwJIJ9xY6P5AYFMR5wTEa+8FrYrrMsKruKdoj1MrCkS6wamkVNCuYppDR80SmVqjeQGSss8RAvMhKIba80Mg+Hhi3fM1yvbtuGnCec9W4zMJ7kUtFKUWjvl0FJVRimNNQKgKGSBkyrd1f2zaAG1SquCqb1JvygUSosawmtQqm/9+nsHZztnSitss2bVGWUVaQQ3ThRdUUY0TEUYqqGt2JMrLCUmtlmEoGiV0hTzklmWSKoNP76hFFHkzk2jlMXZCXQf4uQmTmAl0tKCGYMsr1VD64ALO2qTiyI3DUo4g+t5Qy2A9WyliXErShyTg6Ia3cHwjTEExv2IphDnC3GOpPlCzmI3X5pM8sbdwLg/YKYjZlQMk0Nbj9Ka3ApmMMLxyw2tPGlt4idyuYASxfHdZPDB8+WXX7FGUSScxkEA9NvCh+eZEDTh+JZ1A/TG5OCzx8BExWpFGAxDmDh+FtjSR/ZvPe9/4x25FvzBE/aOcYmoDTKVmCNffTjxV8KBtz/6Tdb4Qt6uBA+tZZY1EWPEWFGYo4peUC2SXZRqhP2Odz/+guYWUs40vzEdRzCKWAq5G/koJX46kAVXbBR5q5QlE7Sj2JG4zOSqGJys4MiG4hN6cGjdGPcTqjauKvPy/MK8rUzOEdPG9Xql6sq8XcQkyRiUDfjJMUwLzmtKNVStaErwydU0shXtWZzDTANjCDSrGesB7xw5JWrMoqSXq8wdWhPerNJoZaFoKArrDN4JaEI1WSla5yhNiNi5t5KSIU0XnjZdYa99a1AEn/76ewTn++NB9kNNo2xj97iDrNHVonBoKtrKfspYI5hN24TjpitOCcukbIW4JPIq7P/cFOu8sVwXjAc/GswQKFHRXAK06P4oBVYxHHc8vNuhRzi+e8QMgwhXmYC1s6gZFMvpqycuL1dqXTk8vCMXWNcNGxypRezU0ENDOUOYHONux3iY0Gmj5MT1emHdVlAN6y1+sGwxMu0tbz7fw96hB4O2De36DnfwxC2xLpF42di/HdDF89VPfkK8Xjk87Di8P6C1jPOHwTKvG+sSMXpgOhzI3nN+PqPdyDi9pZaNHK/oJvzYenA8PB54++6dkBBaZN2uDNbjjw5dLW9/+I60rVT9zLw2UglcssaOI9UMJD3idho/TKi88fL8C3KcMUZjLaiWoSbZ+6WNuFyhZuy04we/c2T/2cbp/ERqV6bHnVCNkwhG6w6N1Lqim/jjrOtGWRZevv6ArzC/iKZUbRF/WvDeEZeG2gcsgdYKdhrYhwEbogiKf3jCeYMZFZdtFtRXBW0t1kxovxM1jlAIO89aMm60MgXWjmVd+PjVmUzj82ni5fmEViJ5E5xn0whEb4sCdNeK1pUK1yVRMjjn8F78XV1uMoADas5UXURZwgmGubZCLbWLdsle9LbzVErwtL/MSvnewHejK1pVgh9EVr4pSsy0oPF26HA7kSQpnSKjNd2ZWGN2Hl0V8bpxfVlY2oJ1ilIBlbmenxj3nv3hAWOhoClWoQ3kknjz7o1gMH3h8GaH3Vn2j0cSmoqjNY1xA2FUhLeBeC6cPmxczxvXl6+wZhCmh7rSnMKmiq/gDkF6sRxZlsbkuulQLUItcxZiJewCuRYwGWUK/nAg7EYxQkUxhJGWFcv8wrZWlmvk6+0JnRWXp5W0JjQb+7dHxjBSsuF4rDw/P7PNF1KNaOsYwohxG/MS+fkvPvDb0yPBH7henila9aHIxLsff0EqlbI8YVsDB5f1jA8jj5+9pcXEMFi++XDl43Nhmt7wW7/7u/jxwBIrj48TZMPz8xPbktB01E4plBjJtUHJtCyWhdSE9hOHzz9neizYF8MWgwSMHaVkqxWjFVuMsiZRgbQlcsqs5zMffvENKhZyzBg3UAs8XZ/RqrE77vBvd4Q6MuwC2hnsNBCM4cE19m+DIKtSRJluq9ClSbzfYcKBhsOYF2wwvP3skccv3jM+TKQtsayR5bKitKLGTCyJ+TqjcqNaB156/mmcSKWQSxYvT2uo1XB+mZnnK9579sXDqFElUpCJbEZIBGZs+GHEua7fpPvwqAtL3yRUJEhfg7U1qLX+m8LvV0hjlpXWKvMm/VxVGW8sy3njsNuBaegwgIHlulCzwlTNuA8E79nWjWXeqKvGFIdrGWUbjcS2PFHLyOA1plSImZoS1sDuODBOcDiI98XKCqPCTXvWWMht634eBwrhLrq13+3YDgs6G9Y503KhpUopDbwm6YIaElaJlULaFtaygmkQV7QDYybQjagXcq0UKjUmlu3Ezr7DhQEzeFKpIh8ZN0wulCg+Ms/XjdNTZNkaXi5xPn59JmuNcg4bNFYVvIK0Ra5PL7RdVx0oEWUecPaBuD13EIX4odjW2E4XAcXHlVIyxjQm59jvH8gFHn70nrCD6iCpGT1ofvjDB9RO4wdNVZXT6QPbdkGTupUElCUSn0+c80rwmsFbaUVyYVteyLGKzZ5p+GEvEibGk8tKw7A1Q7UBpRvL9Rvxs7lWXj6cKbFiGFjjxnKJ1NooEdHunStLfGJYZuq7PXYMjAeP8gaz8/jJw7MizwlS3xVaBU0zzyujGUTxTil0UNjQODyCHRo5V+ze4x6cJIxBYKjKyRAseIvyBa0N/s2R3TiwbUvHXheWpVKtIX+4So/pB3JaqVF2wNorMA1jqnjkmCZaWRaMk8pREFDicKYw9yx6y5p/IT7n6EeuywXlHDFmBhTTuGPOJ9btzPHtI2jFsmxcnhbWS2IZEusu4YKnVbg8v7CcLwzaMQ0OoytNg9MKpRsuiC2gCZpUMsZbHqYDxuwxplJKxKiB4fhIxRLLRmuw5shQN+bliiqZfL6SrhHTHE41ihLrwRKhFflisBmfK86IyY3pk9UcE2xJgMwguq3W0UhY7UR2olTKNkMJXfTaiEhyEr2fFht1adQZ8mpYrwv+waFappWFVgNT8ISHictx5DIu5JTYysbTk+jcPjzsyTnzs5/9gjcHus5SpLWC147T0xPLOmOd5/C4ZysLRgd20xvO88r+ccLYxOdWMzwk5ghbfeLH778gqUKMjdo0ZtjhB09LCzVu6FYwqrDMZ8qGWFyUKhmwKdb1gvYOOwi3tulGbgXtAtaO3YU8Epcr22WmxsT5eeH5mzMlKcbwIHvxrfLx+YX9OOCt53KNYDa2spBV5s3n70mr7ENbhuWycvrmzPK8CB7Xa9EjCrICK5yxNoJSvHn7BjNAU5V1m6kopsMIP7TSEzuPBdSSoBXmeeMwPYgy4fGBqhXHhz0lzaS84Y4atztgwzPr6UIsG7olTFd/x2pcsAze43cTDIGo1L3PdF4c6FQfMBmt72AEOWTwnaK1vyo4rQvYqfK8ZVZVGa14HE7DiBtFyX1ZMx++PvP0zYVtzgSfmafYtWQ127qyLYlspaaf3gwMu1HkBrUjjAE3BpHhNAprtSyOvWZdz6xrRpkJE44icFVbZ9AbchE9n9OHJ5anE/mSUJthOyXWJRGz0H9aa9jRYqsS0ehZZC7s4Gka5stVPCJzZdzvCfuBUjVUK19Ga5iqUTWKpEnaUF70YQuNmBsGiypiL0DeGLzh8THw2ecD7gDTzmGcwjTND754w3yNnLdnljnT0Djr0fqGbMqoOtCio2RLzZkXXqg1oQy8/fE79ruJOhfOpwsmXBimI003zHggqMbjrnDAoAMs8zN+eqQpjXEBpRTWNOIpUcsVZwTk7r3Gas16XSgxUUsVbxldyWukUfF7mS43bVDG951mpSnHcn7m9PWLeJOmLCqEVfP08Ylpesv5vKCa57B/T9yubOuZ42PAB4dWRg6686SYeX66cvpwZjslatSUsmEz+KYotdGMZY2F0i4MIXB8OGB8I+aFUiqtQgiOfLCkWNgKEBvbAstzFoSbTzx+ccSOB5oVgkZWiUwhN7B7z/GzA6Vt1GUGqlwQQWOHjh1Wmi1XASfYQXpgH/DO3+F6pvvMSlS2HqQyxf2umdB369buJtZt5cvTC7kUmlXs3w7sXaDWxPll5vnDzMevr6zXgqoavzPooKhb5Xo9i727sRQlUvjTfsJPoKzGDBPaeVowKKMJfsJ1gbBSZWepbcD6B5QeO9KkyoqjiYxmmkVPNS2JvCS288p2zZQEuYrkSPBSZtdaqXMknhrWDaAVWy0sl4X5stLWQmkWnFi1pQ22q4hQ+ViI20rNEeuD4IeNw48G7EmYDCaRamXca97s9zy8V0yPGnvco6cdzTicDRzej7ydIz9/unKeNzQGbwOjG0lLQhdIY2B3mLguF9ZZrCCmncd6RUob1zWDzizxxA8GDaowThPbdYedLKrGDirYC1ungUKqBj8c0Hkj1oqqhVYTOW5i7JPE+VusH+A6zyhn8EMQgWlTcSEQdo8Y7Ukpymoqb1jzkcvLlaAVu+OB958F1rnQ6sx8vbCcr2gz8s03LwKdPEzsDw94V2hlY5lnUmqktZLnynrOrJeMqjJdN8pijRHIn3M4KwLi2mq2FFFd1a6kRIziCFZRGD9imuf55Ynzy0ZZG1YpTqeN6xzRU+fZxkRKslJDg3GK8eBJMXAqF1HEHyx2dFLtKVF7vG6V1DI//NEDx+NbfJgwzspISGsB9tNL2V+Ksdq+Z8/5NF/56vnEvMkBmpeVLRWG5qlL4+mbK6ePG9uloBDFaz8YfFBQq8g4KLE98D5gjOyFpjCig2M47LHDTqQqBi/u1tsMrYjGbHVQNTlqTtvCsqx9f5WhNLbrhevzB5zSeGPJLYGqDLtAihmVIJGxTibKlErbCuVSqJOhBUtTtXM/Gykm8stF9pk+ENfKulWMgoymYmnK4vwAJlCbF/fr3YG2u+D3BqMK427H7nGHHzfMpNG7EXc4sDs8opJiNVeO73e8/XzP+ZRYloJTMHpHcJptO3F6eSKtUOOFEjO6rHhrcc5RVWP/uOO8yOL862/+DFRg//CW6ncYP6DUrQcawfhOQM94lajzibpeYb6QzhdKKzjv2NaNliFGQXlZbajV4Vugro31slDViNmDNoOQtmMUxHheCfsju+MDrmUOb99SS8XYVcTWPqwcDzty1mwpY4whZ8jV4JWmlJXr+co4KS7PK5ePG8tppSbxCg1DwNiGuLYphimgw0DTRsTJWsNbT9w20lq5nlecU6QiVC7bRDFw24q0L6qhY+XnP/kFKVfsKJVN2lZQBZxIkjgLD48TzjaR1buxZKxUbvNaeF4au88fmI6f48c91olDm9MO54yA/Kl8yvW80cm+Tcn+twjONUZqzBzMiGuasUI8L6xOUbaF7bqhKgyDQ1kIOzg+Gh6PIy1VQlCkrWLtgHEeYwvXdcUtiv3k+8JYY51jS5EcV9Z5Ia0rLTfSmolboahETuIERRWvTF2gporaEk1pnBblBKXAWM+2KtymWHSlkYT9giyMMYZ0ccRg8XuLnwb27xSLudIyrEu3KXxeqHNi2jmyFdHoqgZytXg/inYSjWE6kvdnprIwft6kbPcO4wPeyUS5tMrz80ds0wxWMUyZ3/qtI7rAT3/yRNpOUDzDNOBUY3d0KJW4bqIrNM8Rq1eU0Yx+oCoj+F1dKHklxo03b98xPLxhiYmmtUwSQ5AWpxVUWYmXbzh9+cfYFMnrxvk8o7Rjt3tgmQvPz89d/6aBKtRkUHUWfaWgMcNAyxnnZMAVfOA6z7SWMfuR9z/+AuJMMQoXBvZGHMfmayR4GYksMXOdN96+O/Dx6YVUNdOu4ZRB1YpF07r6oBhzJYFeaSWW7yqDbULTqgVtFaoosaNojrpG1lMhm0jMjXnN6BYw2jOOO5b8IiLcKK4fnljnC4fHI9M4kJeVXCLDg8cdR1QQX5dp957WFMt1ZdsSMVbOp5WXS8I/fsHx899h2L9DaxFaM8ZijZUg5NOM2e7DIKHTfU/1veO04wfHynzO5OvGqDV6q+gsdgjDGMRhzGhMaAxHw/7gGZyCbBDbNkXwgVwVYfDsHixhFPb5jSuH0sRSuJwvzKcX5ucry2nh9DSzLrJALkUwuU5nKBtOKbx1GC3CXeKMJQrzqRZ0EBcvdKVEsV9TWlT5GtBSo6QKTROGkSns8EbQPrU15nkVxBANp6FZiwojuJHcHCRkKNSqoGXGPa0sjEPDj45mrJDAtczpUJVSMtu6ohRMQ0U9GvRvHjEqcz5ldiOMQaaB02PAhz3joHj+xRNxLsRcacoyjAdqA6MDylWGcCAHQ8uZwVsKiqJstzM0pLKhVaXVyPnjL7h+/SUubeStcrkmjD9g1I64NuY5o21jfxCV/6qhxSagBKOlBM5irhusES2ilFi3GR0GxjcPpIuoUTgfUK7QquFtVNR84vS8Mk0GHywPD0dymUlbpA3Sb6/LQk4wDF6Mh+JKCAY/aPAd6BIcxhm0M6gKzgVKapQMMSbimklzYY5nlLaiNdREy3YKATd4SobRW3IrbMvCtRTUtCOvog2VVourFX30qMmhrCFVkT3Z1sJ1jpwuicLE8e2PeXz/G0z7R1wYsD4I0+YWkk0y592XE1mh3H58r+AcRhiDYj1lUJpktAhzNcX08AY37Gi1UlvBBsO4d7igu2lsIhwM496gm2FdMk43HJqchGZjrWJbrqJCpwqxNNKiePn5ytPXV15Oicua2T0EMXeNZwarCaExjo02FpzRlFoJQYNRaKsxWehLVomXyLaulCQMkVorShmMcgQ14FQQ7xRrmN4bUIblsuBoDA+JNBrcYaSNI273gA4Txo+k1DDKoPseaxwdJTqqaWAcYZrEiRongOoyo/TGNAYOw475fKK1hXEq/OZv7djWgmqWGMXAdtjBu/d7vCuk0wLXmRozpWbW9QPN+Ve5DydwsfPLlSX9CcPhAbWbcLsdWivhvLbG5AeejUe5gZylr8+b+IXmUrF2Yj8WtEuMO001mc1kmlGo6lHBgbU461FNs84XlBUXgCEcKFS2lmgtEs8nyI39/oHgHxjNyhtlKeWnxJeG0wqdV4IuAsC/7QWVY6lXUiu4o2VyR6yGVGYqDRcC+8e3Ar1zgZIra0z4MVATXM8vpFwwfWqbi5hV2cHyME3M55kUI0PwuN3IYDSczxitWa+zZG6rSdfI7K5i7lQ16xpZS2VZC9dL4nzJaPfA5z/6S7z5/EdY51G2s4sQ3eamVOeWvwah+qT//PTnf+vg/Pk3H7lcVqo16L0Fo9hofDhdcZdFnoyGH600486hnaLESFUC7zNN0YrIg1zXlaxmBjMQlKemyDJvzE1TlKYlESVerhvrUli3SkqNpw8nKI1gDX70KAzGislpHRwYRQ1G+qxS0U7WJ0pZQnAoq0X1rTZKkqxtchWZia2graA8jA/EWKhdFMpZx7Tf4yaPso7zZSWMj2gzyhTXOBpZzHuHA61m8nahNgsElB5QWlzXWss0NlJtLJto42hTcV4sy1cHyxxRKWGtxxmgVawB5wTbnGqmtESlkOKKRpG2wnqKDH5HjJUlP/PZj3+D0BraGrJRImqmFNWPHN5+jqdw+sWXNLtxQDNOe5Ytsi5RiAwusNsPNFcZpsZyTgR3wOi+t1NQ44KxipwT4DBKyXpFN6w1DIPFaUAVDm/f4oYJQ0brwnFV/PSnP6HqSvAaO2iZlLaCDo7xOBAmGPyAUZptXUhXjbYWvz+iXEDbgDaOkiNpTSQqKVbWy5WyJkyDyXsyhtJPekobMW5QG2mTKmY3jYxhIK4rOa6CBLIOMMznjaYVQxnJRnNaI0tWLBv46ZHPfvBbPH72I9y4w3svzJPbiuQO1RNAgjHmXsregvIvtOfcouJwfASaYGa14TIvpJhJl4wzGmsVymkxJs2FopEvrFSc1rTSiGmTjACoDDoWylbJcePyfCZuGeW8NPRXoZnFmNHa8O7dW3KuXLuFvbgFN5x3uMnTDhMhOIwqlLRStg068mJLEYMWBzKlyevW+5OGSpVy3WhktJ5wztKUIs+izXt+vmCdY9i77u6s0GrA2hGFRekm5jRVdG+aDbjhIDKdNHJV6CpfilKO2hYqFqMhdc3a2gT+5rymNUPcIt4ZpnHCG9uVBcUWw+8MNQqIo5SNtvWeu2hKjPz8qy9RzaF84M3DW8bjgbwsosRXMxjHmjWH9z9kNzjiOrO0J0Yth6CcZ66nBTcO7NxeLC2cw44B5wzzx42PX33NscJ6uWAGoflhPE0H/DjSbKOSRffVCwmy1oU1vjAc33No71BBoV/OfG7fUWOipEhRhdSgaisC4daimkErQ06FGDXZDviww+3fooNjK1UmwJeZbV4xysr6bN5QuWBbIwSP2Y+YYeCyJD4+XdAKnPPU0ri8XKhJkFKtFVl5tNY1hEXeRl0jTVva4FnXwiUq7O4dj5/9iOnxM8wwiRvcOIprwSfAdhn6vJattzL2057zewfnYCeoGXQhBJl6jT6gsyLVRlo2thK5Phce3+8wdYfaGSqQt0KurWuAIg7HaFo21BncSyOXzOWUSTGhfaN42JabyW4R0qsRDKfRmtYiKW/s/IgbAm4cYJzwwdLKSi1atFpqQxkjtvZVMI3Ge1pnEFil0Q3KlmmmkuYVbTVVweX5wunjC+sa2R1liUyDlCJ522ipW5knkcQU5oFBoaWkdoZaN3KN6NqzB4rWFDUrrPUMXjNvGzSH0k4U6BBHKizkrYoA8tGSvOHNZ48MnUEyPA7dAr0Rtw2Pp6yFtiYMMF+v/Os/+Jf8loEH94U4cVNJOaM9zLEw+MDw+EhKC3iNKhU3Fh7twHg8Mj4OKN/AANZQrePl/ETuIt0lLtRoKTky7h/ITfSSWr2tujLBe/IaifGKcyNLnGnBoXcToxaCc15X1uvMuiyUVinVY+0Ob7rRbFVoqxj8gfBgMNbRrCWWIjIll431vBHnVQD7VYn1o1JUJTMkO2jsaAhaMcbAOm/sxh1UWLOUuFqJaIDWCqMNkGhVZhghDLiwY6mN61wodsfDw2cMD2/RwyhlfvCEIeCdvUuP3Pw37zB3pb7VY5ZS7n/uewXn84cPGAPjYeTl+UXAzLL2Q0VB1rSSUboxP81i8DNqmi53hQKtLUobSlNsqZBLxDrL6SJiSHR5C7ZC3GZhA1iLdYZ5jaT8hNIGF0StzQwGN3qatzSjRTgKJbovzQC267pq7BDQaIzWmKqwCmHJFJGbKCmhV7CDpSaRn6ipoJqIXAvTIlK1+C0ulxPn00emksEYjAtdGe5G0pUFc0pSHjrjUBSM0lilUcqjqyNvlfkpUqvFBoECxpxpqotO9758XSPGO/aHA4eHA5dlhlCFbbEJFM4Fy3ScIDXKVonnmVY20nrFdn+b28i+JpEX1c7ixwm/2xGvlaYS05uRguPw9hE3WlKNpNJAGay1uMGIK5vJ0CI1RewQMIiyYqORS8FaR9kcJSdKKaR1RrkVZcWXy04PVBZcs6L0X8X6IacCyqEJGG1l4GU7MgCR/jTaoFRmW6/EOfLy1RPz8xWKZHndQTLNaIpGpEy8RjklCh3J42cnDtZzpChRnHDG4LW9KwOKVaKVAZhWpFI5L4nznNl/NjEeDrhxxE0j+8cjD4+PTPtByB/QlR5FU+m+PPlEVPq/b/b8bsqYLuA8sUqkV6UJkwWa9HSbqJDVVEi1US4Z5q4RW5tAmbwWeX9nKKZSlaJoQ9GGME4YJz4Xra4YYIsR5Qy7455pbzlfFiqVYXRM4yPDzjHsB+xgaNZLlq6NUhpViUCVTEabSGcY6YdzKzRnMWqkbpmcMjknhiBIplIqMScUMO1GSmtY76R16L4c5Jm4vGB0w9iBEmN3GisC/C+VvFbSWikmYUwRSpTOpOuZ0zcn5g8rrIUSZw5vd7jdQGuJZiLYCrmKFo+2gjYZFNUomrd4O1CtYF6XEqlK/GVSKbjRscUL+4Nn99mRhzc7oKKaEY0bpdFlhVJYU6K2KtPUWvHOoI2lad/lLBs1bpStAI6iGj401nWmKYPzk7BsnNhSOD+SqriINyzWT7KSiIVtiWzlxN6/F42icaKkj2RXaGlFWXBe4b1DG/FQLS2jjSzx0XI5aOVorRK3jfPzidNXXzN/fabOCW0tqmlQRmCGTkNQuNFjJw/W4LTnwXi8G3n55kU4qB2go2m40WOAWsTEWRmwwaLDwIblus40PRDGPd5Z/OA4vjnw+PaBaTeKzhCSkG7CXjd1/0+D8Rakt9/73sGZBk9xgYYRlTXbGI87xjGIhV1RoniemqD+t435fIZSOkPF0lTAhh1mcNQyo1vCOYcLAe08bhB2uyFgFYy7kY+/+EDWjVodB22xg2G3GwmDwbr+oXmDCR6cEvUClOgEOYeyCtUzYGtQUaIvaoUlgNKomlHJYbxDG8O2RZYouF0f3F14SkrIijEaoxqqJrGvr4qmsgR1yjjn0a1RiqE1T8mNHEXaMqvENz/7Bd/85COXbxbYGqM3hGkApWVa7K3IVuaM0fRh1A41VNZtIbeMdqLLm9NGikXkPmvFa2kc3WCxRuMG21URMrlXL84b0vJCrhtGNTHhGUemaUcIAWUsBcuaM1taaCrIgKA2Wt0Ig2I/veHxcUKHgPJBMkNXcde68x5rprVMKY2clVQzRWGawTRN3hK577RTWkhpludp4mhuWpJL0XuhHypDyUnc9GohrSvpeiVeZ1rOWGPBiJOA0hYfAsopqhqwwVBU7VBRjSqFYR8osZe1i1zQxiiM9WhdoTvmKS0/rNfErKkY3rx/x5t37xh3A2/fPPD23SN+DN1r5gY0gK7ZcsfOvurYVnG65haw3xV9vyI4/+BUsTZhjEw+tIK/9Zd/k3/vd39XPC5axaCoKaFqZfCOP/mjP+bydAatODwc8ZOY26aSuZw/yJSxg4C99zw8PpByZH7+OTnPHA8TXzjDH/5//oT5urBF8ClQreN5vWIsHB72jGonmkBESkqkTRg01jiM9VhtcFZTKeLZomSrSlXipDU6MIWoFVttGBeYpiCyhkpRmzg9W2uxdkQbsDZAM12b1lIwFCoYLdVAaxQ90YwRjd1UOJ/PbKcTX3/5xMvXM2VBZCeVXCbWOkorfeyupX8xHhsMsWyopZFiFjUFZaCark6YZHhRGglhqhinGHcPFOM5XxeyPjONE9pGtDHU9SM1rxirSUqj3YSZ9iTEcDZV0aAVPn+h1JWyrpSYCAfP/rDHDKMQ111AqSKmTEBOlZKKaAI1iwsDKe9xZsKYCQ3U9UrcMm05Q5ppeQGSfBZFOJKmZbQexFNVO8FaW1lHbeuVcr3AsmFqg8GKm7ny5JjZtlVQan5Ae0tzClGK1zKYUbClxFIKS6kiilZFW1ddN4JtDAaMahjVv6cmmsW5KoKzmMFxeHzg4c0D4xgEpmc6y7rduswiYuafZMtf/iFB+t1qCN8ZnD95iUDsMgwCXA8/+cj/5O/9Dr/xo9+glYxzjuCMMPW946/+nQvz+UJMkRDkdk05sS4L1/OLSAg6EdLyw8A0jmzbyh/9y3/O1z/7Y5LKvP3Be75YCj/76ddcvj7x8pR4mleMhWFynNbCbp9lSFQ2ctxotchktBvpaCNZROuKMdyFpbQSFb5WFKVq1rUQS2R/HDg8PBCCJpfMy+nMmguTd0KLU5XmLIRAsw7nJ2pRUgY5i7GBbVtZtot8uQZi2Vi2xsePM988b2ybpib50ps3LFtmmWeMq907xFJaxnqHD5ZaMpeXhWXdMM4y7i1NV2IUwWMhB6vuZOZBW6JzgoBaFlRV6C1iB0VJmm29UNJKtRbrvVQ2aSXVPgjRlqqkNM0pscyRvIpwlg4D+JFmBlyYQBkgyk5RK7aU2bZNHN0QRQhlJ4wSpb3rulFKJMdEvjyzrmdq3Wg1972zPH9KmZg3/KgJFLTKqJYoJbFcX4iLmEWFcaCi0S6g0EQdWeaZNc7YyWLdILxbLSidFAspNU6XyNcfrjx9OLNtTSayqjFdN46D4bPjgBtES4pWRcgrNdZtYdKVh8cHHh4fJbtr0apVN52g1jrYAG5iZ6120Eu7lbavP8vjewbnpvzr36+NVhv/8v/7Jf/k//bP+Y/+w9/i4eEz2S0ahR0CVYEPE/r4hhSFF1dygpLR04Hdm88IXkS9tOliR0oxtsrfeHjDcv7IN1/+MSpd+e1hT9j/BOyf8q//9InzvGC72kJTlVSuaAWjD7QqC3nlA8O0x4aBpkQLlS5idZ4zNYuqHzVT4toLYUWbI1+fnnmzGH7047cobTitL1wvkZe18PhomfYjuRTSnPBpIQSL9xNGD91H07GtkWUBbw2mgW6Gige3B1fIJkM3F06qcl0jl+dn9g8B6/XdURpVgUwpRcyQiu66sZrSEtqITZ1RYqNnTKB6EeguKCY3wFZoaWF5OTMcPXmSaXfLCRTUrKhqRTsB46ecpRQvSK94TayXDa0aZudBa7YYyUVRi8EoS84bpYEJRbRqcyWRyHHB1i4crg1gRPAMTd2ulOVCvJ66K4Dt024j4l9VFO9VgjJvlEVYO7km5stJSlltcd6J7UaVXbs2ijA60U9WGUPGmEFkVKoiJ83lkvjmm5WffX3lfC1sWQ63N5YtFdKa8WgG5zE6E2PC+4z3jnfvH/jN3/lNPv/x5/hxQGktptXqE0B7/7lWkdqu/f+X8u3A/GVAwvcKTgXSd6EFaNcaS0z8P/9f/5y/8bt/nb/7d/8XKAXrOjOvi0zMtAbrMcqgcgbVs5kpdMwZxnucFZLs7VYx5j3D/sjx7Xuuz18xPXzF7vEtZtwR1Z/y1S++QinF4XhgmkZROKDRbOBwODCOE8c3jzy+/4xxf0QbS41RXtv5zOn5iflyJq0Ly3xmS2JbrtBsW2LdNp6Xb7C7kd1u4LQ0XmbBmGaT2DXPNCjy84XLy1doHG8f36Ns78mVZplnctk47AcWIkYlBqcZpgemR8uSr8znFd0ytTaWmInJkgvUlElZeryYCphEU4rdYcKaQWwYlLxn0+lIqmQ0BcpGK00mnwVUM0zWsX54Yj09EeeB6d2Enwy6u32UBNCgqA7La9QCKVaWy8byciUviWHyaCwKzTovrPNHTLOoAq1octP46cCw32G9JpcKqcqUuzZJsKpBLWzrwnq5kq4zFAGPa+2wHTaZt424rLKn3rIEqTECNimVEjcZEllNrZp1lsvCeiNi05PHDY6mhZHUciZushY5nTJff3Xmpz9/4ZuXSGpGaGHGsxv3lO3KFmfWLXXXM4UM1DLOax6GiePbB+zg5fNSr72k6obHIlVSqU0oa+UerLfA/JbvGN+VNX9lcBrVOvAYipIbSinF08cP/Nf/9f+Vv/7X/xo//OEX1GyJ2yK2f134yiC4ToPqqxUpEUrJxHUR23Lve41uaVpTq0Fbx0OYeHj3A+L1hfDwjvHxh/z0T3/KsmwMwQu8Km3yAdnAbtoJE2R/IBwfGY5v8EG8WUrKrNeZ/eML5+ePnF+e8PPEtB3EJi5lzJCxqVKA81aJLYHbMx4n/DiinWEulZYM21J4+rhRtpmf//QkONYGBai1YIzizeOBtF3wpvHF5+9ww0D1e8xxQKuZEleKLWxGs+g9Vg0iRKwy1lsY9sKsb5lSBTmzlcYlC854cEf8pGnxjNIiSFX7dJyqKLnidwc4VC6nE+fzjN0N+N2BpjVbMcwpo2ugeFnvVCwVS26NNVbOV3AtoPSeWgdRw9GN1i7EdWY7L7x8iFyXjN8d+OxHP+TdD97jR7Hpi+tKKRDXSGmKvK5s85nteqXGih9HBjtK714bcVu7xvEm78U62VUagx8c1gjZoDZoypBTFoexJkZWaI3xGmXlB9pSu2ZZ3ApPH8789Kcf+OZ54ZIUWUlnbV1gyxldCkpDCKJ9qxAqmlKiwDjtR9wYZPikVeeYiJcK5RZm3VulVumhkcntt8tYkfK5gYi+y5HhV2dOJcxQrUXdW/W94h/84R/yX/3f/x/8/f/gP+DhIAz+koTOZTvg+/bsrSlKETMfo+TX6yYiTtJrySvUSol6uPVoJuyw58fjkcNnv8EP/8oTp+cT1/OF0+mF+XrqFt+KWAtqWwW04EeMj2jjwFvMYBm1w/qBMI6EaeB8CizzmbQJ5SrURm1SIrWWWTMoN+GDwziBB2qtKNai6kB4MOgklURBsJ1VGREl0xozBpod8U5Tw4HkHdPjA4/DI62q7u95xbARjh49WmpLqFow2okT1nIiXp9I60LOC6kZitvxePghdmco/pl0/YCxhZYTrUYwDqUC2VjqbsL7gTFntvVEdXtW/YhynqI8UTX8uKf4PcVKv6fMgAuanZnR9hGVVrRRREAZRRgnbHCszx9puWIDqE0y/fPpihoGpjpirKKoQKIx58KybKzXhMqGkh1xyziliR1hZgBTPdoIKR5tRd3QOYpsSNBO9tVS+je0zbgqAzLnLI0mAuWtYZSmNqEB1qqIMTNfVnIG50dx0S6C6dWqktPCzsLgNW/f7pgm0RBSuoHTaG+ZHh+Z9kfQwj0WX5S+v7wLUn+aPRu1SWarn0hmwqsAgtbfbcnw3XtOek2vRAbfuiC1dmvMa+Kf/l/+K6Zh4n/1d/+XjOOepVxIKYtup+keGMI5lwBvjYo4TJdSiVsiBI82n/hIoPq0UIEGO2p21kPY4w8X/PMzahjgybFcz2IjUAulJLZ1xhgncLnaGCYRYTbW4pVGG1E4t8Hinh3z9ULKUn7de4ScyLnIR6PlRqb/XbTDjZ43X+zwxoposNGkKg7ezopUIq2hNXij8cFTVCOMe4bpgLUObzWmT5GN7Y5anSxslIZSUJcnInuKm2m1Mo4HhsM7puNbnC3E+Zk5W+xxQLdMPF8w1mPciA4DZdphrWc/PDLEVcS77Q4TRqwbebAB4weMleW9dg5rB7QytJIhRcgbyyzlnjURo14wppFTRdcBGzceHxw2jJgQOKfEeq3s9hPaj+jBEwaofiWrE1YJECI9X6jWErVlzQXVGp+/fcNkTAdjiDu1cg6BCcgu1naccC0ZvUVquLCtXRWjJk7XE3qp7PeSHOKWmM8zl0ukFsVu2tG8QqXMWjKWhq5gayPYxhgMw+QwHgYnQIRqAxyOTO8+w4dBMrQ2PancsmITg+AmKu+1fbIqUYrWaj/VuveoAvJvyCXyPTOnIB5QQv8Jw4h3nnVdyTnys5/9jH/8T/7P7KaR//n/7H/KsJtYLj1ANVgvAaqlIIbaxA6tSblccyYrhXLiwITSaG71u4xr0BbjRsa96Vb2YihjtOFsDdfLCyVtKCq1JtZ1piqRMqQk1FjxYcQYkfcfjVjZWS2L9+t8JZcMrVuJW4XJMkRoTd6/Ut0CrlWs8WjvUVooYcYKO18rJ0wYa2m19hVMh3MphbX2VcNUA8YL2KCb39gg70tu04p58zmHL35b+lBt8WEgV0QNsBR8OLDTE4c3B6xu5A8fmPYHjN+zpQRa43Y77FvxhzTOkZogb8Rmz8hrt6JqZ6zse28rgZuVwK4WWomk7Ynt9CfktuD3hhYC0ReMGfDDiLIWzlcODw+Mux3ayqCpZojbJmyRuKBpPGwFUPjg2KKoph8fH7DOSutTZbBVUCKHog1WawHcl0ztpHx1uOC2VZzNUuLjn/0xebnKjrZlzi9XtsvCGitNBaadp8WMnwKFxhYreYu4WglGHMqqdjQfsLsq35MdsftHht1RWjZjMVp3PG67T2Nrz6DU/mvoLZtCde0gyT8y3UWJVvN3rTq/23beiHQgStgHapPSolSZJNbc+NM/+RP+T//wHzGOI3/7b/5Nwjixzle2mECD86KEYFTfGSklUoVVxIhTSjSamN7cDuftclAK3XdwOI2eNBqD1QZrLM5anDVczs/ktNGaKNjV9QxUWknU0kSqJAScl/2cUUr0epzHnE/iCbKt5JxoyopSeBHiuzykeWm1UmiAQ6MoRQu5WylQQv4tHX+q+sCg1NrB76pX+freGty+QpRGK9MDs7cSRniBrkv4a61xrTPo0bSaGfaPhCBg64N/RxhGKefXFa00rk8V5SkUppddqr9/pUQfWGnVd4H99QBKafmurEJbI45u6x4V99gWcPs3+DdHUkbqTqN5c2i8ffceYy25D9tqrthacfs3lBzvXpy0htJyvirc5SRrEwCLoD+VvIbbnrJVdKuyNhsTYd8hoFrE1uzhM7brGd0Kp6cP6DowDIVBGcbdA8oFli2inGVLkVIrH7/+RgToDOwOA+x2sJtYzCZO7MMOt39H2B1RfU11a8Naf+3fQv90cjWq+6bcgPC9yVRKkhBojDEdfvo9glO6YkE+bJswS7ZtFT8NLel6XVf+1b/6V/xn//l/jjGG3/vrv4sLnm2bWVcxPPRBi9OzfhVquMnS55yJMdIaGKNxHdMqN44c8lrljYjEve6Hysku04knxeX8RIwruWZUabA2wc8W0RyqtdAYhDlgPX4yKOtwYWCdL1yvZ5brVfRXqaAaulbBelQZj2eVocqHryvoSj9oUHTBVC04YWsoGbASCK1/VrUU6icBKL1IkX9D07+4Du9S+v7F6ttF1Q2DwYIKUiopaQHCYZKgbxBG+Qybkn5fOoSG0hWt1bcuCN2f594/9ee6Cx53dyxtJ+z4llYisV5R/oDVDyikAkkp4q3Hht19OomCqjuzRmtcG7tlgZLpcGtdkFoYSMpo2anDffl/G2q2VnrgVqhOMnZQcmH2i/DzYU9JG5TC2y9m4fEWeR9hGEEpYs5CQM+iwvf09MTTNx8xCvbTwBQcPmiRKdUZvx/Zv/sRw/6RbIWfqTov+CakJ+/335AD+5+RTNlbJC0OZbpXgt8rOJu0Q7K/uaXqUnoKlw+ylMK8rPzLf/kH+C6k9Vf/8r+HtZ6YVrZ1Q3e0zu3L1q85Q5ArrZLSxrpWxnFkGIZ78MrTVglUo7AOBsRG3XrLOA1M+z3PzzueXz6yzBdap2NtaaUJ+5HSEqVlwjAKdNAa/DBhncOHgTBOnP2Zy/nCuq7UmsSluFVaE9U8tCKnimqFUqRHdP0goxVVyw1fm6FWjb3RkJSidJ5fq5VqLdU0CRRT+gGWvlUbJW0uHZKmbjs0KXvFhT310uh2G2vsrURqBatNDzL5lMVWQC4AGewJS6aP/PoN/8l5Up8OKppwbbXHjW+wqhLzMzY8kFpANXA+4IwRlFPJlCz2d9poUFXK9c7uuV0YTTd0V0dXTQjr7VY13DK45n6bt46oejW4vXU/Bq2kQmloVHM0rdm5B6bDA6mZvjuWf1MXWXVYJhSFYbfn7fvPKTl3upd8bqY2lM340WJ3D1JlKbm05bll79/U62d2B7n3D1/1n6U1MtIKqVui6UOl791z3vzuu0OXhnvZWXK6U2CU0pyvM7//B/9vpt2E0Yq/9Jd+E4cnpygBOohlA1pS+230rJXpK4+bmO8MwDCMcojup0zcgrU1OBMIY2B/3BPjA9v2juP1HQ/PH7mcn1nPZxnlJ2FkNJXZykxdK6lmQitCBXIyxdXGY9yAtiPO77lcXliWM6VGCRalCSGgjeJ8vhK3SE4CbmipYY0DNNUYWi1CqzJGlNSN7Ux4hzWVog2miBixNt3GwoDWIuevq1Q9uuiu3KblQHfSbum9lzLiCaKbKO8Zwcu82rnfg02+wNZPtKgy0GGKmoZY13ELjE8CVDCgHdRfLVYPaLvHBel5Syq0UnFG9p03B69aq8A7qzgG3NYIrfU+Td3Kv9rRREi/djup/XXeD317RdhwC4zbMr+fT+ptIHPbEPRpLbf8ImehtEIp3CswQCbDxry+TgCt5MJVWn70C6P220GGiP0P96C+f+i313H7TJWSM6alolGdaih/9HsC328W2bXJGNj0uvSmh1JvzXAV56mX05l/8S/+W5zR/G/+13+f3/yNHwojJGe2LRKUE2QQqu9CuY+gW7V4pSilMs8ztTWmcZI/5+R5taJT0JQcUA0+e3wesdPAsN9zvL7h+vLEcj6T4tLL2W5vrmRAo/tiXDh1Mll2RlyYx/HA4Xggpiupbn1z1WRCpzTD/iDOWVtkXTfWZSWnSM1gjaWavlIxgtm0tvbxf+3Z0khg6j49LppiC8Y4TDUYIwMhfctymF7ey7+ZS8WYnoU0mPaq7WaVpWpA1W9lPtUzsJZpxSdtQ7sf0Fs/fHvUKiVw68CH1sRVnKrR2qONo6aKdp6Kui/cUyldQNmIIJt6DbTWeh+vvj3V1H1YWJtkv1upyK2Xp0nGbLcD3S+ZW9bqDl+3fW+rt3ITQYlRqFWYOKVWSlWA4abtQ1/BSKC0e++tdCE4ObM3EIFcKJ/0if2zvPWYjU8qjz5DwRiMcUIOuF1ASt0tBr9XcColdtpR/MLvmIbbdKr21ybvpRJT5On5mf/mX/w+jw+PHI5/j8fHI3VZiKm7Vt2cjG9ehbUKJM92sqxSUCpbWtBWsd8fMVYGFDcQxA0yJftXhTWawQjR2XnpQ413bNcLKa73g3Z7T84pvOurG6Nw3mOdTE9FikP0dVJaWNYr67YQo8hGBq2F+X6zK1g35uvMcllIKVJaoTZDK7J7a1RqkwxV+3CoNiFxm2ooRZFTwln3OvnV9N5QoVQWsL425P4l19aHS8ZQTaPVTFVFqF7t1p/2g6zEkbk11ZfiUshKJS5T79b7XFXVfbetgHo7XEosNDJCxavGkZtcOM464YzegqaU/h6lP7xNM+mg8FrpZATJGqUI35XWp5yK3o/W19fag7P1wJYgbvf+XFoJ2X/WUnsPeFtzSK9aWqbRyLVQa68mekBaa3DOsW6ZVsScyFgIITBMI01DqbnPTG5T136e7oFp+mUhAz5ldMfeSiY3poM4Sv87rUBN8uN7BSevQwIhjxa55ZS631o9q/cbWGr/l9OFf/Hf/j4//I0f83f+zt/G+SDSI1uUm2OQ3ePttm7GyE3cDKaPSG9Di9K6up7R33ot972oMdj7JJReKmqssyzOMc8X4rZScu43Y/ck0VpU87whjJ7gB5zvKt1GyxeaJ4Z1ZN1m5nlmWzbqvNBUwSiLsQXrHcM4UB5SN89ZWddEyZXSea0yjmk9g1pKzb0qEVCH0plaCzpprDVYY+6v435wVYGiKSbfp783YbLWJJBkCKN7/9O/pw6BU1qyG3Dv/6pSNF3vF969T0L2zVorau1DORqNQkGhtO3GRZXWVx+tVnJOUqnUSiW+TjDhjietfWJcuhFtLTJZU/QBmeq8SBBIYn89tx5TMuRtGCPBditpW5XMKe2WpJJSM01VSi2viYQGqmKs6CmHELDO4FZHa0XOkJFthbGylit9nXaPjFuW6hnz/n9vQHhr7me49c9O3d9Dg5rJeaOV7xmc90Bor1NW6GP42m+v/h+0lkbXGgM0vvr6a/7ZP/tnHB8O/LW/+ldwYSAuK8saqQ2GUfcMKjZrxrrXKRg3Mqr0CjGC869l9i8/5LDeVgQyDbPGEozDh8D1emG+XkSoGimJUhZNoxvESjKROJwpDVZpjAlYZxnGkWHYsS5XrLNSdpcCuaK0whqHC55xr5hyIaUs7ti5SCbpO7GcM6WKTZxuMgjT2qCblE3ioKVx9hacBosEqACtFaoWStHdA1IsFqWPMb3ykGFDRd2pebk2lH4Fg7T2Wnq1epvYyr93u/yrok/Ya6ezyRGTA6bEHwVkFdakRchZXKFLHxreLtFSyv3Xn/aNN/V+tLQ6EnRVLv7b2qhHZeNWEfbMqW6qdqX/+9I7S1XXETtVWjBjtKjtBS89fLfVFaTPbfjV8JNYEWqt0Ldqou+pXy8WgIJqry7VtymSNjI01Oa2nup9Z7uB4RutiOt5SVmGjl2u5N86OEu69QPyBbZeytaOsr9/2D1AvPdYJ1PDddv4wz/6o/6BKP79v/KXCcPIui6sWwStGXXoBqQWmejfbr0+VCnSL7Ym+1DnXP8s/rzxs1wat1LIaoPTBuMcznmscyzzlRg3AVG3KrbxHahca6HUzNgCjXAHEFgn3hcujPjRY4PDnhwxRtG+yYX7uEqJwaoJHpogjlTjDqwopVBKu/dHpRvPNmpXZ7coGrkJCkYVTUHjOhpJmDaya74FVxHM1d3iXCvhitJbEjlgSgDu+nXIcsueCpky39sMuL0bqpIes2l1UxhGjrb6JEO9/ridiZzT60FWggaT9lECUDdD7YR8Ga7w2s/peg/Kezn86aqiZ3Fae50nGHm9xojOUC2tV0p0HSDfL14p52vrfN0e5KVK5aKU6h1lxTQ507dMW3siUn06XlsHpsjc6Db47tm599JaCAW3tVHraz15bV0+5vsqIejGa4nReikCvBY/3LOVtbZrfFbZ0bXGZZ75b37/90UiPyb+2v/o38eHwLquzMtCo3E04jkhb6713vD25vqXh9zG+saf+zQ4e9ahFxdGdclMbWSNoS3WOmzw+DBwvV7Y1pllWeTW5jYJLOSSKGVgLAkfRilz+yrEeMc+HJj2O3wYmOeZYRhZt5XUTX8kE956HYWV9gPV2e+6GXxT3wqEGwdQo2THi/Sg27qJ5GMRrVrdS2CUlL5GS0lbW5V10ODl71dFzoUtbmTZ34hUS/8sVc+Ite9naU16zV6OaV5bGdV3c61xB3rLZV37zV/kd9unP25n5bXCkYn+a9as1HtfqT7JSq33kzRpj+5fsRTpEvwIC8VahzJ98qnV/QzJIE4SSLlPcJvwMvvvCdYbmUJzK5ULN6BIBbmQmlQcqvfDshpSIgOLoukeI/17vu1wKf29fxKUNDoSre/c2y0h/3mJ5r9HcN4K61vJ0b+y12nWre+7Bcyt8evjYmhc55nf/4M/YF1W5nnmb/ze/xgfAtu6sCzLXUrQB9/f/G1q2O/p2+itB2jt0Lj7xFFeQd8p9ZFVk4lpUwKqVlr4kNJjOq4Xi7aGuG29fGqkFPvtXyml4lPBD4Fh6uJRHe43OIfWDj+Ictu6LKzrSlwX4hb7Tq2Xcj0o0VJiqZ6pVB9ktf7ZKi0XlAyDFH6whGkgrYm4rMSuc2uDiE45axmHqQ+yhNtobC+DtaHmyrIsbHGj5CLfXZOSSrJrvfNpW73tGlUfCCleE0Ff5xgjAVlEFoXSxPu01T7Y+e9aCzTUt37v00pL9eHQ64hRUVUfVnUalK5y0b9OlmXtFILHeaEmNtXu35nEQpUs2JDBXK39zOT7JXgLztaQSuQ19G/1/uuF0bOlbjJjKa1XJbR72dqT//0zoHUsbe+PbyAY1aSiaJRPmCjmO6PvuzPnJ+NwpTSGbsdyG13fUnfPdDe5P21M702kzo458yd/+qf8o3/8j2mt8rf+1t9k6DC/6/VKq5X9Yc8wDvfhktbtrg2rqsKoV5GkWxaVg63u64B2G7XfvnQlEhdGG3QRwLI2wmLwwbEsM9uyiD9n/zpyytS6EVMmlEwqmXE3EdSA1pqchZo1DhPeDTgbGIeNbQ3EdWPdNuktde5fWLlfaHLR9Ivs9q3K3S3Dlir+mVoplDEMu5EwBMk8iIKEsrLSsU7Ey1B0CpN00H3+xLgbGHeBm2ZNKZX71F6BM33QkQU+aewrO0gpqUCUUlJ1OEuuIq51OV9JOcn0sn07a3673RAQQsnlW9VXo9GU9Orey2VpraVU+T3nbF8z3HL1LSUgi4o+da9VhjzSc9b7mkQC5NZ6QSlSQiqF9M1NXtttIn3/DtRtV3lrIdU9eCt0NA/9P8qqqEK/QPrv3wK8denL2+WQSx8sv15m95+/gzP23ZnTGFnOliI1cg/MSqWpej98Yhcnkztct9prVZpjrVHGkpTiJ7/4in/yX/6XhGni9373d5mmHcv1wny5cMOhTtPYIYPSXCsEpSNfSA+9fktJFrLcblbVP/z75AyHUh1n1wWglLFoZzDB4YbA7BzLciXHnjl1pZGpJdOWPlHLkbqN5DAx9t3r7Tlv2cs4Q5gmwrqwzDMlJ3JOpCTYXpku2n4YdH+dDdGbeR143O5y+bwFfH2jKKHkwtIGCeZWJaCq6js+mXx/GmDGyPOpvnK6ZYbbqoXmvtU33XpZxW1X14H7ShzZ9KYJLqAYyNsmXqI9q4niolQx2lq8D+Sc70O+W6DV3if7IdwHW0Kx6kOT1nq/LPOGm51Bq5WW5QymLr0JfU15D0zp6e9DJ14Hl6rdDwZK3bK8uv90q/5eq7L+efGaCO7MhdvkrL8zeta8PX+9XV5Z1DhKf16tzH36La3E9yxr70XK7aK/lZH9FN1q5trE01AVoEVab5hl1/MqdKQU/OEf/RHtv/gvoDV+73f/GsM0MV8vXOfl3qeEabijKLTRQO67q9LZIp8OCApG3z5kdX/Vun/A/SJDKz7pUW77UYd3gWEYWOYr67pQcr3v/UotlDVTUiRuK95HcopC5LYypKE/t9MB1xquwwG3baFkUSTcYpQMUl9L9HY/JCLLSUcG3U7Pt6Qsbtmp6/Pclviqiaqd1lr6tyqDG90Dq90CpR+6W/CAurdFSqnbulD6qJ4BpeSVz1d1OGBtjXEMr+dgCFLC3Z7vNsS5HRutsE7oe3fEUbtVX6/zhVxuSK6eXe9ggH75t9fK7NbafMsEqJfntbZPOrj2yQ/uf+7WV94r6n447m3ZbRbQ/4o1woq5feaNVxzy63S79eTU7tPjegMt3D8fI8HY24lbTH7aW//y47uDs9ReCtRv9XT3gqD3mrV/uKlkefH9thNAKdL35CKEWWP4sy+/5B//03/Kum387b/xewy7A+uyMM8LpVb2rdx3T8YIDlTdWRv99r2tXEq59yY3P4rbEOPTSfftlWs0yniBDWqPtwODHxnCxHy9sqxXYozSMyqBlpWaWJYk/p15w3mPHwJ+GkRGQwurRLh+Ukq74Mk542LExUyKkRyF3F1ztyJUclCNVa83+6eHq8lh0P2Wbv1wU/J9EHf7L/cDVYooCCB2EEprYc00aFqyLHSKHq/VmJRutwuul3W3I6pkRdLqLbX3jEa7ZxBZxr++FnVbZbROCL71oHS2ya36Ua9K6Lc+9EZOpgdBKVlahR6Y8LovpdGfp90vhztwgdeL5lZy30vv2+14ixL92tuqztVUaCFX36qNTty4JZveq3SkT763e59C8oztf6kD3lG3iXGTlVv+3nvO1idT/Qj0/kVujFdtzk/H3pDvXzC36V2NUBuDHRmnkWEaeTqd+D/+J/8JP//5z/n7f+/vsdvtZR+5rBKgh8p+N6ERYatbBhB0Sb+Fb+wH9C/1PPWToVL75Eu53fACh1MYKgatLNZ4rAn44JmXa2c0JPnm5dlIOZKvCZc8sQRc2bDO9anu6/pFKnInFoV+wGXR1EnbKpqz20rJcgHQS7l79mpywHV/T/LZ9uFRvyT7Dv6XBjC9dtCymlBKViG6lQ42b7Ryq0S4D3PgdeWSc/+sPskkt+nrjbt4ezIJztfHLXO+Zulyf33yND1zK6i1Y1Pb7Tz1A3sr8fsAq3YmTy7pPuG8OUG/GgLdCrlfCr5POko+Dcx7uuvl6q3nU9zLTHWbZ9xOfusn4N5+9OajivWElNNCZfs0Uyuj+g4XOniSVsXRTUTNC6V+zz3n7ctT/eBX5Bb79MsAma6WevtA230KeH+d8o3gnWcaJ/a7PdfLhZ/99Et++tMvuV5n/vf/2/8djw9vOJ2e2TaR49TAbtqh7W0RLED51iUf5Jzp+05NjIH6x65uqI1+ALhlAnXvDWTgoaW/7OAF56UsXdeFdbmybUsHL2iUFsznrVS16yoeGWEg+CqSmU5IyzeWgtYa7x3VGJy1tBIo40CMGylGUk6yvO837m2goTRQb1Cx+5yBO/dByYVR2uuqQtED4HawaiP3gLl9CqbKglyVgrkpIGJuJ1D+6abuGe0WoK9Zqr0G0CcHX86BlMv3aT6vF8inrUjrFLz76+4/Si8DbyVhKaI5dQvMT17i/QzKv11fs2RfZd1L1tav9hsIw7z2lbpzKlWXHLkF7T2Dtxujp38BcivdkU2tl7D0Evf232/g+94/9QA1XVtXeuWSa29nfulNffL4FfC93uPcrmrafbF6hyL1kvEO06KhbhC8/gmpG7C4N80fPz7z8etvuF5mWm38x//xf8pyXfiP/g//gIeHB+b5TIwrz08vlFwYxoEwDtwggr0N68+p7gic24DgdmZupZu88tuk7vUwGSNfUFVQC31KKpxMZz3eB7b1yrIIeKHcJog9UmrKrLlStkz2otObrLtjdVEdGIEMY5QJ1GqpzWFDoORMjklK3pwpJfWbWHqWqnopyWsPfev1PwWp09/brWdt1HsA0G465JIRSmeh3Hamt/JVo751+O8UqPa6JvlWtu4XMbeppxJh6H5EpSpV3P8u7bXkvd8vPbhuCCL5X3kdAN3wuZ/OGD75fm/nUd0qg9tz3BOkll4eAWkU2h3zKpxkyZCdo3cvrW8XuVxwr+en3vvd1+B8/Qr6n0dJKdzPXkPcynJOxCgXcS19mvxavf+5j19Z1t6+sdaRLMbouy/I7UXdg+ITcLn0MaJ+cPv/l8uFNW4iLJwytcgbWpeNf/gP/xHTOPIP/sF/yG7aswDbtnI+naSXVUqQHrqX1bdS6D4TeO3Baq3kLKuMGw71tsAWdMcnYku89qu18zZvrBLnHIN3eO+Yl1lck2OSPjcXITwowRzHulBzwlhHTh7jfJcA8VjrXw+OVmjl0dbiaqP5So6RnEU4WQyAMrGXva1BLa8L/k9y5ycl3GsCu1UK9x+337/t4Dr4vrVKzvLereYewPzSv10/CYrX1qVfGq1fBNCDnVuClUu7vgbna4mrvpUCX4Puk9fbT+0taP+7F3I/br1kVED+pK0RqmrfjWp9b8GM6ZhX0wEqt/enXl9B/SQLtl6+l64PdEP1tHr/NO4P0+l9qE6uR8AzORdSFtlT0SGurwyb9lo6/3kP9edhVX/9+PXj14//4R//5g3orx+/fvz68T/o49fB+evHrx//jj5+HZy/fvz68e/o49fB+evHrx//jj5+HZy/fvz68e/o49fB+evHrx//jj7+f9PcHHQUSQtTAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1. View single image\n",
-    "plt.imshow(image.permute(1, 2, 0)) # adjust for matplotlib\n",
-    "plt.title(class_names[label])\n",
-    "plt.axis(False);\n",
-    "print(f\"Original image shape: {image.shape}\")\n",
-    "\n",
-    "# 2. Turn image into feature maps\n",
-    "image_out_of_conv = conv2d(image.unsqueeze(0)) # add batch dimension to avoid shape errors\n",
-    "print(f\"Image feature map shape: {image_out_of_conv.shape}\")\n",
-    "\n",
-    "# 3. Flatten the feature maps\n",
-    "image_out_of_conv_flattened = flatten(image_out_of_conv)\n",
-    "print(f\"Flattened image feature map shape: {image_out_of_conv_flattened.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fe802095-e944-4607-b3a7-891ba452372b",
-   "metadata": {},
-   "source": [
-    "Woohoo! It looks like our `image_out_of_conv_flattened` shape is very close to our desired output shape: \n",
-    "    \n",
-    "* **Desried output (flattened 2D patches):** (196, 768) -> ${N \\times\\left(P^{2} \\cdot C\\right)}$\n",
-    "* **Current shape:** (1, 768, 196)\n",
-    "\n",
-    "The only difference is our current shape has a batch size and the dimensions are in a different order to the desired output.\n",
-    "\n",
-    "How could we fix this?\n",
-    "\n",
-    "Well, how about we rearrange the dimensions?\n",
-    "\n",
-    "We can do so with `torch.Tensor.permute()` just like we do when rearranging image tensors to plot them with matplotlib.\n",
-    "\n",
-    "Let's try."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "47f571a1-2303-4981-85f5-33936b39cf14",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Patch embedding sequence shape: torch.Size([1, 196, 768]) -> [batch_size, num_patches, embedding_size]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get flattened image patch embeddings in right shape \n",
-    "image_out_of_conv_flattened_reshaped = image_out_of_conv_flattened.permute(0, 2, 1) # [batch_size, P^2•C, N] -> [batch_size, N, P^2•C]\n",
-    "print(f\"Patch embedding sequence shape: {image_out_of_conv_flattened_reshaped.shape} -> [batch_size, num_patches, embedding_size]\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "224d751a-11a0-4645-a225-cd36e507ebf8",
-   "metadata": {},
-   "source": [
-    "Yes!!!\n",
-    "\n",
-    "We've now matched the desired input and output shapes for the patch embedding layer of the ViT architecture using a couple of PyTorch layers.\n",
-    "\n",
-    "How about we visualize one of the flattened feature maps?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "e4204163-8689-4b9e-8828-4e1e20d9316e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1584x1584 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Get a single flattened feature map\n",
-    "single_flattened_feature_map = image_out_of_conv_flattened_reshaped[:, :, 0]\n",
-    "\n",
-    "# Plot the flattened feature map visually\n",
-    "plt.figure(figsize=(22, 22))\n",
-    "plt.imshow(single_flattened_feature_map.detach().numpy())\n",
-    "plt.title(f\"Flattened feature map shape: {single_flattened_feature_map.shape}\")\n",
-    "plt.axis(False);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2fe6c0a6-687c-473c-a30f-6b01b1df533f",
-   "metadata": {},
-   "source": [
-    "Hmm, the flattened feature map doesn't look like much visually, but that's not what we're concerned about, this is what will be the output of the patching embedding layer and the input to the rest of the ViT architecture.\n",
-    "\n",
-    "TK image - single image -> conv2d -> flatten -> get the output above (show the workflow and transformation, this could be the gif we've but using but extended to work with the flatten section)\n",
-    "\n",
-    "> **Note:** The [original Transformer architecture](https://arxiv.org/abs/1706.03762) was designed to work with text. The Vision Transformer architecture (ViT) had the goal of using the original Transformer for images. This is why the input to the ViT architecture is processed in the way it is. We're essentially taking a 2D image and formatting it so it appears as a 1D sequence of text. \n",
-    "\n",
-    "How about we view the flattened feature map in tensor form?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "0cdeb08e-948d-4810-ae8d-c607b3b9feec",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(tensor([[0.2642, 1.1367, 1.0221, 0.9712, 1.0950, 1.2478, 1.2884, 1.1481, 0.9640,\n",
-       "          0.6204, 0.5996, 0.5855, 0.5560, 0.4992, 1.1578, 1.0633, 0.9593, 1.1931,\n",
-       "          1.3194, 1.1306, 0.7317, 0.4322, 0.6025, 0.7246, 0.7891, 0.5383, 0.4786,\n",
-       "          0.7098, 1.0163, 0.9368, 1.1675, 1.3373, 1.0429, 0.7497, 0.7445, 0.4270,\n",
-       "          0.7430, 0.8750, 0.6105, 0.5147, 1.0207, 0.7852, 1.0669, 1.0157, 1.3291,\n",
-       "          1.0117, 0.4848, 0.6802, 0.8365, 0.7736, 0.8618, 0.9144, 0.8926, 0.9795,\n",
-       "          0.7475, 0.7585, 0.9371, 1.1937, 1.0068, 0.6377, 0.7283, 0.9625, 1.0372,\n",
-       "          0.8920, 0.9372, 0.9034, 0.9683, 0.9405, 0.5958, 0.8740, 0.9419, 0.8599,\n",
-       "          0.5429, 0.6954, 1.0202, 0.9093, 1.0003, 0.7619, 0.8472, 0.8062, 0.6418,\n",
-       "          0.7741, 0.5791, 0.9816, 0.7965, 0.7202, 0.6424, 0.9137, 0.8264, 1.0243,\n",
-       "          1.0920, 0.9548, 0.9166, 0.7937, 0.4675, 0.5346, 0.7774, 1.1001, 0.3298,\n",
-       "          0.4832, 0.5324, 0.7486, 0.8303, 0.8101, 0.9969, 0.9931, 1.0058, 0.6002,\n",
-       "          0.6643, 0.7254, 0.8453, 1.1323, 0.5384, 0.4798, 0.6725, 0.8014, 0.7044,\n",
-       "          0.7988, 0.8185, 0.8911, 0.9720, 0.8939, 0.6234, 0.5674, 0.5775, 1.0011,\n",
-       "          0.6199, 0.6465, 0.6503, 0.6215, 0.8154, 0.7950, 0.8647, 0.9872, 0.8513,\n",
-       "          0.8833, 0.5799, 0.5914, 0.6936, 1.0554, 0.5140, 0.6462, 0.6982, 0.7445,\n",
-       "          0.7394, 0.8124, 0.7462, 0.9183, 0.7471, 0.9436, 0.7147, 0.6396, 0.5795,\n",
-       "          1.0201, 0.5467, 0.7408, 0.6854, 0.6624, 0.7465, 0.5077, 0.7633, 0.8709,\n",
-       "          1.0026, 0.7276, 0.7847, 0.5811, 0.5521, 1.0318, 0.8041, 0.8868, 0.5559,\n",
-       "          0.5889, 0.7236, 0.6976, 0.7940, 0.9365, 0.9110, 0.8182, 0.7013, 0.4890,\n",
-       "          0.8364, 1.0031, 0.1976, 1.0262, 1.1979, 0.9982, 0.9644, 0.8868, 0.9556,\n",
-       "          1.0204, 1.0060, 0.9586, 0.9351, 0.8819, 0.9290, 0.9289]],\n",
-       "        grad_fn=<SelectBackward0>),\n",
-       " True,\n",
-       " torch.Size([1, 196]))"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# See the flattened feature map as a tensor\n",
-    "single_flattened_feature_map, single_flattened_feature_map.requires_grad, single_flattened_feature_map.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6cc9dae5-5bf2-45b4-8266-cf733868441d",
-   "metadata": {},
-   "source": [
-    "Beautiful!\n",
-    "\n",
-    "We've turned our single 2D image into a single 1D learnable embedding vector (or \"Linear Projection of Flattned Patches\" in Figure 1 of the ViT paper)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b165987a-8370-471a-a663-711e0c6e60db",
-   "metadata": {},
-   "source": [
-    "### TK - 4.5 Turning the ViT patch embedding layer into a PyTorch module\n",
-    "\n",
-    "Time to put everything we've done for creating the patch embedding into a single PyTorch layer.\n",
-    "\n",
-    "We can do so by subclassing `nn.Module` and creating a small PyTorch \"model\" to do all of the steps above.\n",
-    "\n",
-    "Specifically we'll:\n",
-    "1. Create a class called `PatchEmbedding` which subclasses `nn.Module` (so it can be used a PyTorch layer).\n",
-    "2. Initialize the class with the parameters `in_channels=3`, `patch_size=16` (for ViT-Base) and `embedding_dim=768` (this is $D$ for ViT-Base from Table 1).\n",
-    "3. Create a layer to turn an image into patches using `nn.Conv2d()` (just like in 4.3 above).\n",
-    "4. Create a layer to flatten the patch feature maps into a single dimension (just like in 4.4 above). \n",
-    "5. Define a `forward()` method to take an input and pass it through the layers created in 3 and 4.\n",
-    "6. Make sure the output shape reflects the required output shape of the ViT architecture (${N \\times\\left(P^{2} \\cdot C\\right)}$).\n",
-    "\n",
-    "Let's do it!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "3ef75c7e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1. Create a class which subclasses nn.Module\n",
-    "class PatchEmbedding(nn.Module):\n",
-    "    \"\"\"Turns a 2D input image into a 1D sequence learnable embedding vector.\n",
-    "    \n",
-    "    Args:\n",
-    "        in_channels (int): Number of color channels for the input images. Defaults to 3.\n",
-    "        patch_size (int): Size of patches to convert input image into. Defaults to 16.\n",
-    "        embedding_dim (int): Size of embedding to turn image into. Defaults to 768.\n",
-    "    \"\"\" \n",
-    "    # 2. Initialize the class with appropriate variables\n",
-    "    def __init__(self, \n",
-    "                 in_channels:int=3,\n",
-    "                 patch_size:int=16,\n",
-    "                 embedding_dim:int=768):\n",
-    "        super().__init__()\n",
-    "        \n",
-    "        # 3. Create a layer to turn an image into patches\n",
-    "        self.patcher = nn.Conv2d(in_channels=in_channels,\n",
-    "                                 out_channels=embedding_dim,\n",
-    "                                 kernel_size=patch_size,\n",
-    "                                 stride=patch_size,\n",
-    "                                 padding=0)\n",
-    "\n",
-    "        # 4. Create a layer to flatten the patch feature maps into a single dimension\n",
-    "        self.flatten = nn.Flatten(start_dim=2, # only flatten the feature map dimensions into a single vector\n",
-    "                                  end_dim=3)\n",
-    "\n",
-    "    # 5. Define the forward method \n",
-    "    def forward(self, x):\n",
-    "        # Create assertion to check that inputs are the correct shape\n",
-    "        image_resolution = x.shape[-1]\n",
-    "        assert image_resolution % patch_size == 0, f\"Input image size must be divisble by patch size, image shape: {image_resolution}, patch size: {patch_size}\"\n",
-    "        \n",
-    "        # Perform the forward pass\n",
-    "        x_patched = self.patcher(x)\n",
-    "        x_flattened = self.flatten(x_patched) \n",
-    "        # 6. Make sure the output shape has the right order \n",
-    "        return x_flattened.permute(0, 2, 1) # adjust so the embedding is on the final dimension [batch_size, P^2•C, N] -> [batch_size, N, P^2•C]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5270aa24-85b7-4b5a-a799-8e5eeca47f8f",
-   "metadata": {},
-   "source": [
-    "`PatchEmbedding` layer created!\n",
-    "\n",
-    "Let's try it out on a single image."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "a5599575-44cc-46c9-95a4-e65eb1379a59",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input image shape: torch.Size([1, 3, 224, 224])\n",
-      "Output patch embedding shape: torch.Size([1, 196, 768])\n"
-     ]
-    }
-   ],
-   "source": [
-    "set_seeds()\n",
-    "\n",
-    "# Create an instance of patch embedding layer\n",
-    "patchify = PatchEmbedding(in_channels=3,\n",
-    "                          patch_size=16,\n",
-    "                          embedding_dim=768)\n",
-    "\n",
-    "# Pass a single image through\n",
-    "print(f\"Input image shape: {image.unsqueeze(0).shape}\")\n",
-    "patch_embedded_image = patchify(image.unsqueeze(0)) # add an extra batch dimension on the 0th index, otherwise will error\n",
-    "print(f\"Output patch embedding shape: {patch_embedded_image.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a4d59a81-0cef-4251-832b-5f69da199996",
-   "metadata": {},
-   "source": [
-    "Beautiful!\n",
-    "\n",
-    "The output shape matches the ideal input and output shapes we'd like to see from the patch embedding layer:\n",
-    "\n",
-    "* **Input:** The image starts as 2D with size ${H \\times W \\times C}$.\n",
-    "* **Output:** The image gets converted to a sequence of flattened 2D patches with size ${N \\times\\left(P^{2} \\cdot C\\right)}$.\n",
-    "\n",
-    "Where:\n",
-    "* $(H, W)$ is the resolution of the original image.\n",
-    "* $C$ is the number of channels.\n",
-    "* $(P, P)$ is the resolution of each image patch (**patch size**).\n",
-    "* $N=H W / P^{2}$ is the resulting number of patches, which also serves as the effective input sequence length for the Transformer.\n",
-    "    \n",
-    "We've now replicated the patch embedding for equation 1 but not the class token/position embedding.\n",
-    "\n",
-    "We'll get to these later on.\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-replicating-the-patch-embedding-layer.png\" width=900 alt=\"replicating the vision transformer architecture patch embedding layer\"/>\n",
-    "\n",
-    "*Our `PatchEmbedding` class (right) replicates the patch embedding of the ViT architecture from Figure 1 and Equation 1 from the ViT paper (left). However, the learnable class embedding and position embeddings haven't been created yet. These will come soon.*\n",
-    "\n",
-    "Let's now get a summary of our `PatchEmbedding` layer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "e440be53-d72c-42b8-87c8-1c31c4262c16",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "========================================================================================================================\n",
-       "Layer (type (var_name))                  Input Shape          Output Shape         Param #              Trainable\n",
-       "========================================================================================================================\n",
-       "PatchEmbedding (PatchEmbedding)          [1, 3, 224, 224]     [1, 196, 768]        --                   True\n",
-       "├─Conv2d (patcher)                       [1, 3, 224, 224]     [1, 768, 14, 14]     590,592              True\n",
-       "├─Flatten (flatten)                      [1, 768, 14, 14]     [1, 768, 196]        --                   --\n",
-       "========================================================================================================================\n",
-       "Total params: 590,592\n",
-       "Trainable params: 590,592\n",
-       "Non-trainable params: 0\n",
-       "Total mult-adds (M): 115.76\n",
-       "========================================================================================================================\n",
-       "Input size (MB): 0.60\n",
-       "Forward/backward pass size (MB): 1.20\n",
-       "Params size (MB): 2.36\n",
-       "Estimated Total Size (MB): 4.17\n",
-       "========================================================================================================================"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create random input sizes\n",
-    "random_input_image = (1, 3, 224, 224)\n",
-    "random_input_image_error = (1, 3, 250, 250) # will error because image size is incompatible with patch_size\n",
-    "\n",
-    "# Get a summary of the input and outputs of PatchEmbedding\n",
-    "summary(PatchEmbedding(), \n",
-    "        input_size=random_input_image, # try swapping this for \"random_input_image_error\" \n",
-    "        col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
-    "        col_width=20,\n",
-    "        row_settings=[\"var_names\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8576f2f1-a2ad-4874-8f76-08156371f444",
-   "metadata": {},
-   "source": [
-    "### TK 4.6 Creating the class token embedding\n",
-    "\n",
-    "Okay we've made the image patch embedding, time to get to work on the class token embedding.\n",
-    "\n",
-    "Or $\\mathbf{x}_\\text {class }$ from equation 1.\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-the-class-token.png\" width=900 alt=\"class token embedding highlight from the vision transformer paper figure 1 and section 3.1\"/>\n",
-    "\n",
-    "*Left: Figure 1 from the ViT paper with the \"classification token\" or `[class]` embedding token we're going to recreate highlighted. Right: Equation 1 and section 3.1 of the ViT paper that relate to the learnable class embedding token.*\n",
-    "\n",
-    "Reading the second paragraph of section 3.1 from the ViT paper, we see the following description: \n",
-    "\n",
-    "> Similar to BERT's `[ class ]` token, we prepend a learnable embedding to the sequence of embedded patches $\\left(\\mathbf{z}_{0}^{0}=\\mathbf{x}_{\\text {class }}\\right)$, whose state at the output of the Transformer encoder $\\left(\\mathbf{z}_{L}^{0}\\right)$ serves as the image representation $\\mathbf{y}$ (Eq. 4). \n",
-    "\n",
-    "> **Note:** [BERT](https://arxiv.org/abs/1810.04805) (Bidirectional Encoder Representations from Transformers) is one of the original machine learning research papers to use the Transformer architecture to achieve outstanding results on natural language processing (NLP) tasks and is where the idea of having a `[ class ]` token at the start of a sequence originated, class being a description for the \"classification\" class the sequence belonged to.\n",
-    "\n",
-    "So we need to \"preprend a learnable embedding to the sequence of embedded patches\".\n",
-    "\n",
-    "Let's start by viewing our sequence of embedded patches tensor (created in 4.5) and its shape."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "50381789-d73d-4648-9144-4d48da87318f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[[-0.4923,  0.0265, -0.0909,  ...,  0.1478, -0.0986,  0.2243],\n",
-      "         [-0.9849,  0.3805, -0.3638,  ...,  0.6115, -0.0805,  0.2097],\n",
-      "         [-0.6015,  0.1235, -0.2506,  ...,  0.6307, -0.4673,  0.2756],\n",
-      "         ...,\n",
-      "         [-0.6668,  0.1713, -0.1711,  ...,  0.4699, -0.2881,  0.2599],\n",
-      "         [-0.6983,  0.1949, -0.1884,  ...,  0.5152, -0.3126,  0.2151],\n",
-      "         [-0.6889,  0.1862, -0.1444,  ...,  0.5019, -0.3564,  0.2378]]],\n",
-      "       grad_fn=<PermuteBackward0>)\n",
-      "Patch embedding shape: torch.Size([1, 196, 768]) -> [batch_size, number_of_patches, embedding_dimension]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# View the patch embedding and patch embedding shape\n",
-    "print(patch_embedded_image) \n",
-    "print(f\"Patch embedding shape: {patch_embedded_image.shape} -> [batch_size, number_of_patches, embedding_dimension]\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d5e417fc-70c9-43d4-a294-e7728a24bf42",
-   "metadata": {},
-   "source": [
-    "To \"prepend a learnable embedding to the sequence of embedded patches\" we need to create a learnable embedding in the shape of the `embedding_dimension` ($D$) and then add it to the `number_of_patches` dimension. \n",
-    "\n",
-    "Or in pseudocode:\n",
-    "\n",
-    "```python\n",
-    "patch_embedding = [image_patch_1, image_patch_2, image_patch_3...]\n",
-    "class_token = learnable_embedding\n",
-    "patch_embedding_with_class_token = torch.cat((class_token, patch_embedding), dim=1)\n",
-    "```\n",
-    "\n",
-    "Notice the concatenation (`torch.cat()`) happens on `dim=1` (the `number_of_patches` dimension).\n",
-    "\n",
-    "Let's create a learnable embedding for the class token.\n",
-    "\n",
-    "To do so, we'll get the batch size and embedding dimension shape and then we'll create a `torch.ones()` tensor in the shape `[batch_size, 1, embedding_dimension]`.\n",
-    "\n",
-    "And we'll make the tensor learnable by passing it to `nn.Parameter()` with `requires_grad=True`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "cc0bb859-e62e-41a8-9a47-339e4272a152",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]], grad_fn=<SliceBackward0>)\n",
-      "Class token shape: torch.Size([1, 1, 768]) -> [batch_size, number_of_tokens, embedding_dimension]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get the batch size and embedding dimension\n",
-    "batch_size = patch_embedded_image.shape[0]\n",
-    "embedding_dimension = patch_embedded_image.shape[-1]\n",
-    "\n",
-    "# Create the class token embedding as a learnable parameter that shares the same size as the embedding dimension (D)\n",
-    "class_token = nn.Parameter(torch.ones(batch_size, 1, embedding_dimension), # [batch_size, number_of_patches, embedding_dimension]\n",
-    "                           requires_grad=True) # make sure the embedding is learnable\n",
-    "\n",
-    "# Show the first 10 examples of the class_token\n",
-    "print(class_token[:, :, :10])\n",
-    "\n",
-    "# Print the class_token shape\n",
-    "print(f\"Class token shape: {class_token.shape} -> [batch_size, number_of_tokens, embedding_dimension]\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f1ce6046-f018-4099-96d1-31dad6fc423b",
-   "metadata": {},
-   "source": [
-    "> **Note:** Here we're only creating the class token embedding as [`torch.ones()`](https://pytorch.org/docs/stable/generated/torch.ones.html) for demonstration purposes, in reality, you'd likely create the class token embedding with [`torch.randn()`](https://pytorch.org/docs/stable/generated/torch.randn.html) (start with a random number). \n",
-    "\n",
-    "See how the `number_of_patches` dimension of `class_token` is `1` since we only want to prepend one class token value to the start of the patch embedding sequence.\n",
-    "\n",
-    "Now we've got the class token embedding, let's prepend it to our sequence of image patches, `patch_embedded_image`.\n",
-    "\n",
-    "We can so using [`torch.cat()`](https://pytorch.org/docs/stable/generated/torch.cat.html) and set `dim=1` (so `class_token`'s `number_of_patches` dimension is preprended to `patch_embedded_image`'s `number_of_patches` dimension)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "a7287b01-76cb-4371-ab07-981f7bbf2be5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[[ 1.0000,  1.0000,  1.0000,  ...,  1.0000,  1.0000,  1.0000],\n",
-      "         [-0.4923,  0.0265, -0.0909,  ...,  0.1478, -0.0986,  0.2243],\n",
-      "         [-0.9849,  0.3805, -0.3638,  ...,  0.6115, -0.0805,  0.2097],\n",
-      "         ...,\n",
-      "         [-0.6668,  0.1713, -0.1711,  ...,  0.4699, -0.2881,  0.2599],\n",
-      "         [-0.6983,  0.1949, -0.1884,  ...,  0.5152, -0.3126,  0.2151],\n",
-      "         [-0.6889,  0.1862, -0.1444,  ...,  0.5019, -0.3564,  0.2378]]],\n",
-      "       grad_fn=<CatBackward0>)\n",
-      "Sequence of patch embeddings with class token prepended shape: torch.Size([1, 197, 768]) -> [batch_size, number_of_patches, embedding_dimension]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Add the class token embedding to the front of the patch embedding\n",
-    "patch_embedded_image_with_class_embedding = torch.cat((class_token, patch_embedded_image), \n",
-    "                                                      dim=1) # concat on first dimension\n",
-    "\n",
-    "# Print the sequence of patch embeddings with the prepended class token embedding\n",
-    "print(patch_embedded_image_with_class_embedding)\n",
-    "print(f\"Sequence of patch embeddings with class token prepended shape: {patch_embedded_image_with_class_embedding.shape} -> [batch_size, number_of_patches, embedding_dimension]\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "79fd9252-c15e-40a6-965a-a872e0e09cab",
-   "metadata": {},
-   "source": [
-    "Nice! Learnable class token prepended!\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-prepending-the-learnable-class-token.png\" width=900 alt=\"going from a sequence of patch embeddings, creating a learnable class token and then prepending it to the patch embeddings\"/>\n",
-    "\n",
-    "*Reviewing what we've done to create the learnable class token, we start with a sequence of image patch embeddings created by `PatchEmbedding()` on single image, we then created a learnable class token with one value for each of the embedding dimensions and then prepended it to the original sequence of patch embeddings. Note: Using `torch.ones()` to create the learnable class token is mostly for demonstration purposes only, in practice, you'd like create it with `torch.randn()`.* "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "48502c61-16b0-4659-b95f-0e830ae93077",
-   "metadata": {},
-   "source": [
-    "### TK 4.7 Creating the position embedding \n",
-    "\n",
-    "Well, we've got the class token embedding and the patch embedding, now how might we create the position embedding?\n",
-    "\n",
-    "Or $\\mathbf{E}_{\\text {pos }}$ from equation 1 where $E$ stands for \"embedding\".\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-the-position-embeddings.png\" width=900 alt=\"extracting the position embeddings from the vision transformer architecture and comparing them to other sections of the vision transformer paper\"/>\n",
-    "\n",
-    "*Left: Figure 1 from the ViT paper with the position embedding we're going to recreate highlighted. Right: Equation 1 and section 3.1 of the ViT paper that relate to the position embedding.*\n",
-    "\n",
-    "Let's find out more by reading section 3.1 of the ViT paper (bold mine):\n",
-    "\n",
-    "> Position embeddings are added to the patch embeddings to retain positional information. We use **standard learnable 1D position embeddings**, since we have not observed significant performance gains from using more advanced 2D-aware position embeddings (Appendix D.4). The resulting sequence of embedding vectors serves as input to the encoder.\n",
-    "\n",
-    "To start creating the position embeddings, let's view our current embeddings. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "33e5e5bf-e744-4249-ac9b-08986be5ef81",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(tensor([[[ 1.0000,  1.0000,  1.0000,  ...,  1.0000,  1.0000,  1.0000],\n",
-       "          [-0.4923,  0.0265, -0.0909,  ...,  0.1478, -0.0986,  0.2243],\n",
-       "          [-0.9849,  0.3805, -0.3638,  ...,  0.6115, -0.0805,  0.2097],\n",
-       "          ...,\n",
-       "          [-0.6668,  0.1713, -0.1711,  ...,  0.4699, -0.2881,  0.2599],\n",
-       "          [-0.6983,  0.1949, -0.1884,  ...,  0.5152, -0.3126,  0.2151],\n",
-       "          [-0.6889,  0.1862, -0.1444,  ...,  0.5019, -0.3564,  0.2378]]],\n",
-       "        grad_fn=<CatBackward0>),\n",
-       " torch.Size([1, 197, 768]))"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# View the sequence of patch embeddings with the prepended class embedding\n",
-    "patch_embedded_image_with_class_embedding, patch_embedded_image_with_class_embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ecd1d068-7cac-46b7-aa43-ea8f01ffed4a",
-   "metadata": {},
-   "source": [
-    "Equation 1 states that the position embeddings should have the shape $(N + 1) \\times D$ where: \n",
-    "* $N=H W / P^{2}$ is the resulting number of patches, which also serves as the effective input sequence length for the Transformer.\n",
-    "* $D$ is the size of the **patch embeddings**, different values for $D$ can be found in Table 1.\n",
-    "\n",
-    "Luckily we've got both of these values already.\n",
-    "\n",
-    "So let's make a learnable 1D embedding with `torch.ones()` to create $\\mathbf{E}_{\\text {pos }}$. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "5bb7f6d1-0824-47eb-a059-6854da5c7433",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
-      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
-      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
-      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
-      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
-      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
-      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
-      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
-      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
-      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]], grad_fn=<SliceBackward0>)\n",
-      "Position embeddding shape: torch.Size([1, 197, 768]) -> [batch_size, number_of_patches, embedding_dimension]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Calculate N (number of patches)\n",
-    "number_of_patches = int((height * width) / patch_size**2)\n",
-    "\n",
-    "# Get embedding dimension\n",
-    "embedding_dimension = patch_embedded_image_with_class_embedding.shape[2]\n",
-    "\n",
-    "# Create the learnable 1D position embedding\n",
-    "position_embedding = nn.Parameter(torch.ones(1,\n",
-    "                                             number_of_patches+1, \n",
-    "                                             embedding_dimension),\n",
-    "                                  requires_grad=True) # make sure it's learnable\n",
-    "\n",
-    "# Show the first 10 sequences and 10 position embedding values and check the shape of the position embedding\n",
-    "print(position_embedding[:, :10, :10])\n",
-    "print(f\"Position embeddding shape: {position_embedding.shape} -> [batch_size, number_of_patches, embedding_dimension]\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "332facb6-b478-4910-b620-c06d1462d8b8",
-   "metadata": {},
-   "source": [
-    "> **Note:** Only creating the position embedding as `torch.ones()` for demonstration purposes, in reality, you'd likely create the position embedding with `torch.randn()` (start with a random number and improve via gradient descent). \n",
-    "\n",
-    "Position embeddings created!\n",
-    "\n",
-    "Let's add them to our sequence of patch embeddings with a prepended class token."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "03370370-e2c2-4e46-bc20-302b97fba9d7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[[2.0000, 2.0000, 2.0000,  ..., 2.0000, 2.0000, 2.0000],\n",
-      "         [0.5077, 1.0265, 0.9091,  ..., 1.1478, 0.9014, 1.2243],\n",
-      "         [0.0151, 1.3805, 0.6362,  ..., 1.6115, 0.9195, 1.2097],\n",
-      "         ...,\n",
-      "         [0.3332, 1.1713, 0.8289,  ..., 1.4699, 0.7119, 1.2599],\n",
-      "         [0.3017, 1.1949, 0.8116,  ..., 1.5152, 0.6874, 1.2151],\n",
-      "         [0.3111, 1.1862, 0.8556,  ..., 1.5019, 0.6436, 1.2378]]],\n",
-      "       grad_fn=<AddBackward0>)\n",
-      "Patch embeddings, class token prepended and positional embeddings added shape: torch.Size([1, 197, 768]) -> [batch_size, number_of_patches, embedding_dimension]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Add the position embedding to the patch and class token embedding\n",
-    "patch_and_position_embedding = patch_embedded_image_with_class_embedding + position_embedding\n",
-    "print(patch_and_position_embedding)\n",
-    "print(f\"Patch embeddings, class token prepended and positional embeddings added shape: {patch_and_position_embedding.shape} -> [batch_size, number_of_patches, embedding_dimension]\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "80a39e97-4504-4931-9cec-2569389f3faf",
-   "metadata": {},
-   "source": [
-    "Notice how the values of each of the elements in the embedding tensor increases by 1 (this is because of the position embeddings being created with `torch.ones()`). \n",
-    "\n",
-    "> **Note:** We could put both the class token embedding and position embedding into their own layer if we wanted to. But we'll see later on how they can be incorporated into the overall ViT architecture's `forward()` method.\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-patch-embeddings-with-learnable-class-token-and-position-embeddings.png\" width=900 alt=\"patch embeddings with learnable class token and position embeddings\"/>\n",
-    "\n",
-    "*The workflow we've used for adding the position embeddings to the sequence of patch embeddings and class token. Note: `torch.ones()` only used to create embeddings for illustration purposes, in practice, you'd likely use `torch.randn()` to start with a random number.*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6654c7ed-eb94-408b-b435-9b352c84328b",
-   "metadata": {},
-   "source": [
-    "### TK 4.8 Putting it all together: from image to embedding\n",
-    "\n",
-    "Alright, we've come a long way in terms of turning our input images into an embedding and replicating equation 1 from section 3.1 of the ViT paper:\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\mathbf{z}_{0} &=\\left[\\mathbf{x}_{\\text {class }} ; \\mathbf{x}_{p}^{1} \\mathbf{E} ; \\mathbf{x}_{p}^{2} \\mathbf{E} ; \\cdots ; \\mathbf{x}_{p}^{N} \\mathbf{E}\\right]+\\mathbf{E}_{\\text {pos }}, & & \\mathbf{E} \\in \\mathbb{R}^{\\left(P^{2} \\cdot C\\right) \\times D}, \\mathbf{E}_{\\text {pos }} \\in \\mathbb{R}^{(N+1) \\times D}\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "Let's now put everything together in a single code cell and go from input image ($x$) to output embedding ${z}_0$.\n",
-    "\n",
-    "We can do so by:\n",
-    "1. Setting the patch size (we'll use `16` as it's widely used throughout the paper and for ViT-Base).\n",
-    "2. Getting a single image, printing it's shape and storing its height and width.\n",
-    "3. Adding a batch dimension to the single image so it's compatible with our `PatchEmbedding` layer.\n",
-    "4. Creating a `PatchEmbedding` layer with a `patch_size=16` and `embedding_dim=768` (from Table 1 for ViT-Base).\n",
-    "5. Passing the single image through the `PatchEmbedding` layer in 4 to create a sequence of patch embeddings.\n",
-    "6. Creating a class token embedding like in section 4.6. \n",
-    "7. Prepending the class token emebdding to the patch embeddings created in step 5.\n",
-    "8. Creating a position embedding like in section 4.7.\n",
-    "9. Adding the position embedding to the class token and patch embeddings created in step 7.\n",
-    "\n",
-    "We'll also make sure to set the random seeds with `set_seeds()` and print out the shapes of different tensors along the way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "8de90548-e6b0-4123-90ca-a23b0fab52a9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Image tensor shape: torch.Size([3, 224, 224])\n",
-      "Input image with batch dimension shape: torch.Size([1, 3, 224, 224])\n",
-      "Patching embedding shape: torch.Size([1, 196, 768])\n",
-      "Class token embedding shape: torch.Size([1, 1, 768])\n",
-      "Patch embedding with class token shape: torch.Size([1, 197, 768])\n",
-      "Patch and position embedding shape: torch.Size([1, 197, 768])\n"
-     ]
-    }
-   ],
-   "source": [
-    "set_seeds()\n",
-    "\n",
-    "# 1. Set patch size\n",
-    "patch_size = 16\n",
-    "\n",
-    "# 2. Print shape of original image tensor and get the image dimensions\n",
-    "print(f\"Image tensor shape: {image.shape}\")\n",
-    "height, width = image.shape[1], image.shape[2]\n",
-    "\n",
-    "# 3. Get image tensor and add batch dimension\n",
-    "x = image.unsqueeze(0)\n",
-    "print(f\"Input image with batch dimension shape: {x.shape}\")\n",
-    "\n",
-    "# 4. Create patch embedding layer\n",
-    "patch_embedding_layer = PatchEmbedding(in_channels=3,\n",
-    "                                       patch_size=patch_size,\n",
-    "                                       embedding_dim=768)\n",
-    "\n",
-    "# 5. Pass image through patch embedding layer\n",
-    "patch_embedding = patch_embedding_layer(x)\n",
-    "print(f\"Patching embedding shape: {patch_embedding.shape}\")\n",
-    "\n",
-    "# 6. Create class token embedding\n",
-    "batch_size = patch_embedding.shape[0]\n",
-    "embedding_dimension = patch_embedding.shape[-1]\n",
-    "class_token = nn.Parameter(torch.ones(batch_size, 1, embedding_dimension),\n",
-    "                           requires_grad=True) # make sure it's learnable\n",
-    "print(f\"Class token embedding shape: {class_token.shape}\")\n",
-    "\n",
-    "# 7. Prepend class token embedding to patch embedding\n",
-    "patch_embedding_class_token = torch.cat((class_token, patch_embedding), dim=1)\n",
-    "print(f\"Patch embedding with class token shape: {patch_embedding_class_token.shape}\")\n",
-    "\n",
-    "# 8. Create position embedding\n",
-    "number_of_patches = int((height * width) / patch_size**2)\n",
-    "position_embedding = nn.Parameter(torch.ones(1, number_of_patches+1, embedding_dimension),\n",
-    "                                  requires_grad=True) # make sure it's learnable\n",
-    "\n",
-    "# 9. Add position embedding to patch embedding with class token\n",
-    "patch_and_position_embedding = patch_embedding_class_token + position_embedding\n",
-    "print(f\"Patch and position embedding shape: {patch_and_position_embedding.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "129b4a2b-0a24-461f-a437-ad49925576c4",
-   "metadata": {},
-   "source": [
-    "Woohoo!\n",
-    "\n",
-    "From a single image to patch and position embeddings in a single cell of code.\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-putting-it-all-together.png\" width=900 alt=\"mapping equation 1 from the vision transformer paper to pytorch code\"/> \n",
-    "\n",
-    "*Mapping equation 1 from the ViT paper to our PyTorch code. This is the essence of paper replicating, taking a research paper and turning it into usable code.* \n",
-    "\n",
-    "Now we've got a way to encode our images and pass them to the Transformer Encoder in Figure 1 of the ViT paper.\n",
-    "\n",
-    "<img src=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/images/08-vit-paper-architecture-animation-full-architecture.gif\" alt=\"Vision transformer architecture animation, going from a single image and passing it through a patch embedidng layer and then passing it through the transformer encoder.\" width=900/>\n",
-    "\n",
-    "*Animating the entire ViT workflow: from patch embeddings to transformer encoder to MLP head.* \n",
-    "\n",
-    "From a code perspective, creating the patch embedding is probably the largest section of replicating the ViT paper.\n",
-    "\n",
-    "Many of the other parts of the ViT paper such as the Multi-Head Attention and Norm layers can be created using existing PyTorch layers.\n",
-    "\n",
-    "Onwards!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "02f725de-64d1-41d2-a9d6-374cf6d4f589",
-   "metadata": {},
-   "source": [
-    "## TK. 5. Equation 2: Multi-Head Attention (MSA)\n",
-    "\n",
-    "We've got our input data patchified and embedded, now let's move onto the next part of the ViT architecture.\n",
-    "\n",
-    "To start, we'll break down the Transformer Encoder section into two parts (start small and increase when necessary).\n",
-    "\n",
-    "The first being equation 2 and the second being equation 3.\n",
-    "\n",
-    "Recall equation 2 states:\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\mathbf{z}_{\\ell}^{\\prime} &=\\operatorname{MSA}\\left(\\operatorname{LN}\\left(\\mathbf{z}_{\\ell-1}\\right)\\right)+\\mathbf{z}_{\\ell-1}, & & \\ell=1 \\ldots L\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "This indicates a Multi-Head Attention (MSA) layer wrapped in a LayerNorm (LN) layer with a residual connection (the input to the layer gets added to the output).\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-2-msa-block-mapped-to-equation.png\" alt=\"mapping equation 2 from the ViT paper to the ViT architecture diagram in figure 1\" width=900/> \n",
-    "\n",
-    "*Left: Figure 1 from the ViT paper with Multi-Head Attention and Norm layers as well as the residual connection (+) highlighted within the Transformer Encoder block. Right: Mapping the Multi-Head Self Attention (MSA) layer, Norm layer and residual connection to their respective parts of equation 2 in the ViT paper.*\n",
-    "\n",
-    "Many layers you find in research papers are already implemented in modern deep learning frameworks such as PyTorch.\n",
-    "\n",
-    "In saying this, to replicate these layers and residual connection with PyTorch code we can use:\n",
-    "* Multi-Head Self Attention (MSA) - [`torch.nn.MultiheadAttention()`](https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html). \n",
-    "* Norm (LN or LayerNorm) - [`torch.nn.LayerNorm()`](https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html).\n",
-    "* Residual connection - add the input to output (we'll see this later on when we create the full Transformer Encoder block). "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "97430d7a-a69b-423c-be2b-ac16e7f9f83f",
-   "metadata": {},
-   "source": [
-    "### 5.1 The LayerNorm (LN) layer\n",
-    "\n",
-    "[Layer Normalization](https://paperswithcode.com/method/layer-normalization) (`torch.nn.LayerNorm()` or Norm or LayerNorm or LN) normalizes an input over the last dimension.\n",
-    "\n",
-    "You can set `normalized_shape` to be equal to the dimension size you'd like to noramlize over (in our case it'll be $D$ or `768` for ViT-Base).\n",
-    "\n",
-    "You can find the formal definition of `torch.nn.LayerNorm()` in the [PyTorch documentation](https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html).\n",
-    "\n",
-    "What does it do?\n",
-    "\n",
-    "Layer Normalization helps improve training time and model generalization (ability to adapt to unseen data).\n",
-    "\n",
-    "I like to think of any kind of normalization as \"getting the data into a similar format\" or \"getting data samples into a similar distribution\".\n",
-    "\n",
-    "Imagine trying to walk up (or down) a set of stairs all with differing heights and lengths.\n",
-    "\n",
-    "It'd take some adjustment each step right?\n",
-    "\n",
-    "And what you learn for each step wouldn't necessary help with the next one since they all differ.\n",
-    "\n",
-    "Normalization (including Layer Normalization) is the equivalent of making all the stairs the same height and length except the stairs are your data samples.\n",
-    "\n",
-    "So just like you can walk up (or down) stairs with similar heights and lengths much easier than those with unequal heights and widths, neural networks can optimize over data samples with similar distributions (similar mean and standard-deviations) easier than those with varying distributions. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf09f6d0-2480-4577-a694-1171898e1777",
-   "metadata": {},
-   "source": [
-    "### 5.2 The Multi-Head Self Attention (MSA) layer \n",
-    "\n",
-    "The power of the self-attention and multi-head attention (self-attention applied multiple times) were revealed in the form of the original Transformer architecture introduced in the [*Attention is all you need*](https://arxiv.org/abs/1706.03762) research paper.\n",
-    "\n",
-    "There are many resources online to learn more about the Transformer architeture and attention mechanism online such as Jay Alammar's wonderful [Illustrated Transformer post](https://jalammar.github.io/illustrated-transformer/) and [Illustrated Attention post](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/).\n",
-    "\n",
-    "But we're going to focus more on coding an existing PyTorch MSA implementation than creating our own.\n",
-    "\n",
-    "However, you can find the formal defintion of the ViT paper's MSA implementation is defined in Appendix A:\n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-2-appendix-A.png\" alt=\"vision transformer paper figure 1 highlighted with equation 2 and appendix A\" width=900/>\n",
-    "\n",
-    "*Left: Vision Transformer architecture overview from Figure 1 of the ViT paper. Right: Definitions of equation 2, section 3.1 and Appendix A of the ViT paper highlighted to reflect their respective parts in Figure 1.*\n",
-    "\n",
-    "The image above highlights the triple input to the MSA layer.\n",
-    "\n",
-    "This is known as **query, key, value** input or **qkv** for short which is fundamental to the self-attention mechanism.\n",
-    "\n",
-    "In our case, the triple input will be three versions of the output of the Norm layer.\n",
-    "\n",
-    "Or three versions of our layer-normalized image patch and position embeddings created in section 4.8.\n",
-    "\n",
-    "We can implement the MSA layer in PyTorch with `torch.nn.MultiheadAttention()` with the parameters:\n",
-    "* `embed_dim` - the embedding dimension from Table 1 (Hidden size $D$).\n",
-    "* `num_heads` - how many attention heads to use (this is where the term \"multihead\" comes from), this value is also in Table 1 (Heads).\n",
-    "* `dropout` - whether or not to apply dropout to the attention layer (according to Appendix B.1, dropout isn't used after the qkv-projections). "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b1a012fa-9bf6-4cf2-bbd0-30ed692f9d74",
-   "metadata": {},
-   "source": [
-    "### 5.3 Replicating Equation 2 with PyTorch layers\n",
-    "\n",
-    "Let's put everything we've discussed about the LayerNorm (LN) and Multi-Head Attention (MSA) layers in equation 2 into practice.\n",
-    "\n",
-    "To do so, we'll:  \n",
-    "\n",
-    "1. Create a class called `MultiheadSelfAttentionBlock()` that inherits from `torch.nn.Module`.\n",
-    "2. Initialize the class with hyperparameters from Table 1 of the ViT paper for the ViT-Base model.\n",
-    "3. Create a layer normalization (LN) layer with `torch.nn.LayerNorm()` with the `normalized_shape` parameter the same as our embedding dimension ($D$ from Table 1).\n",
-    "4. Create a multi-head attention (MSA) layer with the appropriate `embed_dim`, `num_heads`, `dropout` and `batch_first` parameters.\n",
-    "5. Create a `forward()` method for our class passing the in the inputs through the LN layer and MSA layer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "b76ae98c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1. Create a class that inherits from nn.Module\n",
-    "class MultiheadSelfAttentionBlock(nn.Module):\n",
-    "    \"\"\"Creates a multi-head self-attention block (\"MSA block\" for short).\n",
-    "    \"\"\"\n",
-    "    # 2. Initialize the class with hyperparameters from Table 1\n",
-    "    def __init__(self,\n",
-    "                 embedding_dim:int=768, # from Table 1 for ViT-Base\n",
-    "                 num_heads:int=12, # from Table 1 for ViT-Base\n",
-    "                 attn_dropout:int=0): # doesn't look like the paper uses any dropout in MSABlocks\n",
-    "        super().__init__()\n",
-    "        \n",
-    "        # 3. Create the Norm layer (LN)\n",
-    "        self.layer_norm = nn.LayerNorm(normalized_shape=embedding_dim)\n",
-    "        \n",
-    "        # 4. Create the Multi-Head Attention (MSA) layer\n",
-    "        self.multihead_attn = nn.MultiheadAttention(embed_dim=embedding_dim,\n",
-    "                                                    num_heads=num_heads,\n",
-    "                                                    dropout=attn_dropout,\n",
-    "                                                    batch_first=True) # does our batch dimension come first?\n",
-    "        \n",
-    "    # 5. Create a forward() method to pass the data throguh the layers\n",
-    "    def forward(self, x):\n",
-    "        x = self.layer_norm(x)\n",
-    "        attn_output, _ = self.multihead_attn(query=x, # query embeddings \n",
-    "                                             key=x, # key embeddings\n",
-    "                                             value=x, # value embeddings\n",
-    "                                             need_weights=False) # do we need the weights or just the layer outputs?\n",
-    "        return attn_output"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fc1f0c30-a4ea-41e8-98b2-1a6d8de802d1",
-   "metadata": {},
-   "source": [
-    "> **Note:** Unlike Figure 1, our `MultiheadSelfAttentionBlock()` doesn't include a skip or residual connection (\"$+\\mathbf{z}_{\\ell-1}$\" in equation 2), we'll include this when we create the entire Transformer encoder later on. \n",
-    "\n",
-    "MSABlock created! \n",
-    "\n",
-    "Let's try it out by create an instance of our `MultiheadSelfAttentionBlock` and passing through the `patch_and_position_embedding` variable we created in section 4.8."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "ceb1dfc0-40ad-4cee-bc54-e9a5cec56895",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input shape of MSA block: torch.Size([1, 197, 768])\n",
-      "Output shape MSA block: torch.Size([1, 197, 768])\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Create an instance of MSABlock\n",
-    "multihead_self_attention_block = MultiheadSelfAttentionBlock(embedding_dim=768, # from Table 1 \n",
-    "                                                             num_heads=12) # from Table 1\n",
-    "\n",
-    "# Pass patch and position image embedding through MSABlock\n",
-    "patched_image_through_msa_block = multihead_self_attention_block(patch_and_position_embedding)\n",
-    "print(f\"Input shape of MSA block: {patch_and_position_embedding.shape}\")\n",
-    "print(f\"Output shape MSA block: {patched_image_through_msa_block.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5c9f8384-6120-495a-b253-baff5de58097",
-   "metadata": {},
-   "source": [
-    "Notice how the input and output shape of our data stays the same when it goes through the MSA block.\n",
-    "\n",
-    "This doesn't mean the data doesn't change as it goes through.\n",
-    "\n",
-    "You could try printing the input and output tensor to see how it changes (though this change will be across `1 * 197 * 768` values). \n",
-    "\n",
-    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-2-in-code.png\" width=900 alt=\"vision transformer paper with equation 2 of figure 1 highlighted and equation 2 turned into code\"/>\n",
-    "\n",
-    "*Left: Vision Transformer architecture from Figure 1 with Multi-Head Attention and LayerNorm layers highlighted, these layers make up equation 2 from section 3.1 of the paper. Right: Replicating equation 2 (without the skip connection on the end) using PyTorch layers.* \n",
-    "\n",
-    "We've now officially replicated equation 2 (except for the residual connection on the end but we'll get to this in section 7)!\n",
-    "\n",
-    "Onto the next!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0168ef51-ab1f-4ee7-8e9d-2ee7ecca3c7b",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## TK 6. Equation 3: Multilayer Perceptron (MLP)\n",
-    "\n",
-    "UPTOHERE:\n",
-    "* Replicate equation 3 like replicating equation 2\n",
-    "\n",
-    "* TK also called \"feedforward\" \n",
-    "\n",
-    "> Dropout, when used, is applied **after every dense layer except for the the qkv-projections and directly after adding positional- to patch embeddings.**\n",
-    "\n",
-    "> The MLP contains two layers with a GELU non-linearity\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\mathbf{z}_{\\ell} &=\\operatorname{MLP}\\left(\\operatorname{LN}\\left(\\mathbf{z}_{\\ell}^{\\prime}\\right)\\right)+\\mathbf{z}_{\\ell}^{\\prime}, & & \\ell=1 \\ldots L\n",
-    "\\end{aligned}\n",
-    "$$ \n",
-    "\n",
-    "* TK - GELU in PyTorch -- https://pytorch.org/docs/stable/generated/torch.nn.GELU.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "68d9dbfe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Could also call this \"FeedForward\"\n",
-    "class MLPBlock(nn.Module):\n",
-    "    \"\"\"Creates an MLPBlock of the Vision Transformer architecture.\"\"\"\n",
-    "    def __init__(self,\n",
-    "                 embedding_dim, # embedding dimension (Hidden Size D in Table 1)\n",
-    "                 mlp_size, # MLP size in Table 1\n",
-    "                 dropout=0): # \"Dropout... is applied to every dense layer... (Appendix B.1)\"\n",
-    "        super().__init__()\n",
-    "        \n",
-    "        self.layer_norm = nn.LayerNorm(normalized_shape=embedding_dim)\n",
-    "        \n",
-    "        self.mlp = nn.Sequential(\n",
-    "            nn.Linear(in_features=embedding_dim,\n",
-    "                      out_features=mlp_size),\n",
-    "            nn.GELU(), # \"The MLP contains two layers with a GELU non-linearity (section 3.1).\"\n",
-    "            nn.Dropout(p=dropout),\n",
-    "            nn.Linear(in_features=mlp_size, # needs to take same in_features as out_features of layer above\n",
-    "                      out_features=embedding_dim), # take back to embedding_dim\n",
-    "            nn.Dropout(p=dropout)\n",
-    "        )\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.layer_norm(x)\n",
-    "        x = self.mlp(x)\n",
-    "        return x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "442fb987",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 197, 768])"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mlp_block = MLPBlock(embedding_dim=768, # Table 1 \n",
-    "                     mlp_size=3072) # Table 1\n",
-    "patched_image_through_mlp_block = mlp_block(patched_image_through_msa_block)\n",
-    "patched_image_through_mlp_block.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "812ccd0a-554f-4722-9987-f03c079a269f",
-   "metadata": {},
-   "source": [
-    "## TK 7. Create the Transformer Encoder \n",
-    "\n",
-    "* Tk - what is an \"encoder\"?\n",
-    "* Tk - \"transformer block\" or \"transformer encoder\"? - line this up with the paper\n",
-    "\n",
-    "See here for pre-built transformer blocks/layers: https://pytorch.org/docs/stable/nn.html#transformer-layers \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "2c43855c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class TransformerEncoderBlock(nn.Module):\n",
-    "    \"\"\"Creates a Transformer Encoder block.\"\"\"\n",
-    "    def __init__(self,\n",
-    "                 embedding_dim=768, # From Table 1\n",
-    "                 num_heads=12, # From Table 1\n",
-    "                 mlp_size=3072, # From Table 1\n",
-    "                 mlp_dropout=0.1,\n",
-    "                 attn_dropout=0):\n",
-    "        super().__init__()\n",
-    "\n",
-    "        # Create MSA Block (for equation 2)\n",
-    "        self.msa_block = MultiheadSelfAttentionBlock(embedding_dim=embedding_dim,\n",
-    "                                                     num_heads=num_heads,\n",
-    "                                                     attn_dropout=attn_dropout)\n",
-    "        # Create MLP Block (for equation 3)\n",
-    "        self.mlp_block =  MLPBlock(embedding_dim=embedding_dim,\n",
-    "                                   mlp_size=mlp_size,\n",
-    "                                   dropout=mlp_dropout)\n",
-    "        \n",
-    "    def forward(self, x):\n",
-    "        x = self.msa_block(x) + x # Create skip connection\n",
-    "        x = self.mlp_block(x) + x # Create skip connection\n",
-    "        return x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "94f9041c-a0af-4fbe-8969-a2bde8637821",
-   "metadata": {},
-   "source": [
-    "## TK 8. Putting it all together to create ViT\n",
-    " \n",
-    "TK - replicate this with the TransformerEncoderLayer - https://pytorch.org/blog/a-better-transformer-for-fast-transformer-encoder-inference/\n",
-    "\n",
-    "Combine the transformer blocks and patched embedding into a ViT architecture."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "2df890d5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class ViT(nn.Module):\n",
-    "    \"\"\"Creates a Vision Transformer architecture.\"\"\"\n",
-    "    def __init__(self,\n",
-    "                 img_size=224, # From Table 3 in ViT paper\n",
-    "                 in_channels=3,\n",
-    "                 patch_size=16,\n",
-    "                 num_transformer_layers=12, # From Table 1 in ViT paper\n",
-    "                 embedding_dim=768,\n",
-    "                 mlp_size=3072,\n",
-    "                 num_heads=12,\n",
-    "                 attn_dropout=0,\n",
-    "                 mlp_dropout=0.1,\n",
-    "                 embedding_dropout=0.1,\n",
-    "                 num_classes=1000): # default for ImageNet\n",
-    "        super().__init__() # don't forget the super().__init__()!\n",
-    "    \n",
-    "        # Get image size\n",
-    "        self.img_height, self.img_width = img_size, img_size\n",
-    "        \n",
-    "        # Calculate number of patches (height * width/patch^2)\n",
-    "        self.num_patches = (self.img_height * self.img_width) // patch_size**2\n",
-    "        \n",
-    "                 \n",
-    "        # Create class embedding (needs to go at front of sequence embedding)\n",
-    "        self.class_embedding = nn.Parameter(data=torch.randn(1, 1, embedding_dim),\n",
-    "                                            requires_grad=True)\n",
-    "        \n",
-    "         # Create position embedding\n",
-    "        self.position_embedding = nn.Parameter(data=torch.randn(1, self.num_patches+1, embedding_dim),\n",
-    "                                               requires_grad=True)\n",
-    "                \n",
-    "        # Create embedding dropout\n",
-    "        self.embedding_dropout = nn.Dropout(p=embedding_dropout)\n",
-    "        \n",
-    "        # Create patch embedding layer\n",
-    "        self.patch_embedding = PatchEmbedding(in_channels=in_channels,\n",
-    "                                              patch_size=patch_size,\n",
-    "                                              embedding_dim=embedding_dim)\n",
-    "        \n",
-    "        # Create transformer encoder blocks\n",
-    "        self.transformer_enedoder = nn.Sequential(*[TransformerEncoderBlock(embedding_dim=embedding_dim,\n",
-    "                                                                            num_heads=num_heads,\n",
-    "                                                                            mlp_size=mlp_size,\n",
-    "                                                                            mlp_dropout=mlp_dropout) for _ in range(num_transformer_layers)])\n",
-    "       \n",
-    "        # Create classifier head (equation 4)\n",
-    "        self.classifier = nn.Sequential(\n",
-    "            nn.LayerNorm(normalized_shape=embedding_dim),\n",
-    "            nn.Linear(in_features=embedding_dim, \n",
-    "                      out_features=num_classes)\n",
-    "        )\n",
-    "    \n",
-    "    def forward(self, x):\n",
-    "        # Get batch size\n",
-    "        batch_size = x.shape[0]\n",
-    "        # Create class token embedding\n",
-    "        class_token = self.class_embedding.expand(batch_size, -1, -1)\n",
-    "\n",
-    "        # Create patch embedding\n",
-    "        x = self.patch_embedding(x)\n",
-    "\n",
-    "        # Concat class embedding and patch embedding (equation 1)\n",
-    "        x = torch.cat((class_token, x), dim=1)\n",
-    "\n",
-    "        # Add position embedding to patch embedding (equation 1) for every batch\n",
-    "        x = self.position_embedding + x\n",
-    "\n",
-    "        # Run embedding dropout\n",
-    "        x = self.embedding_dropout(x)\n",
-    "\n",
-    "        # Pass patch, position and class embedding through transformer encoder layers (equations 2 & 3)\n",
-    "        x = self.transformer_enedoder(x)\n",
-    "\n",
-    "        # Put 0 index logit through classifier (equation 4)\n",
-    "        x = self.classifier(x[:, 0]) # run on each sample in a batch at 0 index\n",
-    "\n",
-    "        return x\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "7dc9f8ec",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([32, 1, 768])"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch_size = 32\n",
-    "class_tokens = nn.Parameter(data=torch.randn(1, 1, 768))\n",
-    "class_tokens.expand(batch_size, -1, -1).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "f86190a3-ed1f-4ab3-881c-4eecea996912",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-0.2377,  0.7360,  1.2137]], grad_fn=<AddmmBackward0>)"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "set_seeds()\n",
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "rand_image = torch.randn(1, 3, 224, 224)\n",
-    "# vit = ViT(num_classes=len(class_names)) \n",
-    "vit = ViT(num_classes=3)\n",
-    "vit(rand_image)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2c0a0c9c-6d98-47fd-a152-8a06be99b5fa",
-   "metadata": {},
-   "source": [
-    "## TK 9. Inspect the model\n",
-    "\n",
-    "> **Note:** If you go too big, your hardware might not be able to handle it... (e.g. too high of a batch size...)\n",
-    "\n",
-    "TK - Number of parameters should be equivalent to: https://pytorch.org/vision/main/models/generated/torchvision.models.vit_b_16.html#torchvision.models.vit_b_16 (`num_params=86,567,656`)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "494bde26-ed1e-45dc-b615-78ac268ca20e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "======================================================================================================================================================\n",
-       "Layer (type (var_name))                                                Input Shape          Output Shape         Param #              Trainable\n",
-       "======================================================================================================================================================\n",
-       "ViT (ViT)                                                              [128, 3, 224, 224]   [128, 3]             152,064              True\n",
-       "├─Dropout (embedding_dropout)                                          [128, 197, 768]      [128, 197, 768]      --                   --\n",
-       "├─PatchEmbedding (patch_embedding)                                     [128, 3, 224, 224]   [128, 196, 768]      --                   True\n",
-       "│    └─Conv2d (patcher)                                                [128, 3, 224, 224]   [128, 768, 14, 14]   590,592              True\n",
-       "│    └─Flatten (flatten)                                               [128, 768, 14, 14]   [128, 768, 196]      --                   --\n",
-       "├─Dropout (embedding_dropout)                                          [128, 197, 768]      [128, 197, 768]      --                   --\n",
-       "├─Sequential (transformer_enedoder)                                    [128, 197, 768]      [128, 197, 768]      --                   True\n",
-       "│    └─TransformerEncoderBlock (0)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
-       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
-       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
-       "│    └─TransformerEncoderBlock (1)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
-       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
-       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
-       "│    └─TransformerEncoderBlock (2)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
-       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
-       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
-       "│    └─TransformerEncoderBlock (3)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
-       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
-       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
-       "│    └─TransformerEncoderBlock (4)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
-       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
-       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
-       "│    └─TransformerEncoderBlock (5)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
-       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
-       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
-       "│    └─TransformerEncoderBlock (6)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
-       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
-       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
-       "│    └─TransformerEncoderBlock (7)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
-       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
-       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
-       "│    └─TransformerEncoderBlock (8)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
-       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
-       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
-       "│    └─TransformerEncoderBlock (9)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
-       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
-       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
-       "│    └─TransformerEncoderBlock (10)                                    [128, 197, 768]      [128, 197, 768]      --                   True\n",
-       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
-       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
-       "│    └─TransformerEncoderBlock (11)                                    [128, 197, 768]      [128, 197, 768]      --                   True\n",
-       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
-       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
-       "├─Sequential (classifier)                                              [128, 768]           [128, 3]             --                   True\n",
-       "│    └─LayerNorm (0)                                                   [128, 768]           [128, 768]           1,536                True\n",
-       "│    └─Linear (1)                                                      [128, 768]           [128, 3]             2,307                True\n",
-       "======================================================================================================================================================\n",
-       "Total params: 85,800,963\n",
-       "Trainable params: 85,800,963\n",
-       "Non-trainable params: 0\n",
-       "Total mult-adds (G): 22.08\n",
-       "======================================================================================================================================================\n",
-       "Input size (MB): 77.07\n",
-       "Forward/backward pass size (MB): 13168.81\n",
-       "Params size (MB): 257.55\n",
-       "Estimated Total Size (MB): 13503.43\n",
-       "======================================================================================================================================================"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from torchinfo import summary\n",
-    "\n",
-    "# TK - clean up the summary so it looks nice when it prints out \n",
-    "# Print a summary using torchinfo (uncomment for actual output)\n",
-    "summary(model=vit, \n",
-    "        input_size=(128, 3, 224, 224), # make sure this is \"input_size\", not \"input_shape\"\n",
-    "        # col_names=[\"input_size\"], # uncomment for smaller output\n",
-    "        col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
-    "        col_width=20,\n",
-    "        row_settings=[\"var_names\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d0279251-5cc1-42a5-bebc-8391ef911343",
-   "metadata": {},
-   "source": [
-    "* TK - same number of parameters as: https://pytorch.org/vision/main/models/generated/torchvision.models.vit_b_16.html#torchvision.models.vit_b_16 -> 86567656"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "3791f963-7d51-418b-b8bd-4c77341560e5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(torch.Size([1, 1, 768]), torch.Size([32, 1, 768]))"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch_size = 32\n",
-    "cls_embedding = nn.Parameter(torch.randn(1, 1, 768))\n",
-    "# See here: https://pytorch.org/docs/stable/generated/torch.Tensor.expand.html\n",
-    "cls_embedding.shape, cls_embedding.expand(batch_size, -1, -1).shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dd92b752-22f0-4a13-95ac-e085d20013ac",
-   "metadata": {},
-   "source": [
-    "## TK 10. Train model\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "9107b068-f253-4026-ad21-83be41404043",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0632771598bb43e7a49ed5ffa70c4490",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/10 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 1 | train_loss: 4.8759 | train_acc: 0.2891 | test_loss: 1.0465 | test_acc: 0.5417\n",
-      "Epoch: 2 | train_loss: 1.5900 | train_acc: 0.2617 | test_loss: 1.5876 | test_acc: 0.1979\n",
-      "Epoch: 3 | train_loss: 1.4644 | train_acc: 0.2617 | test_loss: 1.2738 | test_acc: 0.1979\n",
-      "Epoch: 4 | train_loss: 1.3159 | train_acc: 0.2773 | test_loss: 1.7498 | test_acc: 0.1979\n",
-      "Epoch: 5 | train_loss: 1.3114 | train_acc: 0.3008 | test_loss: 1.7444 | test_acc: 0.2604\n",
-      "Epoch: 6 | train_loss: 1.2445 | train_acc: 0.3008 | test_loss: 1.9704 | test_acc: 0.1979\n",
-      "Epoch: 7 | train_loss: 1.2050 | train_acc: 0.3984 | test_loss: 3.5480 | test_acc: 0.1979\n",
-      "Epoch: 8 | train_loss: 1.4368 | train_acc: 0.4258 | test_loss: 1.8324 | test_acc: 0.2604\n",
-      "Epoch: 9 | train_loss: 1.5757 | train_acc: 0.2344 | test_loss: 1.2848 | test_acc: 0.5417\n",
-      "Epoch: 10 | train_loss: 1.4658 | train_acc: 0.4023 | test_loss: 1.2389 | test_acc: 0.2604\n"
-     ]
-    }
-   ],
-   "source": [
-    "from going_modular.going_modular import engine\n",
-    "\n",
-    "optimizer = torch.optim.Adam(params=vit.parameters(), \n",
-    "                             lr=1e-3,\n",
-    "                             betas=(0.9, 0.999), # default\n",
-    "                             weight_decay=0.1) # from the ViT paper section 4.1\n",
-    "loss_fn = torch.nn.CrossEntropyLoss()\n",
-    "\n",
-    "set_seeds()\n",
-    "results = engine.train(model=vit,\n",
-    "                       train_dataloader=train_dataloader,\n",
-    "                       test_dataloader=test_dataloader,\n",
-    "                       optimizer=optimizer,\n",
-    "                       loss_fn=loss_fn,\n",
-    "                       epochs=10,\n",
-    "                       device=device)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "32cb3ad2-6eea-4b69-9ce8-322270643919",
-   "metadata": {},
-   "source": [
-    "## TK 11. Evaluate model\n",
-    "\n",
-    "TK - plot the loss curves"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "fcca1148-6475-4012-bfc9-cbad2706c22d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1080x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from helper_functions import plot_loss_curves\n",
-    "\n",
-    "plot_loss_curves(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c370cae-9854-474c-be05-51dabe62c204",
-   "metadata": {},
-   "source": [
-    "TK - why do the loss curves look the way they do? (too big of a model, not enough data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c9bc3d75-028d-4d48-bcfb-d0dd4f53d2ca",
-   "metadata": {},
-   "source": [
-    "## TK 12. Bring in pretrained ViT from `torchvision.models` on same dataset \n",
-    "\n",
-    "* Get a similar model from here - https://pytorch.org/vision/main/models/generated/torchvision.models.vit_b_16.html#torchvision.models.vit_b_16 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "30de8333-74b0-49ae-a81e-0266e6325f26",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.12.0+cu102\n",
-      "0.13.0+cu102\n"
-     ]
-    }
-   ],
-   "source": [
-    "# The following requires torch v0.12+ and torchvision v0.13+\n",
-    "import torch\n",
-    "import torchvision\n",
-    "print(torch.__version__) \n",
-    "print(torchvision.__version__)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "b0b87f68-98cc-49f8-89bd-ff220a757f76",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'cuda'"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "device"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "29887400-2f92-4c75-961e-0541bea6b73a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set seeds\n",
-    "def set_seeds(seed=42):\n",
-    "    torch.manual_seed(seed)\n",
-    "    torch.cuda.manual_seed(seed)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "b8e2dda6-8af0-4255-815f-4d885fa4b477",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Requires torchvision >= 0.13\n",
-    "pretrained_vit_weights = torchvision.models.ViT_B_16_Weights.DEFAULT\n",
-    "pretrained_vit = torchvision.models.vit_b_16(weights=pretrained_vit_weights).to(device)\n",
-    "\n",
-    "# Freeze the base parameters\n",
-    "for parameter in pretrained_vit.parameters():\n",
-    "    parameter.requires_grad = False\n",
-    "    \n",
-    "# Change the classifier head\n",
-    "set_seeds()\n",
-    "pretrained_vit.heads = nn.Linear(in_features=768, out_features=len(class_names)).to(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "8fbd83a1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "======================================================================================================================================================\n",
-       "Layer (type (var_name))                                                Input Shape          Output Shape         Param #              Trainable\n",
-       "======================================================================================================================================================\n",
-       "VisionTransformer (VisionTransformer)                                  [128, 3, 224, 224]   [128, 3]             768                  Partial\n",
-       "├─Conv2d (conv_proj)                                                   [128, 3, 224, 224]   [128, 768, 14, 14]   (590,592)            False\n",
-       "├─Encoder (encoder)                                                    [128, 197, 768]      [128, 197, 768]      151,296              False\n",
-       "│    └─Dropout (dropout)                                               [128, 197, 768]      [128, 197, 768]      --                   --\n",
-       "│    └─Sequential (layers)                                             [128, 197, 768]      [128, 197, 768]      --                   False\n",
-       "│    │    └─EncoderBlock (encoder_layer_0)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
-       "│    │    └─EncoderBlock (encoder_layer_1)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
-       "│    │    └─EncoderBlock (encoder_layer_2)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
-       "│    │    └─EncoderBlock (encoder_layer_3)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
-       "│    │    └─EncoderBlock (encoder_layer_4)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
-       "│    │    └─EncoderBlock (encoder_layer_5)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
-       "│    │    └─EncoderBlock (encoder_layer_6)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
-       "│    │    └─EncoderBlock (encoder_layer_7)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
-       "│    │    └─EncoderBlock (encoder_layer_8)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
-       "│    │    └─EncoderBlock (encoder_layer_9)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
-       "│    │    └─EncoderBlock (encoder_layer_10)                            [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
-       "│    │    └─EncoderBlock (encoder_layer_11)                            [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
-       "│    └─LayerNorm (ln)                                                  [128, 197, 768]      [128, 197, 768]      (1,536)              False\n",
-       "├─Linear (heads)                                                       [128, 768]           [128, 3]             2,307                True\n",
-       "======================================================================================================================================================\n",
-       "Total params: 85,800,963\n",
-       "Trainable params: 2,307\n",
-       "Non-trainable params: 85,798,656\n",
-       "Total mult-adds (G): 22.08\n",
-       "======================================================================================================================================================\n",
-       "Input size (MB): 77.07\n",
-       "Forward/backward pass size (MB): 13322.95\n",
-       "Params size (MB): 257.55\n",
-       "Estimated Total Size (MB): 13657.57\n",
-       "======================================================================================================================================================"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Print a summary using torchinfo (uncomment for actual output)\n",
-    "summary(model=pretrained_vit, \n",
-    "        input_size=(128, 3, 224, 224), # make sure this is \"input_size\", not \"input_shape\"\n",
-    "        # col_names=[\"input_size\"], # uncomment for smaller output\n",
-    "        col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
-    "        col_width=20,\n",
-    "        row_settings=[\"var_names\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "2e4ed730-de72-415f-99d1-5dffd57e9dec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TK - the above output has the same number of parameters as our own created model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "94cb3900",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INFO] data/pizza_steak_sushi directory exists, skipping download.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "PosixPath('data/pizza_steak_sushi')"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Download pizza, steak, sushi images from GitHub\n",
-    "image_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\",\n",
-    "                           destination=\"pizza_steak_sushi\")\n",
-    "image_path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "2e6ae0fe-73c0-4930-988a-e4df903084b6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(PosixPath('data/pizza_steak_sushi/train'),\n",
-       " PosixPath('data/pizza_steak_sushi/test'))"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_dir = image_path / \"train\"\n",
-    "test_dir = image_path / \"test\" \n",
-    "train_dir, test_dir"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "dd2f58ff-6182-453a-a802-70ff98c09557",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ImageClassification(\n",
-      "    crop_size=[224]\n",
-      "    resize_size=[256]\n",
-      "    mean=[0.485, 0.456, 0.406]\n",
-      "    std=[0.229, 0.224, 0.225]\n",
-      "    interpolation=InterpolationMode.BILINEAR\n",
-      ")\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Create dataset for pretrained ViT\n",
-    "pretrained_vit_transforms = pretrained_vit_weights.transforms()\n",
-    "print(pretrained_vit_transforms)\n",
-    "\n",
-    "train_dataloader_pretrained, test_dataloader_pretrained, class_names = data_setup.create_dataloaders(train_dir=train_dir,\n",
-    "                                                                                                     test_dir=test_dir,\n",
-    "                                                                                                     transform=pretrained_vit_transforms,\n",
-    "                                                                                                     batch_size=1024) # From here: https://arxiv.org/abs/2205.01580 (there are other improvements there too...)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "a49408b4-24d9-4bb1-90a2-dd61c08f78a4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7904829b8c1646d38bb6033a5b4367c4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/10 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 1 | train_loss: 1.1490 | train_acc: 0.2356 | test_loss: 1.0584 | test_acc: 0.4667\n",
-      "Epoch: 2 | train_loss: 1.0017 | train_acc: 0.5289 | test_loss: 0.9194 | test_acc: 0.6400\n",
-      "Epoch: 3 | train_loss: 0.8716 | train_acc: 0.7244 | test_loss: 0.7983 | test_acc: 0.6667\n",
-      "Epoch: 4 | train_loss: 0.7583 | train_acc: 0.8089 | test_loss: 0.6942 | test_acc: 0.7733\n",
-      "Epoch: 5 | train_loss: 0.6608 | train_acc: 0.8622 | test_loss: 0.6060 | test_acc: 0.8800\n",
-      "Epoch: 6 | train_loss: 0.5777 | train_acc: 0.8889 | test_loss: 0.5318 | test_acc: 0.8933\n",
-      "Epoch: 7 | train_loss: 0.5076 | train_acc: 0.9156 | test_loss: 0.4700 | test_acc: 0.9067\n",
-      "Epoch: 8 | train_loss: 0.4487 | train_acc: 0.9244 | test_loss: 0.4188 | test_acc: 0.9333\n",
-      "Epoch: 9 | train_loss: 0.3993 | train_acc: 0.9378 | test_loss: 0.3765 | test_acc: 0.9333\n",
-      "Epoch: 10 | train_loss: 0.3580 | train_acc: 0.9378 | test_loss: 0.3417 | test_acc: 0.9467\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Train pretrained feature extractor ViT for 5 epochs on Pizza, Steak, Sushi\n",
-    "# TK - can probably increase the batch_size here because we're using feature extraction and not \n",
-    "# training the whole model\n",
-    "from going_modular.going_modular import engine\n",
-    "\n",
-    "optimizer = torch.optim.Adam(params=pretrained_vit.parameters(), \n",
-    "                             lr=1e-3)\n",
-    "loss_fn = torch.nn.CrossEntropyLoss()\n",
-    "\n",
-    "set_seeds()\n",
-    "pretrained_vit_results = engine.train(model=pretrained_vit,\n",
-    "                                      train_dataloader=train_dataloader_pretrained,\n",
-    "                                      test_dataloader=test_dataloader_pretrained,\n",
-    "                                      optimizer=optimizer,\n",
-    "                                      loss_fn=loss_fn,\n",
-    "                                      epochs=10,\n",
-    "                                      device=device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "3c0af18e-6419-4dd6-b8ea-f5830bbd63d5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1080x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the loss curves\n",
-    "from helper_functions import plot_loss_curves\n",
-    "\n",
-    "plot_loss_curves(pretrained_vit_results) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "0fd00943-01aa-4ef4-b366-3cb859a25b6f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INFO] Saving model to: models/08_pretrained_vit_feature_extractor_pizza_steak_sushi.pth\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Save the model\n",
-    "from going_modular.going_modular import utils\n",
-    "\n",
-    "utils.save_model(model=pretrained_vit,\n",
-    "                 target_dir=\"models\",\n",
-    "                 model_name=\"08_pretrained_vit_feature_extractor_pizza_steak_sushi.pth\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "f52ef12c-b88e-4796-84eb-981491a84334",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pretrained ViT feature extractor model size: 327 MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pathlib import Path\n",
-    "\n",
-    "# Get the model size in bytes then convert to megabytes\n",
-    "pretrained_vit_model_size = Path(\"models/08_pretrained_vit_feature_extractor_pizza_steak_sushi.pth\").stat().st_size // (1024*1024)\n",
-    "print(f\"Pretrained ViT feature extractor model size: {pretrained_vit_model_size} MB\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "277a08e0-bc0e-453d-bf0f-b56164ae71ab",
-   "metadata": {},
-   "source": [
-    "## TK - Things this replication misses out on\n",
-    "\n",
-    "TK Put down the difference in the paper vs this replication\n",
-    "* Many of these things are in Table 3:\n",
-    "    * training data (ImageNet from scratch vs FoodVision Mini data) \n",
-    "    * LR warmup\n",
-    "    * LR decay\n",
-    "    * Weight decay\n",
-    "    * Number of epochs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "04b1569b-117e-43fd-9e0b-324157cb82a4",
-   "metadata": {},
-   "source": [
-    "## TK - Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dd69be46-cb68-4391-9834-8f87d8814722",
-   "metadata": {},
-   "source": [
-    "## TK - Extra-curriculum\n",
-    "\n",
-    "* layernorm\n",
-    "* See the illustrated transformer for an overview of the Transformer model:  https://jalammar.github.io/illustrated-transformer/ + https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/\n",
-    "* Attention is all you need paper - Yannic video\n",
-    "* Vision transformer - yannic video"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3ecc8ddc-fd70-4e4b-b888-430f01de73c9",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.8.13"
-  },
-  "vscode": {
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e7e81227-aa0c-4e15-9ac4-20cc7128c915",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/mrdbourke/pytorch-deep-learning/blob/main/08_pytorch_paper_replicating.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "873828f0-e50f-40b9-9879-f9a01adaa020",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# (WIP) 08. PyTorch Paper Replicating\n",
+    "\n",
+    "TK intro\n",
+    "\n",
+    "Want to recreate ViT paper: \"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale\" - https://arxiv.org/abs/2010.11929 - TK will refer to this as \"ViT paper\" throughout.\n",
+    "\n",
+    "* TK what is ViT?\n",
+    "\n",
+    "* TK - The name Transformer comes from the architecture name in the paper where it was originally introduced, [*Attention is all you need*](https://arxiv.org/abs/1706.03762). An architecture is usually considered a Transformer variant if it uses attention layers in a specific pattern. Since the Transformer architecture originally focused on text data, the goal of the ViT paper was to bring it to the vision.\n",
+    "\n",
+    "* TK - The original transformer was made to work on sequences of text (1D), Vision Transformer turns images into sequences of \"patches\".\n",
+    "\n",
+    "* TK - original ViT also called \"vanilla vision transformer\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccb53b99-277c-4ac0-a1fb-283c47576be1",
+   "metadata": {},
+   "source": [
+    "## TK - What is paper replicating?\n",
+    "\n",
+    "It's no secret machine learning is advancing fast.\n",
+    "\n",
+    "Many of these advances get published in machine learning research papers.\n",
+    "\n",
+    "And the goal of **paper replicating** is to take replicate these advances with code so you can use the techniques for your own problem.\n",
+    "\n",
+    "For example, let's say a new model architecture gets released that performs better than any other architecture before on various benchmarks, wouldn't it be nice to try that architecture on your own problems?\n",
+    "\n",
+    "* TK image: paper replicating = research paper (language + diagrams + math) -> code (turn language, diagrams and math into usable code) / (translate a research paper into usable code)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "966b353c-c9d8-4568-ad64-c0df45a39442",
+   "metadata": {},
+   "source": [
+    "## TK - What is a machine learning research paper?\n",
+    "\n",
+    "A machine learning research paper is a scientific paper that details findings of a research group on a specific area.\n",
+    "\n",
+    "The contents of a machine learning research paper can vary from paper to paper but they generally follow the structure:\n",
+    "\n",
+    "| **Section** | **Contents** |\n",
+    "| ----- | ----- | \n",
+    "| **Abstract** | An overview/summary of the paper's main findings/contributions. |\n",
+    "| **Introduction** | What's the paper's main problem and what are previous methods used to try and solve it? |\n",
+    "| **Method** | How did the researchers go about conducting their research? For example, what model(s) were used, data sources, training setups, etc. |\n",
+    "| **Results** | What are the outcomes of the paper? If a new type of model or training setup was used, how did the results of findings compare to previous works (this is where **experiment tracking** comes in handy)? |\n",
+    "| **Conclusion** | What are the limitations of the suggested methods? What are some next steps for the research community? |\n",
+    "| **References** | What resources/other papers did the researchers look at to build their own body of work? |\n",
+    "| **Appendix** | Are there any extra resources/findings to look at that weren't included in any of the above sections? |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8ce67f6-0b00-448b-885d-b7d22bce4ff6",
+   "metadata": {},
+   "source": [
+    "## TK - Why replicate a machine learning research paper?\n",
+    "\n",
+    "A machine learning research paper is often a presentation of months of work and experiments done by some of the best machine learning teams in the world condensed into a few pages of text.\n",
+    "\n",
+    "And if these experiments lead to better results in an area related to the problem you're working on, it'd be nice to them out.\n",
+    "\n",
+    "Also, replicating the work of others is a fantastic way to practice your skills.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-george-hotz-quote.png\" width=600 alt=\"george hotz quote saying to get better at being a machine learning engineer, download a paper, implement it and keep going until you have skills\"/>\n",
+    "\n",
+    "*George Hotz is founder of [comma.ai](https://comma.ai/), a self-driving car company and livestreams machine learning coding on [Twitch](https://www.twitch.tv/georgehotz) and those videos get posted in full to [YouTube](https://www.youtube.com/c/georgehotzarchive). I pulled this quote from one of his livestreams. The \"٭\" is to note that machine learning engineering often involves the extra step(s) of preprocessing data and making your models available for others to use (deployment).*\n",
+    "    \n",
+    "When you first start trying to replicate research papers, you'll likely be overwhelmed.\n",
+    "\n",
+    "That's normal.\n",
+    "\n",
+    "Research teams spend weeks, months and sometimes years creating these works so it makes sense if it takes you sometime to even read let alone reproduce the works.\n",
+    "\n",
+    "Replicating research is such a tough problem, phenomenal machine learning libraries and tools such as, [HuggingFace](https://huggingface.co/), [PyTorch Image Models](https://github.com/rwightman/pytorch-image-models) (`timm` library) and [fast.ai](https://www.fast.ai/) have been born out of making machine learning research more accessible. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b09a7ccf-41e8-4ee4-8d78-aff5f650ca7f",
+   "metadata": {},
+   "source": [
+    "## TK - Where can you find code examples for machine learning research papers?\n",
+    "\n",
+    "One of the first things you'll notice when it comes to machine learning research is: there's a lot of it.\n",
+    "\n",
+    "So beware, trying to stay on top of it is like trying to outrun a hamster wheel.\n",
+    "\n",
+    "Follow your interest, pick a few things that stand out to you.\n",
+    "\n",
+    "In saying this, there are several places to find and read machine learning research papers:\n",
+    "* [arXiv](https://arxiv.org/) - Pronounced \"archive\", arXiv is a free and open resource for reading technical articles on everything from physics to computer science (inlcuding machine learning).\n",
+    "* [Papers with Code](https://paperswithcode.com/) - A curated collection of trending, active and greatest machine learning papers, many of which include code resources attached. Also includes a collection of common machine learning datasets, benchmarks and current state-of-the-art models.\n",
+    "* [AK Twitter](https://twitter.com/ak92501) - The AK Twitter account publishes machine learning research highlights, often with live demos almost every day. I don't understand 9/10 posts but I find it fun to explore every so often.\n",
+    "* [lucidrains' `vit-pytorch` GitHub repository](https://github.com/lucidrains/vit-pytorch) - Less of a place to find research papers and more of an example of what paper replicating with code on a larger-scale looks like. The `vit-pytorch` repository is a collection of Vision Transformer model architectures from various research papers replicated with PyTorch code (much of the inspiration for this notebook was gathered from this repository). \n",
+    "\n",
+    "TK image: showcase the above"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7448de48-ec72-4d63-9948-869a4fc58c04",
+   "metadata": {},
+   "source": [
+    "## TK - What we're going to cover\n",
+    "\n",
+    "TODO\n",
+    "\n",
+    "* ViT -> FoodVision Mini\n",
+    "* Layers = collections of functions to manipulate data -> Architectures = collections of layers (blocks) -> All layers (and blocks) have inputs and outputs\n",
+    "    * Replicating research papers starts by figuring out the inputs and outputs of your layers -> blocks -> model "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf677bb7-719a-447e-a8e8-c4f287146b62",
+   "metadata": {},
+   "source": [
+    "## TK - Where can you get help?\n",
+    "\n",
+    "All of the materials for this course [are available on GitHub](https://github.com/mrdbourke/pytorch-deep-learning).\n",
+    "\n",
+    "If you run into trouble, you can ask a question on the course [GitHub Discussions page](https://github.com/mrdbourke/pytorch-deep-learning/discussions).\n",
+    "\n",
+    "And of course, there's the [PyTorch documentation](https://pytorch.org/docs/stable/index.html) and [PyTorch developer forums](https://discuss.pytorch.org/), a very helpful place for all things PyTorch. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a8913de-e49e-40c9-89c7-6b847fac9def",
+   "metadata": {},
+   "source": [
+    "## TK 0. Getting setup \n",
+    "\n",
+    "As we've done previously, let's make sure we've got all of the modules we'll need for this section.\n",
+    "\n",
+    "We'll import the Python scripts (such as `data_setup.py` and `engine.py`) we created in [05. PyTorch Going Modular](https://www.learnpytorch.io/05_pytorch_going_modular/).\n",
+    "\n",
+    "To do so, we'll download [`going_modular`](https://github.com/mrdbourke/pytorch-deep-learning/tree/main/going_modular) directory from the `pytorch-deep-learning` repository (if we don't already have it).\n",
+    "\n",
+    "We'll also get the [`torchinfo`](https://github.com/TylerYep/torchinfo) package if it's not available. \n",
+    "\n",
+    "`torchinfo` will help later on to give us a visual representation of our model.\n",
+    "\n",
+    "And since later on we'll be using a newer version of the `torchvision` package (as of June 2022), we'll make sure we've got the latest versions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ebe46d77-6c4d-4102-9994-2cb89f633f18",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch version: 1.12.0+cu102\n",
+      "torchvision version: 0.13.0+cu102\n"
+     ]
+    }
+   ],
+   "source": [
+    "# For this notebook to run with updated APIs, we need torch 1.12+ and torchvision 0.13+\n",
+    "try:\n",
+    "    import torch\n",
+    "    import torchvision\n",
+    "    assert int(torch.__version__.split(\".\")[1]) >= 12, \"torch version should be 1.12+\"\n",
+    "    assert int(torchvision.__version__.split(\".\")[1]) >= 13, \"torchvision version should be 0.13+\"\n",
+    "    print(f\"torch version: {torch.__version__}\")\n",
+    "    print(f\"torchvision version: {torchvision.__version__}\")\n",
+    "except:\n",
+    "    print(f\"[INFO] torch/torchvision versions not as required, installing nightly versions.\")\n",
+    "    !pip3 install -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu113\n",
+    "    import torch\n",
+    "    import torchvision\n",
+    "    print(f\"torch version: {torch.__version__}\")\n",
+    "    print(f\"torchvision version: {torchvision.__version__}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30caf875-557e-410f-8dff-bd4a9f6c7ae4",
+   "metadata": {},
+   "source": [
+    "> **Note:** If you're using Google Colab, you may have to restart your runtime after running the above cell. After restarting, you can run the cell again and verify you've got the right versions of `torch` and `torchvision`.\n",
+    "\n",
+    "Now we'll continue with the regular imports, setting up device agnostic code and this time we'll also get the [`helper_functions.py`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/helper_functions.py) script from GitHub.\n",
+    "\n",
+    "The `helper_functions.py` script contains several functions we created in previous sections:\n",
+    "* `set_seeds()` to set the random seeds (created in [07. PyTorch Experiment Tracking section 0](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#create-a-helper-function-to-set-seeds)).\n",
+    "* `download_data()` to download a data source given a link (created in [07. PyTorch Experiment Tracking section 1](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#1-get-data)).\n",
+    "* `plot_loss_curves()` to inspect our model's training results (created in [04. PyTorch Custom Datasets section 7.8](https://www.learnpytorch.io/04_pytorch_custom_datasets/#78-plot-the-loss-curves-of-model-0))\n",
+    "\n",
+    "> **Note:** It may be a better idea for many of the functions in the `helper_functions.py` script to be merged into `going_modular/going_modular/utils.py`, perhaps that's an extension you'd like to try.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "960eb156-c1b1-4e76-a812-01bf045835bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Continue with regular imports\n",
+    "import matplotlib.pyplot as plt\n",
+    "import torch\n",
+    "import torchvision\n",
+    "\n",
+    "from torch import nn\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "# Try to get torchinfo, install it if it doesn't work\n",
+    "try:\n",
+    "    from torchinfo import summary\n",
+    "except:\n",
+    "    print(\"[INFO] Couldn't find torchinfo... installing it.\")\n",
+    "    !pip install -q torchinfo\n",
+    "    from torchinfo import summary\n",
+    "\n",
+    "# Try to import the going_modular directory, download it from GitHub if it doesn't work\n",
+    "try:\n",
+    "    from going_modular.going_modular import data_setup, engine\n",
+    "    from helper_functions import download_data, set_seeds, plot_loss_curves\n",
+    "except:\n",
+    "    # Get the going_modular scripts\n",
+    "    print(\"[INFO] Couldn't find going_modular or helper_functions scripts... downloading them from GitHub.\")\n",
+    "    !git clone https://github.com/mrdbourke/pytorch-deep-learning\n",
+    "    !mv pytorch-deep-learning/going_modular .\n",
+    "    !mv pytorch-deep-learning/helper_functions.py . # get the helper_functions.py script\n",
+    "    !rm -rf pytorch-deep-learning\n",
+    "    from going_modular.going_modular import data_setup, engine\n",
+    "    from helper_functions import download_data, set_seeds, plot_loss_curves"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f9bdd26-26ac-4756-bd8e-b7a50799f28b",
+   "metadata": {},
+   "source": [
+    "> **Note:** If you're using Google Colab, and you don't have a GPU turned on yet, it's now time to turn one on via `Runtime -> Change runtime type -> Hardware accelerator -> GPU`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5e246f92-e509-474e-b6c7-c82cf11cb8ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'cuda'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "device"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a695192-2644-4aa7-beab-7222a24b1a1a",
+   "metadata": {},
+   "source": [
+    "## TK 1. Get Data\n",
+    "\n",
+    "Since we're continuing on with FoodVision Mini, let's download the pizza, steak and sushi image dataset we've been using.\n",
+    "\n",
+    "To do so we can use the `download_data()` function from `helper_functions.py` that we created in [07. PyTorch Experiment Tracking section 1](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#1-get-data).\n",
+    "\n",
+    "We'll `source` to the raw GitHub link of the [`pizza_steak_sushi.zip` data](https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip) and the `destination` to `pizza_steak_sushi`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "37b5ffc0-7093-481e-8081-dbdfac4c24f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[INFO] data/pizza_steak_sushi directory exists, skipping download.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('data/pizza_steak_sushi')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Download pizza, steak, sushi images from GitHub\n",
+    "image_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\",\n",
+    "                           destination=\"pizza_steak_sushi\")\n",
+    "image_path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55a047b1-9f12-4dcf-8d97-83b7cbb37392",
+   "metadata": {},
+   "source": [
+    "Beautiful! Data downloaded, let's setup the training and test directories."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "92a426b6-df22-4a58-9d5e-b382c73c6048",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup directory paths to train and test images\n",
+    "train_dir = image_path / \"train\"\n",
+    "test_dir = image_path / \"test\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14e84624-6af8-4231-b606-3eabffd172db",
+   "metadata": {},
+   "source": [
+    "## TK 2. Create Datasets and DataLoaders\n",
+    "\n",
+    "Since we've got some data, let's now turn it into `DataLoader`'s.\n",
+    "\n",
+    "To do so we can use the `create_dataloaders()` function in [`data_setup.py`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/going_modular/going_modular/data_setup.py).\n",
+    "\n",
+    "First, we'll create a transform to prepare our images.\n",
+    "\n",
+    "This where one of the first references to the ViT paper will come in.\n",
+    "\n",
+    "In Table 3, the training resolution is mentioned as being 224 (height=224, width=224). \n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-image-size-and-batch-size.png\" width=900 alt=\"Table 3 from the Vision Transformer paper showing the image size and batch size\"/>\n",
+    "\n",
+    "*You can often find various hyperparameter settings listed in a table. In this case we're still preparing our data, so we're mainly concerned with things like image size and batch size. Source: Table 3 in [ViT paper](https://arxiv.org/abs/2010.11929).*\n",
+    "\n",
+    "So we'll make sure our transform resizes our images appropriately.\n",
+    "\n",
+    "And since we'll be training our model from scratch (no transfer learning to begin with), we won't provide a `normalize` transform like we did in [06. PyTorch Transfer Learning section 2.1](https://www.learnpytorch.io/06_pytorch_transfer_learning/#21-creating-a-transform-for-torchvisionmodels-manual-creation).\n",
+    "\n",
+    "### 2.1 Prepare transforms for images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a45ea650-c3fa-479c-8767-48bc3a1f1267",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Manually created transforms: Compose(\n",
+      "    Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=None)\n",
+      "    ToTensor()\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create image size (from Table 3 in the ViT paper) \n",
+    "IMG_SIZE = 224\n",
+    "\n",
+    "# Create transform pipeline manually\n",
+    "manual_transforms = transforms.Compose([\n",
+    "    transforms.Resize((IMG_SIZE, IMG_SIZE)),\n",
+    "    transforms.ToTensor(),\n",
+    "])           \n",
+    "print(f\"Manually created transforms: {manual_transforms}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "437078c2-eb42-471f-8561-94845d0a878d",
+   "metadata": {},
+   "source": [
+    "### 2.2 Turn images into `DataLoader`'s\n",
+    "Transforms created!\n",
+    "\n",
+    "Let's now create our `DataLoader`'s.\n",
+    "\n",
+    "The ViT paper states the use of a batch size of 4096 which is 128x the size of the batch size we've been using (32).\n",
+    "\n",
+    "We're going to stick with a batch size of 32.\n",
+    "\n",
+    "Why?\n",
+    "\n",
+    "Because some hardware (including the free tier of Google Colab) may not be able to handle a batch size of 4096.\n",
+    "\n",
+    "Having a batch size of 4096 means that 4096 images need to fit into the GPU memory at a time.\n",
+    "\n",
+    "This works when you've got the hardware to handle it like a research team from Google often does but when you're running on a single GPU (such as using Google Colab), making sure things work with smaller batch size first is a good idea.\n",
+    "\n",
+    "An extension of this project could be to try a higher batch size value and see what happens.\n",
+    "\n",
+    "> **Note:** We're using the `pin_memory=True` parameter in the `create_dataloaders()` function to speed up computation. `pin_memory=True` avoids unnecessary copying of memory between the CPU and GPU memory by \"pinning\" examples that have been seen before. For more on this concept. Though the benefits of this will likely be seen with larger dataset sizes (our FoodVision Mini dataset is quite small). See the PyTorch [`torch.utils.data.DataLoader` documentation](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) or [Making Deep Learning Go Brrrr from First Principles](https://horace.io/brrr_intro.html) by Horace He for more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d0ac8145-f89a-490f-82e3-d4b22225d163",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<torch.utils.data.dataloader.DataLoader at 0x7fa8cc731550>,\n",
+       " <torch.utils.data.dataloader.DataLoader at 0x7fa8cc731520>,\n",
+       " ['pizza', 'steak', 'sushi'])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Set the batch size\n",
+    "BATCH_SIZE = 32 # this is lower than the ViT paper but it's because we're starting small\n",
+    "\n",
+    "# Create data loaders\n",
+    "train_dataloader, test_dataloader, class_names = data_setup.create_dataloaders(\n",
+    "    train_dir=train_dir,\n",
+    "    test_dir=test_dir,\n",
+    "    transform=manual_transforms, # use manually created transforms\n",
+    "    batch_size=BATCH_SIZE\n",
+    ")\n",
+    "\n",
+    "train_dataloader, test_dataloader, class_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a980a5b-0c3b-440d-87f5-c54f1ab143ab",
+   "metadata": {},
+   "source": [
+    "### TK 2.3 Visualize a single image\n",
+    "\n",
+    "Now we've loaded our data, let's *visualize, visualize, visualize!*\n",
+    "\n",
+    "An important step in the ViT paper is preparing the images into patches.\n",
+    "\n",
+    "We'll get to what this means in a second but for now, let's view a single image and its label.\n",
+    "\n",
+    "To do so, let's get a single image and label from a batch of data and inspect their shapes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b5734a22-ded5-403e-84f5-d7a90ed3f085",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(torch.Size([3, 224, 224]), tensor(0))"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get a batch of images\n",
+    "image_batch, label_batch = next(iter(train_dataloader))\n",
+    "\n",
+    "# Get a single image from the batch\n",
+    "image, label = image_batch[0], label_batch[0]\n",
+    "\n",
+    "# View the batch shapes\n",
+    "image.shape, label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "898cbb6d-b433-41be-9280-41de129077df",
+   "metadata": {},
+   "source": [
+    "Wonderful!\n",
+    "\n",
+    "Now let's plot the image and its label with `matplotlib`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "afe85fae-38fd-4f34-a52c-29d02cce09c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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
+      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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot image with matplotlib\n",
+    "plt.imshow(image.permute(1, 2, 0)) # rearrange image dimensions to suit matplotlib [color_channels, height, width] -> [height, width, color_channels]\n",
+    "plt.title(class_names[label])\n",
+    "plt.axis(False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b8416fa-fb0c-4276-8405-12531ba78b71",
+   "metadata": {},
+   "source": [
+    "Nice!\n",
+    "\n",
+    "Looks like our images are importing correctly, let's continue with the paper replication."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13bfb028-1afa-44ec-89e9-8890975311ea",
+   "metadata": {},
+   "source": [
+    "## TK 3. Replicating the ViT paper: an overview\n",
+    "\n",
+    "Before we write anymore code, let's discuss what we're doing.\n",
+    "\n",
+    "We'd like to replicate the ViT paper for our own problem, FoodVision Mini.\n",
+    "\n",
+    "So our inputs are: images of pizza, steak and sushi.\n",
+    "\n",
+    "And our ideal model outputs are: predicted labels of pizza, steak or sushi.\n",
+    "\n",
+    "No different to what we've been doing throughout the previous sections.\n",
+    "\n",
+    "The question is: how do we go from our inputs to the desired outputs?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e7f0a12-cce9-45d0-a572-4ad612a98735",
+   "metadata": {},
+   "source": [
+    "### 3.1 Inputs and outputs, layers and blocks\n",
+    "\n",
+    "ViT is a deep learning neural network architecture.\n",
+    "\n",
+    "And any neural network architecture is generally comprised of **layers**.\n",
+    "\n",
+    "And a collection of layers is often referred to as a **block**.\n",
+    "\n",
+    "And stacking many blocks together is what gives us the whole architecture.\n",
+    "\n",
+    "A **layer** takes an input (say an image tensor), performs some kind of function on it (for example what's in the layer's `forward()` method) and then returns an output.\n",
+    "\n",
+    "So if a **single layer** takes an input and gives an output, then a collection of layers or a **block** also takes an input and gives an output.\n",
+    "\n",
+    "Let's make this concrete:\n",
+    "* **Layer** - takes an input, performs a function on it, returns an output.\n",
+    "* **Block** - a collection of layers, takes an input, performs a series of functions on it, returns an output.\n",
+    "* **Architecture (or model)** - a collection of blocks, takes an input, performs a series of functions on it, returns an output.\n",
+    "\n",
+    "This ideology is what we're going to be using to replicate the ViT paper.\n",
+    "\n",
+    "We're going to take it layer by layer, block by block, function by function putting the pieces of the puzzle together like Lego to get our desired overall architecture.\n",
+    "\n",
+    "The reason we do this is because looking at a whole research paper can be intimidating.\n",
+    "\n",
+    "So for a better understanding, we'll break it down, starting with the inputs and outputs of single layer and working up to the inputs and outputs of the whole model.\n",
+    "\n",
+    "TK image: stacking the network together like lego (functions + layers + blocks = model)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2852f3f-61f0-4dad-ae8c-49db54e28470",
+   "metadata": {},
+   "source": [
+    "### 3.2 Getting specific: What's ViT made of?\n",
+    "\n",
+    "There are many little details about the ViT model sprinkled throughout the paper.\n",
+    "\n",
+    "Finding them all is like one big treasure hunt!\n",
+    "\n",
+    "Remember, a research paper is often months of work compressed into a few pages so it's understandable for it to take of practice to replicate.\n",
+    "\n",
+    "However, the main three resources we'll be looking at for the architecture design are:\n",
+    "1. **Figure 1** - This gives an overview of the model in a graphical sense, you could *almost* recreate the architecture with this figure alone.\n",
+    "2. **Four equations in section 3.1** - These equations give a little bit more of a mathematical grounding to the coloured blocks in Figure 1.\n",
+    "3. **Table 1** - This table shows the various hyperparameter settings (such as number of layers and number of hidden units) for different ViT model variants. We'll be focused on the smallest version, ViT-Base."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bac7c7d2-e58b-47b1-ae3e-7ae9f922326d",
+   "metadata": {},
+   "source": [
+    "#### TK 3.2.1 Exploring Figure 1\n",
+    "\n",
+    "Let's start by going through Figure 1 of the ViT Paper.\n",
+    "\n",
+    "The main things we'll be paying attention to are:\n",
+    "1. **Layers** - takes an **input**, performs an operation or function, produces an **output**.\n",
+    "2. **Blocks** - a collection of layers, which in turn also takes an **input** and produces an **output**.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-figure-1-inputs-and-outputs.png\" width=900 alt=\"figure 1 from the original vision transformer paper\"/>\n",
+    "\n",
+    "*Figure 1 from the ViT Paper showcasing the different inputs, outputs, layers and blocks that create the architecture. Our goal will be to replicate each of these using PyTorch code.* \n",
+    "\n",
+    "The ViT architecture is comprised of several stages:\n",
+    "* **Patch + Position Embedding (inputs)** - Turns the input image into a sequence of image patches and add a position number what order the patch comes in.\n",
+    "* **Linear projection of flattened patches (Embedded Patches)** - The image patches get turned into an **embedding**, the benefit of using an embedding rather than just the image values is that an embedding is a *learnable* representation (typically in the form of a vector) of the image that can improve with training.\n",
+    "* **Norm** - This is short for \"[Layer Normalization](https://paperswithcode.com/method/layer-normalization)\" or \"LayerNorm\", a technique for regularizing (reducing overfitting) a neural network, you can use LayerNorm via the PyTorch layer [`torch.nn.LayerNorm()`](https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html).\n",
+    "* **Multi-Head Attention** - This is a [Multi-Headed Self-Attention layer](https://paperswithcode.com/method/multi-head-attention) or \"MSA\" for short. You can create an MSA layer via the PyTorch layer [`torch.nn.MultiheadAttention()`](https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html).\n",
+    "* **MLP (or [Multilayer perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron))** - A MLP can often refer to any collection of feedforward layers (or in PyTorch's case, a collection of layers with a `forward()` method). In the ViT Paper, the authors refer to the MLP as \"MLP block\" and it contains two [`torch.nn.Linear()`](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) layers with a [`torch.nn.GELU()`](https://pytorch.org/docs/stable/generated/torch.nn.GELU.html) non-linearity activation in between them (section 3.1) and a [`torch.nn.Dropout()`](https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html) layer after each (Appendex B.1). \n",
+    "* **Transformer Encoder** - The Transformer Encoder, is a collection of the layers listed above. There are two skip connections inside the Transformer encoder (the \"+\" symbols) meaning the layer's inputs are fed directly to immediate layers as well as subsequent layers. The overall ViT architecture is comprised of a number of Transformer encoders stacked on top of eachother.\n",
+    "* **MLP Head** - This is the output layer of the architecture, it converts the learned features of an input to a class output. Since we're working on image classification, you could also call this the \"classifier head\". The structure of the MLP Head is similar to the MLP block.\n",
+    "\n",
+    "You might notice that many of the pieces of the ViT architecture can be created with existing PyTorch layers.\n",
+    "\n",
+    "This is because of how PyTorch is designed, it's one of the main purposes of PyTorch to create reusable neural network layers for both researchers and machine learning practitioners.\n",
+    "\n",
+    "> **Question:** Why not code everything from scratch?\n",
+    ">\n",
+    "> You could definitely do that by reproducing all of the math equations from the paper with custom PyTorch layers and that would certainly be an educative exercise, however, using pre-existing PyTorch layers is usually favoured as pre-existing layers have often been extensively tested and performance checked to make sure they run correctly and fast. \n",
+    "\n",
+    "> **Note:** We're going to focused on write PyTorch code to create these layers, for the background on what each of these layers does, I'd suggest reading the ViT Paper in full or reading the linked resources for each layer.\n",
+    "\n",
+    "Let's take Figure 1 and adapt it to our FoodVision Mini problem of classifying images of food into pizza, steak or sushi.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-figure-1-inputs-and-outputs-food-mini.png\" width=900 alt=\"figure 1 from the original vision transformer paper adapted to work with food images, an image of pizza goes in and gets classified as 'pizza'\"/>\n",
+    "\n",
+    "*Figure 1 from the ViT Paper adapted for use with FoodVision Mini. An image of food goes in (pizza), the image gets turned into patches and then projected to an embedding. The embedding then travels through the various layers and blocks and (hopefully) the class \"pizza\" is returned.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "add31aa8-2809-4f77-b94c-e34d865c12d0",
+   "metadata": {},
+   "source": [
+    "#### TK - 3.2.2 Exploring the Four Equations\n",
+    "\n",
+    "The next main part(s) of the ViT paper we're going to look at are the four equations in section 3.1.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-four-equations.png\" width=650 alt=\"four mathematical equations from the vision transformer machine learning paper\"/>\n",
+    "\n",
+    "*These four equations represent the math behind the four major parts of the ViT architecture.*\n",
+    "\n",
+    "Section 3.1 describes each of these (some of the text has been omitted for brevity, bolded text is mine):\n",
+    "\n",
+    "| **Equation number** | **Description from ViT paper section 3.1** | \n",
+    "| ----- | ----- | \n",
+    "| 1 | ...The Transformer uses constant latent vector size $D$ through all of its layers, so we flatten the patches and map to $D$ dimensions with a **trainable linear projection** (Eq. 1). We refer to the output of this projection as the **patch embeddings**. |\n",
+    "| 2 | The Transformer encoder (Vaswani et al., 2017) consists of alternating layers of multiheaded selfattention (MSA, see Appendix A) and MLP blocks (Eq. 2, 3). **Layernorm (LN) is applied before every block**, and **residual connections after every block** (Wang et al., 2019; Baevski & Auli, 2019). |\n",
+    "| 3 | See above. |\n",
+    "| 4 | Similar to BERT's [ class ] token, we **prepend a learnable embedding to the sequence of embedded patches** $\\left(\\mathbf{z}_{0}^{0}=\\mathbf{x}_{\\text {class }}\\right)$, whose state at the output of the Transformer encoder $\\left(\\mathbf{z}_{L}^{0}\\right)$ serves as the image representation $\\mathbf{y}$ (Eq. 4)... |\n",
+    "\n",
+    "Let's map these descriptions to the ViT architecture in Figure 1.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-mapping-the-four-equations-to-figure-1.png\" width=1000 alt=\"mapping the vision transformer paper figure 1 to the four equations listed in the paper\"/>\n",
+    "\n",
+    "*Connecting Figure 1 from the ViT paper to the four equations from section 3.1 describing the math behind each of the layers/blocks. Some details such as \"residual connections after every block\" are referred to in Figure 1 and in the text but not in the equations.*\n",
+    "\n",
+    "There's a lot happening in the image above but following the coloured lines and arrows reveals the main concepts of the ViT architecture.\n",
+    "\n",
+    "How about we break down each equation further (it will be our goal to recreate these with code)?\n",
+    "\n",
+    "In all equations (except equation 4), \"$\\mathbf{z}$\" is the raw output of a particular layer:\n",
+    "\n",
+    "1. $\\mathbf{z}_{0}$ is \"z zero\" (this is the output of the initial patch embedding layer)\n",
+    "2. $\\mathbf{z}_{\\ell}^{\\prime}$ is \"z of a particular layer *prime*\" (or an intermediary value of z)\n",
+    "3. $\\mathbf{z}_{\\ell}$ is \"z of a particular layer\"\n",
+    "\n",
+    "And $\\mathbf{y}$ is the overall output of the architecture.\n",
+    "\n",
+    "**Equation 1**\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\mathbf{z}_{0} &=\\left[\\mathbf{x}_{\\text {class }} ; \\mathbf{x}_{p}^{1} \\mathbf{E} ; \\mathbf{x}_{p}^{2} \\mathbf{E} ; \\cdots ; \\mathbf{x}_{p}^{N} \\mathbf{E}\\right]+\\mathbf{E}_{\\text {pos }}, & & \\mathbf{E} \\in \\mathbb{R}^{\\left(P^{2} \\cdot C\\right) \\times D}, \\mathbf{E}_{\\text {pos }} \\in \\mathbb{R}^{(N+1) \\times D}\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "This equation deals with the class token, patch embedding and position embedding ($\\mathbf{E}$ is for embedding) of the input image.\n",
+    "\n",
+    "In vector form, the embedding might look something like:\n",
+    "\n",
+    "TK - update the vector form to reflect a real exmaple\n",
+    "\n",
+    "```python\n",
+    "x_input = [class_token, image_patch_1, image_patch_2, image_patch_3...] + [class_token_position, image_patch_1_position, image_patch_2_position, image_patch_3_position...]\n",
+    "```\n",
+    "\n",
+    "Where each of the elements in the vector is learnable (their `requires_grad=True`).\n",
+    "\n",
+    "**Equation 2**\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\mathbf{z}_{\\ell}^{\\prime} &=\\operatorname{MSA}\\left(\\operatorname{LN}\\left(\\mathbf{z}_{\\ell-1}\\right)\\right)+\\mathbf{z}_{\\ell-1}, & & \\ell=1 \\ldots L\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "This says that for every layer from $1$ through to $L$ (the total number of layers), there's a Multi-Head Attention layer (MSA) wrapping a LayerNorm layer (LN).\n",
+    "\n",
+    "The addition on the end is the equivalent of adding the input to the output and forming a [skip/residual connection](https://paperswithcode.com/method/residual-connection).\n",
+    "\n",
+    "We'll call this layer the \"MSA block\".\n",
+    "\n",
+    "In pseudocode, this might look like: \n",
+    "\n",
+    "```python\n",
+    "x_output_MSA_block = MSA_layer(LN_layer(x_input)) + x_input\n",
+    "```\n",
+    "\n",
+    "Notice the skip connection on the end (adding the input of the layers to the output of the layers).\n",
+    "\n",
+    "**Equation 3**\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\mathbf{z}_{\\ell} &=\\operatorname{MLP}\\left(\\operatorname{LN}\\left(\\mathbf{z}_{\\ell}^{\\prime}\\right)\\right)+\\mathbf{z}_{\\ell}^{\\prime}, & & \\ell=1 \\ldots L \\\\\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "This says that for every layer from $1$ through to $L$ (the total number of layers), there's also a Multilayer Perceptron layer (MLP) wrapping a LayerNorm layer (LN).\n",
+    "\n",
+    "The addition on the end is showing the presence of a skip/residual connection.\n",
+    "\n",
+    "We'll call this layer the \"MLP block\".\n",
+    "\n",
+    "In pseudocode, this might look like: \n",
+    "\n",
+    "```python\n",
+    "x_output_MLP_block = MLP_layer(LN_layer(x_output_MSA_block)) + x_output_MSA_block\n",
+    "```\n",
+    "\n",
+    "Notice the skip connection on the end (adding the input of the layers to the output of the layers).\n",
+    "\n",
+    "**Equation 4**\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\mathbf{y} &=\\operatorname{LN}\\left(\\mathbf{z}_{L}^{0}\\right) & &\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "This says for the last layer $L$, the output $y$ is the 0 index token of $z$ wrapped in a LayerNorm layer (LN).\n",
+    "\n",
+    "Or in our case, the 0 index of `x_output_MLP_block`:\n",
+    "\n",
+    "```python\n",
+    "y = LN_layer(Linear_layer(x_output_MLP_block[0]))\n",
+    "```\n",
+    "\n",
+    "Of course there are some simplifications above but we'll take care of those when we start to write PyTorch code for each section.\n",
+    "\n",
+    "> **Note:** The above section covers alot of information. But don't forget if something doesn't make sense, you can always research it further. By asking questions like \"what is a residual connection?\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd36899e-5bc7-411a-aab7-28e3a5a2c6cb",
+   "metadata": {},
+   "source": [
+    "#### TK - 3.2.3 Exploring Table 1\n",
+    "\n",
+    "The final piece of the ViT architecture puzzle we'll focus on (for now) is Table 1.\n",
+    "\n",
+    "| Model | Layers | Hidden size $D$ | MLP size | Heads | Params |\n",
+    "| :--- | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ViT-Base | 12 | 768 | 3072 | 12 | $86M$ |\n",
+    "| ViT-Large | 24 | 1024 | 4096 | 16 | $307M$ |\n",
+    "| ViT-Huge | 32 | 1280 | 5120 | 16 | $632M$ |\n",
+    "\n",
+    "<div align=center>\n",
+    "    <i>Table 1: Details of Vision Transformer model variants. Source: <a href=\"https://arxiv.org/abs/2010.11929\">ViT paper</a>.</i>\n",
+    "</div>\n",
+    "<br>\n",
+    "\n",
+    "This table showcasing the various hyperparameters of each of the ViT architectures.\n",
+    "\n",
+    "You can see the numbers gradually increase from ViT-Base to ViT-Huge.\n",
+    "\n",
+    "We're going to focus on replicating ViT-Base (start small and scale up when necessary) but we'll be writing code that could easily scale up to the larger variants.\n",
+    "\n",
+    "Breaking the hyperparameters down:\n",
+    "* **Layers** - How many Transformer encoder blocks are there? (each of these will contain a MSA block and MLP block)\n",
+    "* **Hidden size $D$** - This is the embedding dimension throughout the architecture, this will be the size of the vector that our image gets turned into when it gets patched and embedded. Generally, the larger the embedding dimension, the more information can be captured, the better results. However, a larger embedding comes at the cost of more compute.\n",
+    "* **MLP size** - What are the number of hidden units in the MLP layers?\n",
+    "* **Heads** - How many heads are there in the Multi-Head Attention layers?\n",
+    "* **Params** - What are the total number of parameters of the model? Generally, more parameters leads to better performance but at the cost of more compute. You'll notice even ViT-Base has far more parameters than any other model we've used so far.\n",
+    "\n",
+    "We'll use these values as the hyperparameter settings for our ViT architecture. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9aedd15-5a98-431e-bd9e-9d18616e4bff",
+   "metadata": {},
+   "source": [
+    "### TK - 3.3 My workflow for replicating papers\n",
+    "\n",
+    "When I start working on replicating a paper, I go through the following steps:\n",
+    "\n",
+    "1. Read the whole paper end-to-end once (to get an idea of the main concepts).\n",
+    "2. Go back through each section and see how they line up with each other and start thinking about how they might be turned into code (just like above).\n",
+    "3. Repeat step 2 until I've got a fairly good outline.\n",
+    "4. Use [mathpix.com](https://mathpix.com/) (a very handy tool) to turn any sections of the paper into markdown/LaTeX to put into notebooks.\n",
+    "5. Replicate the simplest version of the model possible.\n",
+    "6. If I get stuck, look up other examples.\n",
+    "\n",
+    "TK - gif of mathpix\n",
+    "\n",
+    "We've already gone through the first few steps above (and if you haven't read the full paper yet, I'd encourage you to give it a go) but what we'll be focusing on next is step 5: replicating the simplest version fo the model possible.\n",
+    "\n",
+    "This is why we're starting with ViT-Base.\n",
+    "\n",
+    "Replicating the smallest version of the architecture possible, get it working and then we can scale up if we wanted to.\n",
+    "\n",
+    "> **Note:** If you've never read a research paper before, many of the above steps can be intimidating. But don't worry, like anything, your skills at reading *and* replicating papers will improve with practice. Don't forget, a research paper is often *months* of work by many people compressed into a few pages. So trying to replicate it on your own is no small feat. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f1717f5-f6bc-4cce-b5eb-093822da988d",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## TK 4. Equation 1: Split data into patches and creating the class, position and patch embedding \n",
+    "\n",
+    "I remember one of my machine learning engineer friends used to say \"it's all about the embedding.\"\n",
+    "\n",
+    "As in, if you can represent your data in a good, learnable way (as embeddings are learnable representations), chances are a learning algorithm will be able to perform well on them.\n",
+    "\n",
+    "So with that being said, let's start by creating the class, position and patch embeddings for the ViT architecture.\n",
+    "\n",
+    "We'll start with the **patch embedding**.\n",
+    "\n",
+    "This means we'll be turning our input images in a sequence of patches and then embedding those patches.\n",
+    "\n",
+    "Recall that an **embedding** is a learnable representation of some form and is often a vector. The term learnable is important because this means the representation of an input image can be improved and learned over time.\n",
+    "\n",
+    "We'll begin by following the opening paragraph of section 3.1 of the ViT paper (bold mine):\n",
+    "\n",
+    "> The standard Transformer receives as input a 1D sequence of token embeddings. To handle 2D images, we reshape the image $\\mathbf{x} \\in \\mathbb{R}^{H \\times W \\times C}$ into a sequence of flattened 2D patches $\\mathbf{x}_{p} \\in \\mathbb{R}^{N \\times\\left(P^{2} \\cdot C\\right)}$, where $(H, W)$ is the resolution of the original image, $C$ is the number of channels, $(P, P)$ is the resolution of each image patch, and $N=H W / P^{2}$ is the resulting number of patches, which also serves as the effective input sequence length for the Transformer. The Transformer uses constant latent vector size $D$ through all of its layers, so we flatten the patches and map to $D$ dimensions with a trainable linear projection (Eq. 1). We refer to the output of this projection as the **patch embeddings**.\n",
+    "\n",
+    "And size we're dealing with image shapes, let's keep in mind the line from Table 3 of the ViT paper: \n",
+    "\n",
+    "> Training resolution is **224**.\n",
+    "\n",
+    "Let's break down the text above.\n",
+    "\n",
+    "* $D$ is the size of the **patch embeddings**, different values for $D$ can be found in Table 1.\n",
+    "* The image starts as 2D with size ${H \\times W \\times C}$.\n",
+    "* The image gets converted to a sequence of flattened 2D patches with size ${N \\times\\left(P^{2} \\cdot C\\right)}$.\n",
+    "    * $(H, W)$ is the resolution of the original image.\n",
+    "    * $C$ is the number of channels.\n",
+    "    * $(P, P)$ is the resolution of each image patch (**patch size**).\n",
+    "    * $N=H W / P^{2}$ is the resulting number of patches, which also serves as the effective input sequence length for the Transformer.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-annotated.png\" width=900 alt=\"mapping the vit architecture diagram positional and patch embeddings portion to the relative mathematical equation describing what's going on\"/>\n",
+    "\n",
+    "*Mapping the patch and position embedding portion of the ViT architecture from Figure 1 to Equation 1. The opening paragraph of section 3.1 describes the different input and output shapes of the patch embedding layer.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2010c168-88c7-4045-8c02-8e759ffacef8",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### TK - 4.1 Calculating patch embedding input and output shapes by hand\n",
+    "\n",
+    "How about we start by calculating these input and output shape values by hand?\n",
+    "\n",
+    "To do so, let's create some variables to mimic each of the terms (such as $H$, $W$ etc) above.\n",
+    "\n",
+    "We'll use a patch size ($P$) of 16 since it's the best performing version of ViT-Base uses (see column \"ViT-B/16\" of Table 5 in the ViT paper for more)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bb10d7f1-1aca-416f-b5a4-3abe722ff207",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of patches (N) with image height (H=224), width (W=224) and patch size (P=16): 196\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create example values\n",
+    "height = 224 # H (\"The training resolution is 224.\")\n",
+    "width = 224 # W\n",
+    "color_channels = 3 # C\n",
+    "patch_size = 16 # P\n",
+    "\n",
+    "# Calculate N (number of patches)\n",
+    "number_of_patches = int((height * width) / patch_size**2)\n",
+    "print(f\"Number of patches (N) with image height (H={height}), width (W={width}) and patch size (P={patch_size}): {number_of_patches}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e5f118e-e828-498a-abe1-0de8a5d90cd5",
+   "metadata": {},
+   "source": [
+    "We've got the number of patches, how about we create the image output size as well?\n",
+    "\n",
+    "Better yet, let's replicate the input and output shapes of the patch embedding layer.\n",
+    "\n",
+    "Recall:\n",
+    "\n",
+    "* **Input:** The image starts as 2D with size ${H \\times W \\times C}$.\n",
+    "* **Output:** The image gets converted to a sequence of flattened 2D patches with size ${N \\times\\left(P^{2} \\cdot C\\right)}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1f684bab-1e4e-4251-99b7-839b0b69dbd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input shape (2D image): (224, 224, 3)\n",
+      "Output shape (flattened 2D patches): (196, 768)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Input shape\n",
+    "input_shape = (height, width, color_channels)\n",
+    "\n",
+    "# Output shape\n",
+    "output_shape = (number_of_patches, patch_size**2 * color_channels)\n",
+    "\n",
+    "print(f\"Input shape (2D image): {input_shape}\")\n",
+    "print(f\"Output shape (flattened 2D patches): {output_shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "addb44a8-dd44-4ad4-8641-5e8c6f49753b",
+   "metadata": {},
+   "source": [
+    "Input and output shapes acquired!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ee6c9bf-e40f-4511-9325-498251f5b998",
+   "metadata": {},
+   "source": [
+    "### TK - 4.2 Turning a single image into patches\n",
+    "\n",
+    "Now we know the ideal input and output shapes for our **patch embedding** layer.\n",
+    "\n",
+    "What we're doing here is breaking the overall architecture down into smaller pieces, focusing on the inputs and outputs of individual layers.\n",
+    "\n",
+    "So how do we create the patch embedding layer?\n",
+    "\n",
+    "We'll get to that shortly, first, let's *visualize, visualize, visualize!* what it looks like to turn an image into patches.\n",
+    "\n",
+    "Let's start with our single image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "336e1b36-9849-4104-8cb9-bb64a20ffc48",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YIveHI1M/0miLsQKRA1dbw7YVtE7R2MT9wwlFpHMNKSu0bogxE3PmdAo8vxvJwpKOJ0LwhGnASbjpOjqtaEREyYSWCaUMCl0PbQLD4CmloGRDzoJhnJl9IOeaqllr2V89+XDGqRvF9/3gH+VXfvN3ePbiRC7nI/icl615oVi3eNEUBFJrPv7xj/CX/tKf47/2Z3+K11+7qSHhB4GDUs4brwJIdUPlEJhOJ453txxuD5zuD4Q51PBr2VG6cWx2O3ZXW3bbDtvUMCiVRJgj03HkdDrSn3pCCJS8nB7iEhx5zNvKCsqwIKlKn5FfpT6Qb6rqPc9GuwBErGGukku4KqsxLwir1vU1H8PmSzT70UAfDXGNI1iRtyWXvEjoLuIXWR5LL/n8vRREyShRyGeP+Yi2kguqPP6dUp9c0xax/NmcIc6E/kj/4hkv3nmbd9/6fQ4PD5QCn/mdL/DVr7/Nw2mmCI3TstoyokaBfqZIRUhgrGI6TpS5Aj0yQxwjUyo0dkPIib73KARzAF8KfgowR16/gkYXGpvRrYGoSCnXvzEknr3omcvE4DM+JERWaEY+9prjjY91tLawaSxSgNaCK7VlLxpy0QQPwzCSQsYohyqF8QT3pxMxQCoVeTZS0hhJZyQhQ4yBNNfDfPaBJMBnxTgVpAQlEykX/FzICZSEbmsxjaXdfMic87/8x/9LHPuBL3zxq8SoyEWQS15iHlmR2AvHt4aZWms+/vGP8XM/+zP8/M//JT7+iY9cGOajZb5cLqmgRl485njqubu74+72ntN9v5RuludJiTGG3X7P9fU1212HtRopBTlnwlyh7uHY0/c93vsPgD7n7f5oAGI1Drnkieoczl6Wg7TS9bYlz1Vanz1qNUyB0eacQyolEeoRqb3MOc9Ayjl0raE2FPISRj6CMC8jpS99jpeCj0tPV0sW6wFa80NAqpeuQYF6NJZSHyDqLZAheYqfmA8P3L7zNm///pd5/s5bTMOJFCZSCPSnATXc87FO0RXNcZgpQWJsS6cNxzRTIggjkbEwjyccihaNERJDwaYEp5kXx7cxErZthxKSlGEOkWka6UymazS7XUO7M0Rf0ff7h4lT7/FJ4r0gZBhOc/1+pMCHEa0anDN0jUBKmKeJjMI0LVo35CJxDTQNhCkgk4KoSXuHFYV+mAkxYYxh2zZsrEadIwyFWADBVDLTHBhDIsaE1hmIzHPGh0BKiW674ebpFa51aPchw9qPfuKj/Cd//5e4e+hJyVGLaBFEfARS1hBzASiUUnz0o2/wF//in+fnfv4v8YlPfBRr1Mt2AY+lhMtNshjmcOq5v7vj7vkdh8ORMMd6CotazrDOsd1uub65Yrvd4pxBSmqoNc01FH6oIc0Z+JECuYSAedmE3w6kWUPO1TjPCKyoJQ2lFELVEodY8s36mOodrTU1dJXVgwoErACRlIvxPa6ab38gL4QzMHRpjN/WMFekdb1vBYnq/7x0fasRy8U7ri6xriwg56VInwOCTAwTp7t3uH/3Te6+8SaHd99hOt4T40whcbg/MA4zu3bLXsPrr98w7RPfeOtdYrHsr64QyvC2f4FBMg4BWQoyJTSSivknSIWcPZ0ELSXOOVzTEEJCo6oXp3oquUQW8xi5ff7AfEyEmMlJUUIh+0j0kZ0xpJyY5wPOwKYxCArjOCEFFScwBu0sUgMxIhWkMIMI1Vhd5o3XN+w3mmnUeB8qgCcVOQZSjBSREUqAkOSSyUvEYa1GaVND2VjwQRBTwlrD9dMbuk3LZtfi2g9Z5/zy773Jl3/vTUIU5wJJhe1XLyOW9Kie1FIKXnvthn/tz/40P/tzf4GPffwjaKOWPZJfyk9XL3sOyHKuHrMfuL+75+72nsPhxDyHJTyqpQPXWHb7Hfv9ns12g3MGISAuxILT8cTxoWfoR8jr37zMJz+4cdc9LS4MUr8E3KxkgzVMrf+as3Fqo5FCP96nFGohHpTCwuipHl+8dCgtoJRYyg8X9rWYzMuGxyNwVZYC/mP+vgJD5QOftdQIVQCyZqS5lCWNqOGuzLWGPDwc+ObXv87x/gVWC/rjLS/e+yoyHGhSQMwDKp1IOZAL9EPPu2+/4LobaE3LMByhCD52fc04JTotMFYyikApHiskU050rYOiSEmRBJTsEbnQGs3eNTRtg+0cdw8HvJ8xKWGt4qqzWDT3L3oeTkeGY0Ani7PQGIVVoGRm00iMgZgz6arhar+hdQKr5JJ2FUIqaGRNZZSAkiuyTEDqDFJhjEbHWtPWWhFDWr5DTc6aeQ6k0VOyoggNCoQoyFIrv7ap+WuYR6QwOGewrUOqQikRaw3tpvsD7e87Guev/doXORwDGUURSz2rsJiJrJD3YmRSCK6vdvzpP/3j/OzP/gzf8z0fX0JN6gdf6mFr+LbmVFBraSkGpmHgcDhwd3fP4eHIPHtyfgx/m6Zhf7Vnv9/RbWoBWMjVMAdOxxOn+yN9Py7lD3ne3C+xlYQ8h4yr57z0lB/0muvvxqxlD41agB2t9VKTrIeQlDW0rUDReqpchO9c5rnLJRXlnOPVa3P2bQuVrXzAQB//rfexPL5usnxG0tdrnB+NWlamFSktIevM3B8ZTke+8sUv82u/+Mvcvf8+skRkmenayBtPO3TnUDmR07wmqzy5fp3+ITGPgTTOOG2gQNs2ZOGR/oTIimuTMDvFlARZNXitub3rycYypoj3MzeNoVGKRgo6CftGs1Ud9w8PyJ3GNYbdrkMWePbNWwYf0cKSYgJR0AqUEmz2jq6zaAOJSFTgWgspEH2o2MNCXzQuM8+RQkEtJI9uu6VkQUiQfIZUo5tCQqp6bYUVtK7BREe46wljJKaI1QZrDHEqpJTJOWGMZrNpyElXAE5DDp4YAs41aGE+nHG+9daBlAxFZLIICFT9Yoo8o3zrZru63vHjf+pP8lf+8s/xgz/4qUfDpJzh/nVdGgxASol5mjg8HLh7cctxMcxSVo9cQ9n91Z7r6yvarsU2FqilknkcODw80B9PjMNECgkhZEUdF0ZPNcrHcgmSyo1Vj0hs5ajqc1irz/lkzXGNUWhjUFpXb7nUIpWsoXARZaHYyeWz18OoXITOj1ZZzmYIvMQ5XQ3zg2vNm799eHtxrT9g3GsVRJRMSYXkJ4bjHdPDLafn7/Pum7/P3Yv3eOcb7/DwjfdgnJEloEWizRptMzl7dOOYppnoIURJP0fm4whhiZ5yrS+OMSBFriG+jNzsFF2WPEwB7TS3h5kmR1SjkL4QlMaVQqMVkoJMHjEkGhl4ui20+4bdvkULwfP3T4iYaE2DEgJnoOssQhYQGes02oLrKjgXikeJRJaQUy35pFIqz1oajKzGIaRejBCiz5RUQ1SFRFDRZSUkIXuyzKTsyaWQSyDFGSkMShtyiUvKVK+50RKJIQVQWiKsJmrBtutoTIP4Dib4HY3TR0kssiJ65MXVqAsAooIkN9dX/MRP/mn+xl//q/zoj/4wjTNoJYB8Jjw/QvM81s1KIcXEPI4cHw7c3VXmTzXMurmUlDWUvdpzfb1nu92gjIZSiCkyjTOn44GHwwN+8pT0WH+79FSX4eFaO5RSoc9I6mM4ezZOY86hqjUWbTRamwWJlY8kBCkXSt36/ytPtoaweSm7nBFtan63GtQ5DF1D/SUMLWvpolywgZaLv5aizvWrc0y8/oX6IitDi5IRKRPGnvfffouvfvFzPLz/Nv74Aj+emIeBcH+iKzNSRQygBewFNCkhvKcoiZ888ySJQXK8H5keTlhpkFbh04SxjhATSka0MXStIQsFIdNpKCWh48TeCmKcyDGxUYIrq+mUIoWJWBJRCK6uDLLVmK5wvReUmOhd5OlNQxGWnBIiBYwtmKahSAEiM8QRIwztRqGjoORCSlCURmiLj5U3rmVBLoWlFCr3NoZMCpkSIIVMTpkUEkYbrNGUuRBLIoVaERApY5e9I4rAx1ARfgqCjJEapSRzmNDS0DSOIBWNNZSUkOUxgvznMs4oUs1PygJslBpT15Oh5mFd2/LjP/4n+dt/62/yIz/yRzBG1lrmIyh/NoiXzvtSKLngp5nT4cj97R13d3eMw/hYAxWStnVsr7ZcXW/ZbFqs1eRS8EtXSX/qORyOTOMMOT9665wpRX3Le1iZSIJad1TKXJQ99EI2UNVTOns2Tm3MYrwrp/YRgUUIhCrnkJ31toXgcAZ8ao1icZCrUX4g1C1lecyCgBUuHsX50JKXh82lnz2HuPJs2CkmxtOBw/P3ePFuRVzfffP3EKnH6UyjDSFF4njEELG2oEVByYJUiRgSxlqkdEgp8CkSoqLERKMkndNYI4hJMidq0d5k5hBwG0WUiud3R0oWtEZys3PEJDj1ATH2tMbwpLOUOJPmCZxEK4vWGmsquGKkAKu4fmLZ7gQxwDxlSqrhabPVFNVwHHpSBETGaY1PupaPzEIsLZBTYJxOOGUppWCLAVEIKZJyQkkDyqCkIPlAKYpCIUuBazuK9/hhIvmCiLKWoRKEVEt9UkkkhTgHMhklFFoVtAYtColM9iOSiZKPH844y7qhuTCs8rjJnXN87/d+kp/5mb/Ap3/4j9awTz8ihJee6mW7XNg/3nM6nri/rR5zGiZyLudyhrOW3dUVVzd7NgvrByCGwDyOlY53PDENQz1FOfuRejSUDFyWLi5zzOoFa85YQ1itNdoYtFIYazHO1G6Q5TFSrFS8R89ZSQo1LD0b58VnPn/2vHR1rMazgjgX/5yrjJfn2uPTFwf5yKB66aoK4OIUFjlBqnzTd77xDb74O7/DO1/7Cv3DHXk+QThxs1XsGkuJE+F4D36gayyN05QSkRKkhiLBbizt1iEPB7ROlJjRMrDdarabhm6z43CYOd0OjKPH7A0USfJw8hP9YcaaBqkVMLPbbdg0ho3JOCO52mj8nFBS0RhFt2lpnCGmmRxrpCFUottYpNT4kIhB1etZCrYB5RYjLA1aZHKQ9IdMJrC5ajBGgC8YpWqIq0ALwewDSglyCihZMMqAkEg0oYASkhgCMVVEN5dMSpEQQn0fEYSSZKmWAxlKkcSUUCKjlKJtHFJLpCpIEoiZGA9Mw4cMa8U5dGIJRTmXT6QUXF3v+emf/lf5sX/p02hdQ8THDfcI88u1ZneBNAZfSyaHhwOHw4GxnypjX9SivXOO/f6K/fUV3aardLxSzu1ep+OR0/HIPE0VFDiDIPVNSiUoeWXePJZJVgRWG/NojEZjllzSLP9ff9cvEQpWptDKq13rk/X1XzbO9e9+O6WJcwpeLksni8ktoezq33MptR1N1MeWvIar5xdYo+EKHuXI8e6W/v6WMAw8f+cdfue3fpvPf+5zhGHEKolWGacjymv25gpiQM0jeyfZbDRNp2tPrqRGF1rR7BymqTS4FARZShocIShca9jtW0JKyHuPkgmrWuIcCUoSpoQuljLDEAaMCGAjG2uwVw3WCDadrht63yJSxlmD1IowJspUaKJCq4TQgCxoVXCbpd4cQRmBsgnbGsiOh9ue22cn7u9n2q2k21Hb/XIiTpkYC1JqrJGkFMipkELAGEERkZIlMSdyKuRUiDGTUqKESCmJlDMxJmLK5KIRKAq1z7YgyTmQYkXbc0koaxHUOrm0krbRDKdbTKO+ZX/8sxnn+b+P4dOaP7Zty5/443+MP/fn/yw3N1dos3iQvG68C2SUC5vNheAT4zByf/fAw/0Dw2kk+oSg9ixqY9lst+yuFo9pq8fMMTJOU0VlD0f8NNUc82JDr7nWmg+vPZMfRGGNtdUgF8M0i7FKvYawdVMqvRjjmf+65I9CLiTiCi69RKhgPZ8+4N3yoztcyeLrxRELBW/tuqtMH87eshp1wU8T42kk5QQ5k2Mgp0SOET9PGBH50u98luPz9zm9uOX5u+/z7jffpT/WNr+gNFLBIAJhHDEIrhuNEaC1ZmM1rtEUBUWtzWDg40g4eYyCXWuZksc0iuQEWRUiI6aFtpMoUWisYB4OyNIgMDipSTETxgnbaMLoUQWMEkuJwWKtRhRJHH2tV8aIEAK9sLGKqIduDfsjuSSMrM+VCoSK5BzJWROmxHAKCOGwtqVkCLMn+sJwmAh+2RtZklOmlErQT0aQrSCVRMkSKTRiCYel1LUfOKXan4qilFpmFFmQSkaUylP2UySHyoksJZNmjds0aGUwXYu1jvHUk3f7D2ecSyvEst8fIQlrLJ/+9Kf5y3/l55eSiaKCogvH86IOerlRWTzfOA48rHzZ4wHvPeR6sbS2bDZbrhYCu7YGBMQQmaeJ4+HA8XBgGkbSGsrmteb3Mmgi+FbD1FpjrMWuxrkgr+vvZ6rd6mXXjhL1yJMVgrPXrJWSb/28l8a5GtylOZ6NkpewnMeSzzmVKEsDUKF4z+d+9df56u9+gZwCJSWin8kxIIGxP9Fawf2z95HB098/cHro8UNAZUmSgiIVPlbQw+cZl4/opzt0USAKOWbS5JGO2qisCiElpjEh0TXkM4ren5j6gHQa23ZkGTGt5OqmpbQFJUEm0DJiTUOIgZw8VoJCkWM9UGqJSuPaisCXUJuu86J80bQW10qKyBX1VgYpQCZJSZkSM+ja/D6NE6fjRAkWP0ZKqntqnCLOWUrRxDlhcEghiH1gmAcoinmekRKiE0QLWUZKEVjbYIwlxABLaSp4SEESg8CHTCqi1kZFrZXGCLIIdpsNjZKkOGONpGss2mlc65BKMsVQc+wPZ5w8IquLX5JK8vGPfZSf+7mf5cd+7EdxzqL0Y0uRWAqIK1J5GdWlnJmWZum72zsOD8eKsOZSw1kpaduWq6srdrsdxlXDTCkxzzPH44njw4FxGMiL8kEtD14gn0KcjeeSPKCXMLV6SYuxi7dcjXLlverFIBeCwUq9q7etdv/YLnY+FD4Qvb7M2HmZCbte2su1kgkeubRLWrDAQTJnjvcP/NYv/SJvfu4zxGnEWcM89JQU2W83OKM5rqSCaSKPHsaILZqcEjHFJQqRxCSJAYajZ7AeLSNKREoOjENEm0y3s9idqfnbcpBooVDGsu025PlEQtBtWqYUySWz3zXIFoKf0EWTY0FZcEGgisQISUyRlFLNI6VDL8V5ZTQxFbwPyJxRTtG0GlQgZ1DKIYSi5Mx48sTZ15KdUFAkYYYSDMFLoq/haO97yljQ8gklZsZ+onEtxijGceC+H1DSkaitjTlAbgrCVOwjyYwQkSIK3nuiz8xjYp4qJS+Git5KBFIWckykLOiaDde7HVokDg8josykKHFaY0zNn40uEOcPZ5xSiQWgqRtGSsH1fsef//N/ln/1J3+cbWvRWi5k9XWTrUX+unFXJkvlvM70hyMP9/ecDkfCtHhMatG+bRu2Vzs2u4VgICDnSJw846l/NMyUyDmj1lKBeKy7iqXLQwhRW7yURBuJNpXJY+3CezX1R5na2SKlrgn7SlS/6EY5KxdcINVyQZ8rv1icP+NLueP67weQqrJSnhCVELCEs5cdJYW0/C4pQhLizFu//wXU8B43amQsI6oAMiB1orOeJ1cdcQwc7wfiMCGjYCsV2jn6caDPBZUD0nb02hC9AZ8JfWRKHoqH5HA2U3QgK0XWEt0YlBbEUCghkUX1RJutYwxVRyfOnuE00kiHLJUvqxpbi3AiY1Pk5moLMXF4ODDPc2XhbFqamyv0blMRzhgpqiLcxspa+BcJay2tc8sB7+n7GREzRjjyDCFlpgmKbEAJjClYLZBTYg6Bw91ASVQCfbSUHDgdR4Z+RoiIVBrbFFxrkEuNXMmMJOJ9JJbMPGTmMRB9JiVBSoYQM4mMUjUtS7HqaklA5AwioQQkPzKUgN02yBTpbEsWGalfjqX+mY1TSHEOyYQQtI3jT/2pP8HP/exf4LWne7SuXMeyeMwVCa37LT9utZIJwTP0tS/z+HBgnuYqS7J0gLjGsb++Yn+9X9y+IKdUDfo48HD3QH/sSSmevU+mFoklS0/m0pZ1bufSopZCdDVO4zTa6nPJRBuN1GYJX/WZN7sSE16SE2HlA1/UT1nz65f94GVddW1kRixA0JqHitU7ljNAuzY1F9Z2rgIlkULkzd/7Al/8rV+hLT3YjGsMfZi5udrQtrIigHmmfziQfUIWiVIGIQqKiNtYro1hnD0xe1IuRKlRxZN9OmMFKYJyFi0EJQjGo8dEMM6QQ8bPI8EPNefKGSVh6kdyLOQ5M6UJmWvXhnIWbStFTjWCbduQZk+WFucV3f6am098lM3TJwgpSNNIzAllNDoXMrlKxkiBVlXjh/o103UtGkXXdBirmWJkmCZSEZQokNoAHiMUISuG3iOlJkbB3UNPDAFVJELaqi80z4Sw5JMIhM1oAVM/M6VELILkC8nnxWEZfMiECEILtFRYq2mMQxSBljBPPVbV8FYKy1wKxESaJ7b2BtUavvni7sMZ51m0i8pu+NSnvo+//tf+Kt/zyU9gbS0rrJvxzLyBs7dcXoUYI+M4cnh44HB/z9j3pBjJpbbUOOe4utqzv76ibTuUkrUXcMkxH+5PHA9Lbioe63xrQ3cpgiwESiikXIgEeukiWRBZ6+wju0ebyvhYGEHiglMrlDiDQCt97+z9uCiRrHntB+iBj9ftMoAt5wPsDK+WCvCsmNLqN2t5U5ARqBJJw4Gvfv6z/NNf+E+Jd2/T+BOuZFJOHPsT2sFNe40iMw0z02lESYeWle9bRMaHkbZ1WJFQGu6OR2RSGFHlQJRk0doxkDOzTxV8yYLiKw9Uoymx4AfPNCbIsr5jKQlkYpGkWTBPkTBHXKvYqI52YxG6oLQkMKE7xcZtECja7TXtky2qs4hcCGNZoikWIA+M1QhduzliDCitabsGrSRaaJxWFCp5IIbE7BNxLsgsmWdP8JEcC6FkisygDNJKshTIVNBWIUxhnjw+JfJQ67PKFqxSxJzxRRAXo0+pLC2LkeADhUxjFM4aNl2DMwarLWGeGfsTOWZs17HZ7IkCJiIxjEgZ2bQbQhg/vHFSarJ9dbXjZ3/uL/KjP/ZprNVLC9i3SlusIORaNkkpMY8Tp4cD93f3HA9HQojncFlby3a/P5dMtFGknEjeczoeKy2vH4mxXoi1DFF47IcsiyHpBdCpwI5Aa4mxFQDSxmCsrUQCbRaPWeuXq1RIfW7tJoFH9YLHterlPP6zrsuyyctc2CUvXQz18eUKkKvqpJDkixooS0RR5om3vvA5fvsf/6e8+8XP8bF9i04BUTImB/ZG0pUEwwEhJek0oks1KiEUucAwTsToMa1Fp4QuiUYJYimExJnK6FxtjYtpZp4CSL3kXfX9BAV+TkynQAilIpwxIbUmFEEskmEMzGMkxswE5CaTXELbghAZtEQaMFuDNS3dbotoFb5MqFQ7QsI4EuaJtu2wjUOqBKK2KeaUFnqkQGmBEoXJDyRfICrknIkHT0mSyYcaIqeqzaSV4zhHQorsb64wbYteOqCElthNQ4qVEZSBEiNzP5OFphhLLFBSJqVCXBhzQpRKkLAKYzWbbYs1CqstXhWkcLXLRWWM07SbFicSGY+QGaEFzYftSlk3nbWWn/zJn+TP/Jn/Kk3rFv2fP2hzPiZXpWSC94x9zRf7w4HgfQ1NRC3ud13Lbr9js91irSUvtLy+7zkea6O0DwEhQQlFzvlc66sEqeW1Vu+39FAqU4nqxpqllmnOiKyStYNkzS9Xw5RLN8kq1flyeaR2d6y3f9v65UUE8S33P1akLn4tvEQcKCBKJQ8c7h74ymd/ky//+i/x7M2vIsaRrEEUj1NgOsf1boPUmYLHSkNUko3RTHNAOYufI/08gSwUZSkkYppQ1hL8zBgioNhKUzszckFJAVIhlEJog8yS6AN9nIiheqUUBTFCjgV8IiKZc2acqq5rEZJUFDfdHmkk2oEgkIoHLSk6YzYatzXo1hJCIsUZwghpqhQ+a0A4coGcCiLnM2d6xQtKTJWkECDNAjEF2iQBzSQS2mi6vSMJy+0pcTqMTDlzeHbHZtvypG0IStI/nLi52rPZbKuaY4ikrPAJfJbEVAgloUpCyZpyKFUlLjcbx2ZbyfbXr11RckShME7S7Qw5eUqOyM5gdx1No0hlBmvJ0oBqP6Rxitou8+lP/zB//W/8NZ6+9qTWotbS5Uvh2+WqniPGyLh0mhwfHpimaaGzVWGrpm3Y7a/Y7ndY5ygUwhIC9wv7Z54rrL6q3OWczzXUSnIoSCVQZtGH1XLxmupcu1xFl6WSSwN0NWCtFxreyvZZENnLHs9Lw1ujgXWtAsyXQNC3tnit1liWmuUSXZTH+mwuGSFBlkyOE8/eepPf+ZVf5Xd/7Ve4ffstCJHiA31IbLeGZmvQrcE1LSEOxBjrYbNtIBte3PakEuv70hq36ZgKjL6SuoXUDLHQh0xRihf9iBCKbWMQWSEkGF1b8VIq5CgI0VMy5KzIWdQCfKx6UolMFCCNwXWGYZrxJeBLZGNbhAggC0rW7qacIpSIEAmjMiIlfBhRcaS1UJwAEYkp0BhTW7JyrLXwix7aIgo2G2JOhLGWlkqSCJnZbDvs1qG2hhcPE9NDzxA8EUU/DjyME2m/RZLxJTHnwNZtYSoM08Q4Z6SwjKlwGkeSiLx21bHbdeRc06vNxvHktRuun1zRbRqsEwQ/kWOl7JEVEkMMM14UlBE03dKJ4lqybtDtzYczTiUVT57c8Df/5n+dP/pHfxDnTPWaYqHFwUubVVxsvhQT0zBxOpw43B8YhoGcHgWIrbXsdjt2+x1t0ywhVWKaRvpDZf9M41hzU3EJxkjWVqwqHUKlDSqFMqvR6cVjLvXL1WuevaO8aO1i0QKqGjQfFHo+k8+/TS3zO82ZuexdLatoF4vms6hsldPxgffefptxnNnv99xc7zjevsfnfvWX+cpv/BrPv/Ymvp/RuqHEwjAPRNOhrxu6rkVART59T9tu0Kpj0xnGUTPF2kvZ4PApcTzMpCgwUtFuO2gkOQ3MgPCBfVZkqcjRVzmTnAijRxQJCcahdglJoUlRMM+JlGuTtnIGbRRN14Ax2OiZy0jMPSkVmkYhVMEYSQgzcfZIcVX5u2TIEe8HnEyYraVtLUo1NE2L1ophLMQQKwtLKFIuSAVSabQTKCspKhHCyDSdmKeBzeaKpx97HdkWhpTY32x5UiyDl/iHI9M8chpmrBGYtjmDT5lUCQhCEYViiInDNGMbgXIK20iMckgNm13HZu+Y44iKlXtbOdY1lDbKYE0lskQ0erNFO00mkpEEDFl+SM9pjeWnf/qn+Imf/HEapzB63bDyUQ1DrGyXC3Z9huBnhv7I8eGB4dATpwKllijMygDa7+k2HdJISkokPxOGgek0ECe/bnMElVspxaJVrsRSv5QLbXBh9JzlKw1G24r6LXS8lUOrpHg0zEVWUyqQMp8FuVhMaq0yLpa4vp0LqKuc67r1jspe+WBYK5Z+2JwjYRoJ/UCOnq9+6fN8/jO/wXgckKWgSkb4kTQeEQ+3NDnW1/QRkSNCRrQ1aKVJ0deG535m7CdUabDbgtYC1xo6s+W6KKYEbz+cONxHYi4U65CmJbmCLhlNRklFNIaHUMslrgjSnACPRJOjIKYqfZlzIYY6kqBI0I2jfbKjGIFuqrhVVxpQGiUKm87inEIbgTAwJ4+xDtsahKnlCqEEttVkYQizwKkOa1qkrPXSVHxVBywVwde6ypRpaRGqRbgW0wiaPOBGSUpHSqxhbRYR1yj2G4WfG0RKROcwJdfvJYGyBgHEmBFS0DiF04Zj1AwxkFBYJXE5YfJE02pM67CNIJUJpQQpJkqCtmswzqBtJWfYboduNgijQVoQiRxO+DDC0pb2oYzzU5/6fv7yz/88u0VxYM018zIaQV4U2Bf6LJRHVYLjofJm59kvUqs1z2zbhv1+z267xVhNLokYPNPQ0x+OTMNAXORF1pEEZ6BpAZvWzhCl1rBVn0NYY+zZY1bveRnargJdC5ldPoa1Z+h0ZfSsDJ/LkBWWPHF5X2f6T/3/i57p5fmLJmyMvP/2W3zml3+Zd7/2NayC/u595uMtxQeGwwNpGmg1vPH0ijeuHE3e8H54YDhOaFUwVoCMCLup4XeoCm45NBipMEKiZGHTVO/oQ+Hh3Re1wz9ngpDMIfDs2fuEGFBkOqMAxcOQ8dOATIGbrcVpixIw+wrw5FzV+1IqhJgJMSGNoGst2+sN2VQ2kSgsCvWGFDxd2yJVwTUW3S7IuCx0my2b3RZRNAqN2u/pY2R4fsRZjeoaQg5M80TOAqUdhUpeUEVQcqXGubZFuhYvC91eUJ5GRIxIJxnHgRIzRkm0EsQ405/qaAYlNBmJX/bqk6stUJsbEJIoFXMIkCO71nCzVey3kpsbS7dtEVYjnaXdtLUhY0lvjNVYZ1DOoJoGu92hXIewpqolkMg+Ms59jejchzTOv/N3/i1+6Id+cOnIuBAWXhg4l5WBtbieYmaeJk6H46IxMxLTIjSlJK61bHY7trstrq1EgxDiQr1aRJ+nmZTzS4joJU9XiIqoaqMrkVivoswaay3WOYytxvkoO3Ihbbn0cQpVRbnKGfi5VBRcDIwLY8vlHJ7Wc2m5t1yWSxakdlFKL0tB+/7d9/mH//f/J5/5pV/EpYgIHoPnamNwSmBKwLaK3dbSmoRi5I2nLVZLHu57pIDd1nD9dId2hpwjsghee+2a0WlKyJADm84yDjPP3nkHZWuDskipMmKCYPSekFM9oIQgC4kvAt8H4hhoRGHWhUFEZInElCoQhqz5ZwYfIimXShoXkUxEGYlQNcUAqq6wFhhdwTfXdLSbjm57jRAB02i0a8kBVFHIRnJMt8QhE48T/W0EWbCdwbjNUk+fSXkm+9rSJlQm+xkpJU4akoqEMjDnARkl94cXxJIQSjNNnofDkfujZ46icqidQaRS67e+ULJinhLTnBmiZ54CeyO43mk++rGO7/3kFU+edhQKc4wUrdGu1spzFjWyc5qiBWiLcg3SWqS1YEwtVZWEES3d7opIi5DxwxnnT/3UT2CdWUCgpVh+4RnW/HEN+apynmfoTxweHjidekKsTBepa57ZbFo2u24hGlT01c8Tp74a5jzP55BwbQNDXPzNBayRagEG9KPHVOvPmVSw3rfOMHmUqRSrvKUQC4X4cZgAPOabl39XlJcH+VSLXdk+5aUZQoWV556Jfuabv/d7fOlXfx3/7DlGQUfByIw2sNk4bNdgTaZtFVKVutGdYrN7wkc++oQQI1ZD4+qBMvcTBE8r6+c7nXqqpkZmDpFcJMNxIBfFzX5HGiLCF4ycKdToJqY6WgAl8MGjlg6YlAp+jsiSaglhUSsPIeJ9IJZq7M3GYZ3CNYLNvkVZSdNuCD4yjz1KNjSbDq105Uk7RxEF4wRFJKY5UqJka1qKz5Sk2Lornr175OHhAdManrxxXT2TKuToochHSV8SKXtIGWkc83wiMmO3Bm0NpjHEITGNM+MwMs8T3kfmqOpwJSGQJZETgOTu7kAYJ0JM+BBxwL4xfOLJhk997+vsXqvlpSkkTFNJFhgD2uFMgzUWIQXTPBKFprUNummR1pIklCKoygeWZrMl5IZm84EBWv+sxmmdRptaDBZiKbSzUvNYCOdn9loltQ8Dx8MDx8ORaeHNClHLFq6r4xJqC1gVWvJhph/6MwBU23rqbJRcSqXjwcIkEueezrX0YezaBK0qmd1ahF5LKi8rG5yngi19d2fdn7Om0JJlLiHK44erRigEZ0Hrldyz0mpr+Lt60AX8Waw1TD3f/PKXCLcvuDKCrZXsjUZLUAayH0k6YroWZUBaiXYK21i0aclJMY0z09iTfUaryo8ti/FXxFqCsRyCZBIdYyncPtxitMH3E8chEIrCyar/NMeEFLUEEkUdrqNlzXtTCgQh0QJirrIeVbVQkpAYU9vE2s5WmUqr6BrN5mqPa3d4H3mgilNLqXFNi1R6ue71CBRSooqqjdBFMQyeFOHhYeDu+YkUFU2zaO9kTVE1lFZFkGMmL4SA8XSCklGtA6lxG4eWis12y5gS9KF6NCnY7zbMMXN/ihTEkudLrLaM/YQvmRI8KSastuw7w2tXLW883dF2Fp8DCIXZbur11grTduhmi2k2SO3qfp/bqrho26rVe9Z1qny2R+55Yb//kOMYan5WQY61gH4u/C8h3Pr/KQfmuUqGHB4eGMdx2bU1zzPO0m06um2HcRahJCF4TqfqZcehJ6UqcLwaJ6sMiFhU/lZK3TLop4bbculsqDVMsaC1tZF6aZY+h7VVmqTml5dzUcQZ3KqRezkTKS5Iwx/kHZyNdLki5/C2XqvVw2ZK9MT+wE7BblfpdjstKwMIjx8HnBY0zuJajWwMxRqyNiStSEKStQZlKblgdINaWqBSKjjXMLlEQPHV5yPffHYiJY1OmVZ5pCxsnKO1jpQiKRaMMRxDoR9nQi60qm5WlCBFCEKAUlUyJFX0VkhN021QurDbO57ctDStxCowCKzQkARGtghhELIap5BVrbCqvmcUaok2ahPz5AMlgjaOfpo4nAas2kHRpCAIISNLIPpKZQwp0TpHCYVpDkAg9hN62yGlQTWKfg7000hKdbpX22aeFkWMgeB7hjmgs0IJgRYSt+gX+RSX/lLHRz5ywxtvXLF72uFeu0LuLFnV8FWpgjSiAnRNg24apOooRaLbSmSo7KmFlJLDcsYriAFZKpnfmebDGacxFd2sXpPzpqsKKeXsMkvORF9lLfvjieEM6KzUN1lH8u13dF2L0YZSMtOSm/bHE2Gu5AQhBTmWs4SkgEftWLmGp/rcLP3Yn2kWYru9UGiXF+LO62vUU6t80DBZDbP+flkmegR4Lm77QH33/BhWGDtDqTW96fSAjJ6rbYNUmUjCtJabroEyEX3G2UK3M8hGgVVE15KloxRFKZKmcxhpySEic0UuS8qE5NHdFt20vPX2M77w1sCcGxQCEzPSFF5/coVuLPd+ws8TWluU1oyyMN/X8FIphdOVOaSlIhVFiqVycGMiZ1HLJW3H9U1H20lunjRYl2k2tcsnzB6ZK6aw314zz2MlDei6zXLKWKuJITP5ypM+3Z0wWfO02yCpTCnlLBSxHAoghKIO062Dj8fTSH8/oIxCO40xstL7Bk/MMykUQqj1XFEkYQ7Mk2eeRC3j5IgpCVkqd1wqiSg17dHbKlD++mvX/MAPfi83H7li/5Er2qdXiNaRSj1sS56AqY6MFLLOslACskQZCRliqSp8JWUSE4KIzAqBB6pg3jQNH8449dLtIc5F/3UD1jrnWT0vpgXQOXI8npjGCuiUZRPbpqXddDRtNUxK1Q4aDz3jcSBNARLncHJVzFuNQIh1enM1tpVcYLRGK4G1lXR8VjnQGrVoHEnxCAKdNWOXCODcoVo+8NnOM0TKWcyulAXcQSBKPN9PUfVaUKCEhRcroUjK3PONL32OX/0Hf5eHt79C2RS+9M07DseBH3hjx7/8qde42mn2bzzFNQKhMkUWipQ01hCRTKFKfBqpCSVx6k/EPuFEJs81X5nGQMyCfiycxjrIV6YJWWZcW3VbYxHk44mtttxcX3GaPA/HI40s2MawNwa1TJVOIhNSglT1acMiNNFtG9746EdoNwIYabva8dM0jsF7TtOJdhsoMtKaDY0xVbXAaKZpIuXEdrfDWUeJnmEYSMMd0zgSTxanHfvXW7IoxFHStBbbNpSi0KIDWYhlADEzDCdyyriuwXSarAS+nxhmj9ZtbYyOhX4e6PuJeUr4IJjHjMpgUGfkXVEoMeJFod13XH9kz6d+5Af55A98H7vXn2A3DcLqpRxSa7U5nBCpJ5eAj6mOrSBRqGlAzpkUwlIqKQRG8AkbI4ERYSXJPuXYfwf7+07G+S3sn8uu4ZWAtohBD33P8VDpdjGm8/3WWbbbDbvdjqZpEFJWb7KwgPw01dx1mRh9OXL+US92QV2NxuhLeZEqwrR6UG30mUSg1rxzHYkgH+eRnEc3c8ZmLz7jEphees0VmV0j1QVEqkhSNdhCpmYAkZIKhxe3fPkzv84Xfv2XeHj+FioX3r7veffomaZC+ca7bNXED33qda6fPKHdarLwKCMIMRJSwDUOaRTzvLBqRCGmxP3dC/AzrangSEy+FrkRtCR8GNAic7MxdK2iEHh+eyDNnv1ux8ZYcpaodKqK6rYhisJpmFA5stGSjZEoWBhZBSkU1kiePNmhbMAnz0xEu4ZZCLI2CAtvfPwTPH/xgkYNVVhbV1mUUzjWElzUFCVpjEE2rvafkpjnAVEK7X5DLoYwCpzeYLQlpozSqoJcU2AcfdVSRlCyIkXJOAXmkJkiFBEJfuZ47BnHhA8JUEhpKWhEASMrWYBcdYOQCalhe2X42Pd/lNc/9TH2n3ydzdUOaXWtxZdyxkGyVBBrb6lUilgqUOfnmRTqBO4wz8hCVVgYB8a7A7kf0DrQPd1jrztcsv9ZjPPb1GFWEeSyzDUZlhF7fc88L2UQQBvDdhlk23Vd1RjKiXka6Y+HJc+szI8VeFkpcevfX1u3qoasOnvSeps6t36tj1mH2FY1g8Ugq7TLWU6EDx46H/x4l5/6Ao0Wy7ClvBhnEZzlRRCV6ZL9yNe/+GX+wd/9e3zts79Jkz3txvDOuy94525mDJYwBbzM3N0dud0brm5a2v0OaRRCZbRQoNTSK1vriyFFsshgBO3VjtBroM4eLbECQlfdhu+9DpwmT9dt2VmJKIHT4ZbdZoPTEqtrYf9wGEHUJvOiG05hYIgZGas+jjNNLYPIXGeCSPDhwBe+9Ftc3bTcvLGnNHvkbs/V9ROUMZxOA5ub1xljpoRbpvFEIVXCyUYhlUXp2hjtXIORguvrNxhOD0v7mUKYlownhIlYMq1zhBB4OPX4ecT7uBhm1ZTyXuJnzxA8PlF5vmFmHDx+zkgcpGUSuTC4pqHbbBiHgehHSgZrwWwk2yct3/vp7+fTf+zHaF9/DbtpkK0AmSmqkFKEHHG2Dr3qp5noff1cQuL9QJxHCJ4yT+A9KUei94QXJ+6/8YzpeMC4zOv54xj7FOk/pDTmtzPSy26wnNNinFM9pfqxqhosTcpN07Db7dgupHbWvs5hoB8G/OxJMbLS8tbMbfXYa4nkHKouhqjP4xIe9X/kKsC1ktgXFQOxjNk7/4hv4y0/sMrKqljrmh/kynKhRihyHUJbInke+cpnf5Nf+Hv/EV/6zd+mHO95erWBh4l431NOAgO0QrEVCh1h7keGU884GXRJuKZGCk5afAj4yQPruAfY7HdY1TEZRxwmap3cI2PiarPhR76/5TTO+BApYUSgyMriBRSRyCh6P/IwHkmyJZHphxM+zRQhKrihBcIoKAWjFkHtRmFagdto2r1le72lvblh+/QpH/2eT5JSJornuM2OzTwT7x6qCJhruN7vmUMhFUlEkTH4Mdfe0faGnd0QvSeXiF1AsZQP3D0/Mbx4Vvt6c6pRkG3JSSzC1pE8J6aY8EWQhSIimOZMSAYhLUZZUhpRSrLdNXzv930f3k8MrWJ8AJCYJrN/reHJ91zz2kdarm8Mzc7WsqTw+BCqcLasg5Fi8AQ/Vph05XiXQg4TZeqJ4wApoFIg50SeRoa750yHe2LwICHlSCm5ji37MMZ52c/5aDgVAALIsY6CP/Unhr6Gs+vwca0Nm82GzWaDtRYhBCll5nmuHnbhza4QCkKcX3c1zJdmlSwdJ6uh2kUHSJta03zZOJeGab0YJ6vnvKh9/AHX5LKOWZvNX6IhPB5OQtU5I6WKc6Vp4s3Pf5bP//Iv8vu//Rke3n2XayXRnaVrNE+0xnQa6zridGRnW17fW/adpXMOqzXI2oqViBQhCJNnOPYIZTFmC1isNYx9IgmFdFuYBTlKSgykeUYrSeuqYHESojb/2o73H3r8XMcRINWi3epJM+SQcFrimsVbxkCOiSISwghco/nIJ57S7A12Y7h+7YbtzRW5cTTba+aQebh/IKfMi3ffJQVPCpKSLUY5xt4jlK0UQKOJQoBQxGIY57Hq4qraUBFLzVE313vmAC/ev2UcB9q2w7Ub4uQZ/MA0J+JcVfTGkEmiIs2FOpmMnLHaLtxr0LrQdZqcB3zoOfX35JDoOk3XSbYbwdVeYa2nP71Xe1CNJXIeTkkSAqEtUmughtlykU4Jc2C8O/Dw7D3m0wmRE85Z5Monzh5pq0jY9skWt98SVaF8B0fxz2Ccjxtzva1Qc03vZ/q+6vpM03zmCSql6LqW/X5P13YoWdE0P88M/Ylx6KvgU6kJdO2aqsN0LqdDn/sjeTnEXYkHK+1OnMe6P4p5rWoIYh3Oe45my1IW+UBb10oPLC+H8tUYV9GtZV4llZCgiiD7yLvfeJPP//I/4Yu/+csc3vsm44vn7Jxj5yxX+yt2LpGmyG5IyDLiGslut6PtFNvO4LRCS4FtOkKYCcMMxXM6HTk8HHDNluwKRWzQWuOswcuE0hrXKeYxME8nnJHkBHNMxAzz6ImTR+mCP804Y/DzjG0U3/PaDdcRbo+BYfQYo4gohikwTyNBFZwF1VqULWQV2Fzv6a42CGeIKETW+Cnz/HRLf7xHloxVlScd5lKbpmU1EG0tRrcI23F7d8LPHopgGk7MwxHnFNapWmqwFmE17dWezkeGaSYjkMIyjT0Ph4HTYWCeay7pyWQhkHJVIkxYpei6HdFPaCUppTZr9/2B5y9e4L1HRthf7Xnt9R12E3Au0TaZqX9GToH9zRNsu6EoQ8q1pKOUwJoNfSiEtEyPSzCOE3fvPeedr3ydMIw01mKcRbqqho+S7G6usK5j/3RLd7PHa4OWH3JWystGuuzghTUTU2SaJ4Z+YBwHgg8LcCCwxj56TecQQhJCYBqrQvs0znV8WilL6Ajk9Mj+WcsoC03vPLJ98ZpmYf1opZALr1bJhfWzSuOrhSu7kgsWosHqNb9tv+X5DFqYQOvMi/L42VcAqBJUMt/86lf5v/4f/w/c/v4XKf0DOkf2bYdAo2XNFWMKNA34oUeXxNPdDt2AEAmy4v72nqg8T8QVpdRQdh4HpnHAShApMvWn2uot4P524vQwI5JCZEmMZenw18SYOA1jnUmaIHhf68xCk+aCjBmlCzomNqWWbbqsyMBxmhA+YETBaIk1SxiXI/3Yc512oDbMwRP7gVZY/OjRUuCkYR57PAmlKllEAUPfo0REB0+WI7sbhZOeaT4RJo8/3XM63BKc4slrT5CmpQgwbUOnBAnBaRw53Z0YTu8x9yM+ZmIWHMdAJlGUIC9kDK0EXWOxQkL2KC0pRVEF4zXDqccaDTnRtg03T7Y8fbpFuBFrJUYJ5jCiTQMlAAmlHTmVqi6hNLOPpCxAuiprEiPeJ9KUKGMinCKmaxgGz+CPZFnY7gRPr55ytb+ibRuMaSp5QXxYacyX2qaW6l2puebkPae+53RahJ1zqBtZGpxzNF2HcQ5k7cEM08zUj8yDJ/lY9WZrEyOl5KqZI0SdZiZFbbvRYiFCVMJ0bZjWtY7EOpzI1J/FY4raYlIJzMDaPlMV8l6OAr5dU7SswPpSUlm8bS61GX+5IVMgTIzPn/GZf/j/5vjmF5DTESHAaIsskeH0gO0s2hnazrDtDK2ubVjWObSyFAmlaMbeVz6oULRtIcURmRKbpqUA05zpjzN3txNhOnB/9IgskbGGpO2mwW5bZOcQ04zUE7oYCgKfamNwDpEQBXKRRY8BYgqkwRO9J0lVZ5mmhJYSJRRKWUSRlbebRJWhjKEqNQhBazVaSGQR+DkzjIGmcUihIfWQI8PDPaSpqlNYQzg+EIQmjIXoC7pAYyq4lWLCqILdNpjNDbd3A35SqH1HmSamkyeJfB6lKBYWGKZgnaNrLNvWLW1oiZwCcap9o05rNDNGJQIzTZvYd5rtNoA7YXctdrNH2g06e7R1pCJxypJiJoYK7ggxgmgpwoIwNYQONedvnWa73eHklnESPL89cuwLYZ7Z7w3lIxNaPJBl5vr6dYS9oojNhzPOx1HoC11vGQWwTgU7Ho6cjid88KygjraKdtNWAabzRV9LJ32dKJyrYa7j61YC+YrCAmeVda0vQB8pK9lgAYfk0pWyEhTUShETiwLfWtYU4tySWckFi4leyIqs8pZndtAy5i5T647rLEuKR8w9b3/5S3z2//OP+Ppnfws9HGmzZfKeojLXVy0f/0iHwfNk39CqCDFDu0heBMHYzyAFc4wMcSQdqtT/kycOowXeFwQJqQzzKXJ8EXjvzZ7joZCVprEthIQgVwnROeA6yxQS3W5PYxxD33OSR/w4M3iPDzUM0CkukYIk51JDqwymaNolZzdAmiM+iUXO0SGkZpoCISekdETvUUIjtFmS+soE2mw7js/vSNHXA17ZpXhfCKeBpA1StDirIRuM2TH7cWnCtwhZv9/NfocPiRwDu+2G6aHn7r3nzGZEGGiuHNpV/u5+v0FRSHEi+RmrYOhPDDJXrVoN0iRunu4RsiWR2O227G4s7VPH9vqGZrNDWbCqQekGITVzyCCrinspouIkwpMoxFD3dgkTMU4II1Cd5dhP3M+ZZ8eZu8NE8okpQiwHfJx4GiPt00/gtplC+HDG+cG1auPMy/To4VRVzFhrYbKCCm1bFa2FqGPg53lmHIbqYdNjDbRK1VfZt8cRfOqcW671zVXVwDqHNosc59JVUvVh9EWe+dgofVkxObOBLmq0HzTQ+sCVyVfZI3ElgwMyJfLwwJd/7Zf5p3///0W6vcX1A0+FIhZJPwzYK0O73bBtFTLUAUKRTKM1Wkq00wSxHHIIlDHMJTD5mbsXR1onMTuDIBJ8pKTA8OCZHiLzITIfC0FGZhVorKFrHae+R0WJNBUQKcpwvyji2W5PSD1xiPgYyDliS9VoFQhEljTa4OeEVNCqGhnk5MneExvFzfaKbrdHaVdngyRQOwe5kFOkKIMyFtd0lFLHIFqjmOeEbTZEP5NiQptmSVVqiJizJCqJ9zNSlDrV2jR0zQbXtqAi5B1WC+IcOBiF1JJ+c2QfYu3tFQJjHEpJjBZM/UQg0FpN4xqsKfhZ0jQGYxXdU83u+grTavZP9qAVuu3Y7p+itSalE9N8ACXYba8QyjGHOmezpkaZHMH7ieQjKQbS3BN8TyTUmqi1RDyHMdGHZWBvn5ahwxnbRU7HAbXrKdLzB63/33XOD5QRYoxM08RwOjGNAylGVpFlYypC2y1j+gqlDh2aJuZpEekqjyPu1pc9eyv5qAV0Sc07S1vaFZVdcku5ek350s8KEJ0/w5JsniVCeEwxv/VDlzMpulDqv8sMknA88eVf+VV+4+//x6T7W/rnz5FjpMHSD3eUNKDUjru7Z5S0I/YTJgl2rsG1itYKRA7oVtBuHTELijLgO+QsyHlm6ic2lhqKSkn0CSMkMmeMotYpc53LYp2m2ThiCZjGgJKUInk4nCi5sN1ecXroOc0JlGO7aYnzRCFhqDXUkkHFghESLQXS6EVAOeNjRDWa7c2ebrdj8oHRD2jrmOaEnmdcu2GafR0GpDUSX+u9IVb91yLwoZCy5GZ7TaYQ01jn2Eh5FvEuKdV5qix6wyKjJbSdBVpmo2spJtdyzK5pKcETxpFpGvEh4JoO1wqkAG1S7UKShlIq2GQaxeZKsbsxaGfoXtvQbl9D2ydIZclxRKW6KW3XIoTE+0BKGW105SZ7X+V2vCdNMylFSpzJ0RP8zDyOECHPAV0EWmiykAhlKQLGMXD7oueN+xPbJwOi/ZAkhHVzX3oX7z3jMDAce/w8V1EqUWeJOOdemjidSyIEzzT2zNNYyeyLaazzP6QSiLIYpNLfUkI5KxhcjnRfDVebZYzf48+aJz9a/xqSf/tDZ9WKPa9cgExc7pElUIaB4cUzvvybn+Ez//Afcfrm25Tief/FCRvhI53F2sL+yTXFCh5OJ16MiRw0jWqZDjPypkW4iCgjTVdntARf2StaW2x2lCKJU2AeAq6tFMRUIlJktMk0Xa5BUDG4ruXJ02uM1SBrxwZKkH0ixIBWlhgzRWiEsmhrsIiFxRQq4TCmJXxPdZiwUiidKBIaq3GiRXYaoQshB5LI+FgwjUYqg4+Zw2lEagtCohWUGDm8eJ8yzQgEIdf34FpT68w51dqrqN8faSYLSYyJFhAlEeeBImr4TpEYI8nZ0G41KMfmKmGFJI0D98/egzDzxhsfpXGKMCn8VBDF14lhWqNUwVmJazVuq+i2At0qtrsNynYkFCEFUhgg9GhZ45ppPKJMS+MckElhZh5O+GEkzoH5tOg3ybUxQ5Cj5/75A9NDZGt1nU5Gpmm2SCJhqmr1D8/v2b+xo9Efcuz85VplLscldxz6nhQi6zR6rRXdZkPbtqil4TbFqglUtYAe+9bEku9Up1bBJnXB/lm5tCtNzy1zTYQSZxV2uead4uVQdu1kufhD34rMXrIQxQVNb0VyV3oehRxn3v/S7/KlX/2nvPmlzxHef87x2S2nnDiGTJNhM3uuNh1bZevcEtkyRcFxLkwiEuLIvQyorUaJRCwSGRMhZWLK+JgY5oQxArMxxEkt+b4kxwpkNa3m+o0dO9Gg7I5ms0FbxWk4MYdEzAmjHa2yaG2IofZkVk4rSFHJByVHGrN06Fhd9WJTQhuFbQym0dUL6yXfNoJ2V6U3REm0suP6+glKWzK1/NIah1YSFUdKSAvKKeiHiVQgFdDWME1DVXCQCqUdQlsUkaZswBq67aYKfuWpDgEqDiGrArvRS1O8aWp9MQtyoyhEuk3Ha0+fEOYTKQ041SKLRhaPcuCspGkEUheU9qQ5krPhpFvcxqIcJCZiuiNNJ9AOpTuELhhjEdkv+/hUha9nj59m/DBy6ie0rO1t9f1JtAFnoegq/XoaZqb5BKLQaBCxcHo44oeRZvshRwDCpdesigXjMhczzL6euqICR841bLoNzjmkqEBD8gE/zQTvSalq1T4qnpdqoIglnJUv5ZtKabSxyzQwu3hUs6jlLeMSVkL8apxSPPJmP0jRW2Ppl2LbVVm9njCiqDNPFgrkxHh3y+/+yi/y7IufRfR3dCqgRAAhMU1LGgeCSMjs8Kc6rGdjLa0xJDlx7I8YW8GyydeQD19w2WJdS4oTL14cmGOmbRy6WPI84wbBZtughEOpmW6r2W+uaK9eQ5gtGejHnqmM+BwwesmrpK2E72EmhIhxujY/+5nTPBKjZ68aXru6YuMscz9iRKHZtTSbBtsYlFNgBD4FfMnoxlRqYQJTFdEoQqKtxTUt2jQYVSh5pJDZtB2m3RLCMxCSKcyQff3+c6p9jyWd+z3bbosSGaMUpEAcEyJapMsIXci5AjJSSrSUCFXno8hWsd040lR5R0UGTG6QRSEJKAK2DFizhLmqDrglR2QGEWdIIzkUshzJ6UDO49LeVkhhJgIpVYHzeRooMS6K7rV/uE21H1grRULh9i1XH0noZuY0JJosQUW8n3Da0jqDEoXxNOKnGVE+ZCnl7G8WrzlPU6Wa9YuS3jITRWlN23RVLU1ZZJFEP+NHjx9moo9V4iNXMOhx0Ho1itre9WiYUmukXsWg7ePEaaXPHSbyIpRdye6XBnnGfrhAaFdNoFLR2CQlRSSQAZFr4p7k0nUyjfjDHe9/9cvcffOrMN/hZCLYRLsXjMeASJVw0TpJzLUvkSKYQ0TKjImJ11pHYyviqRRga9nHGEPjGkDSbiMiVlLHs9uRrdW8JrYkDUInINFsWuzeoVpJFIKYMnOJFCloGlO7SPxAUjMZiTIC0wh8iBibQQhmHOPRE4Ri+/Q1rKgtYm3r2Fx1NJsGZRVRJooRIFwlbpfqXVOuniRlgRJVRE0rUzEAXZXxQlAIpUGA2TiG0wlrQemqN+fDTBYRSp2AXmQiy0gqCUHBxJmUAtLaOvFSyNoqRuWvSqmWPLVyZUXjkB0EP6CVZ9u8VoW18aT5gJgSqfRQIko5lGwQoqCsAqrYliwBaSoN0zYtEYGMIHKmzCNxHInzjCwZpRy5aKLKmEYjTIPIVNArZ6STbF5vaHea67kwe0G31bWFbIwIERfATCOyOHcW/XMb5xrnlVKBnbWTZF70Z6E2QlvnaDcbXNOgtSKlTIj18fNcEdrLEQ1CPIaa5/zygtmz5pGP3NqF/XPxOLU8bi3BlDPZQJxrJtUoy7d8IiGqb6SUpaevsuKrFw2U+Z67N7/CN373c7z95S8i41hztRLZbwySDms9d4dIHmuEcJirTpLVjhAyMQyUHNltmtqaVgpN26BdLS/V6WXVa286R2dsHdDzcFhGGBmmIZNzwLUO7Sx+CkhxqrU+bXBWEjtL9gKrII4zMQYiEtvt68BWKeuEaJ+woUHrwtX1DuNqR8XuyRW77QZkwViD0FX0quSCsrU0NU5zzWlzqUir0Ww2Hc2mRRqH1AbrgFmirEHlzHA6klNEG7W0jZmqCyokpAClp5QAKiFyDYOFakg+M80BnQTKJAShHmoykpVECFNp0noBAoVAZIW0oF1laKoikQTC1MAomWcNckY2LVppcvLEXD0liVrOWOr3xjZnEAmqusc8eVLwZKrie8YQSiVIZMEiqVnDda0VpjUIqylZktC011uUdty+9wIRAvsrx81rHc4WcviQ/ZxiQSpzSvh5oj+d6pSvnM6+r86u6Oi67nEsfKwI7TiNhOAr2nmB0j7KeMizYeoLYzTrhGmtX/KoSusqb7F6z4WitxLa65uuPy+PROCRHQRLR0nlxFZApPb2FREpYeD07td58zO/yNc++xlOL275xBsf5TYJ5tNAc92yaQRZOFKE0xTw48zk6x822mNVDf2QEm0NrjVos2jvlkwuGaMVKXoEke2mQbmWk9KkORAnz6mfUbCowVmyFCgCmoJ0VW282Wxwba03zn3PIU5QMsZUrSalKhlkOvT4aaaJcHPT0rQNMQf6sadrmjphLBd0MYiF/hZzRoqacyIlRSgykRIr29Q5jdu0CN0gpUCriIgw+pn7uweSnxF6aXZfplRLVcNhSZVCSeW0NPNXlXalDMI5JAbTdaDskgoFhCooIVDUYbZ1cFZVxJAFitYY11bKYJaUslzzRiBDbWpfqCtkKZnHqaLTQqGUIaRADLEOfbIsIzFEJWh4TwozJYVK1lCOICqFMRdRFReVQotmOfjnGo2JOj2PxvBR69hcO9I0stu0tJ1BOkES/5k8J6QYGMdhEeB69Jpi8Zpd1+Iah1KKtPR3TvPEPE91XkXJS1vYaiUsBncpWXmBul5IWVaDlefulDMgtEhZiot8s/JoxervH+e48DIqW4GgvIBRcvGckRxHxmdv8+4XP897X/gcw7tvkaZE2r9OzoY5wP19j5CFfk70fSSm2t938jMlw7at16TRhpLmhf+rqJPWIlYu/aUUgvfkFKs8aPTkMCOA4ANDTDSmXtPb5z2mg2vRYoxEmogoCSUb2tYwC0jThNGakqHbX7F98hTjNmQfeNDPmE6LtGUpmMYxTjM2aRKJJDLaGEKMCDLTODGlGR0dZqfR1iFU5eX6eeLh8MB2t6W53p8/CzkwDT3H2zve/9pbWKvZP31C6xq0dXU4EFAwi/ZuIBOQWpOLoRSDcrV/U5VyDo1zmilxRpaqNiiygJSQqo5mkKkOr8olUhYkWGlHyou+cjEo2SFFHZaUY0QIsE3CyC1W7zCuRYR+OdWr0kFK9btN80zxnjj2lDiTlCGYQtJQtEKoqoyfY1oarAUpVscgVa0nN22Dbhvcfgfen4dNoyXzxTiOfy7jrF6z5kJD39ceuBDOgIpSCucsrm0x1oIU5JjwYWZavObarAsXHFceG6kv882VIbQKc61lFGMeZUYe65qPIWxZQCmWss+5ceQDoNBaQlklV/LCnZVZIJJnfPFN3vrcb/DO5z/D+OJ9dE5kKXn2/BmbzRWu/RRvf+0rpJQ4jZHbg0eh2Hcd2kf8HCiyDu1RSlEKjMNQKXFaYHOt1zpnyTlVIeKcSd4TiidNARECBnGeW5pSoh9OtNKQfcN88rW5xrWUWCdexQBjPzP3MxhLs7mi29+gdAtNgBRxWjGMI5MPaKPZGol1isY2bLZ7ijBM/Yk4jYRp5jgc0LFhYxo612FcBWJSSRxPR/rTA92wO0vOxDgyHY/cP7vl+bsvaDYbbHeFaiDpQoxVZKvWptWi2G4wzRWxNKQC0jmKUkhlyIiqu+MzpAmRPbIkRE6UqFGqDimKKaGUJMWJmDPWbrHNvs7XJEEONZdVmiJX0C+hdItSO6TeU1BIlZA5kOJMTJkwh1phKpJSdJXMPJ5IUiJ3Er1xtQumyCoX6iNzH/Cjr+1iCoTIZAHaSYTR2FajWlcJDVLgc+Z0f/hwxgm1wXSaJoa+ZxrHC1W6Co9b53DOobQ610Gnaaod4YvXPCOki1FcjjsQ4mVveC6hXNQ7zyMSVuW81UjXXFM+1jVX31idtODixkcjXd5KkVUcW4hMOD3wzhc/yzd/99eZX7xNawp229J7QT+O5FFy8+STvDgUcpSE3NHPBUpgs5PcPN0xnHpULoQwEDAoItEHjKwgUE26ahglWEIqa6pqYMmoLDBZkpSjZIExmpgDrql5oyx1ZmYpahF5lhSpKFkx9Z7TQ8/2tQ3WbdCmq6iolHSbXbVgrdkZg7GOEKoYm5ESyAgF1jmyn6vuTS5LSFmNyDYNrYL9fosfDjSNI0wjyS2hXAwLiUCy215jNztC1hRh8b42UpOqLpWTMIwjoQjghqyqcFYWtSG7SHMmcgmhKDGS4gBFIotDWYdKFaQM47AciKle3bVrCIHMgRJnhEiLnpCumkRFIKQilhklPDHrmnIpi8w1hBcONFWwTIkWNSX8odbqWxQKxTBFQinECKfTwOn+wNSfkDlwte1QRtemkDyy2c3s9hswAiFqJaKksnTWfAjjLLAMIxoZh5EQwpI31g9nnaPp2vMU6hQicZ7w01RJ1IUlN6i2mdeulsXTrdhNVcW7aKjWCyVv0QCqOUYls5+HDamXQaDzkNqXrJDH0g1wbpEWsBJvRc7E4ciz3/8C3/zd32J48TZWRLSzzCWShhmRC+++9Rbf+MYD7z0f0HZDyIUh1JN5Tpmbqx0yB5iq4rgoAmkkUllsY+oEY6VJCKa5ntAhBBrlFuSxUIpEookykXOVqwzztHgchdAOYQ1ZOZJs6jgCqUD4mp9pB65DGosoudbn+gf86UCYR2zTYpoWIfXyXcBwvCfe3+I2VzjXEQGMw2mB7hyu22CbjqZrcY3BOYXMT+tou1xD8KZxmKZlthv2T9+gdVcI16Gcw3Yd3g+UHIg50GhB9oX55FHNhhRKpe81lpw0WVd0OBWJRCGFQ+lNZeGMIyJmtMhoWdFynSLkjHFqmZQ2Ekd/BnjIqRIaiqUUT4wRSqKQiPEBUQqm3aOspsSMKBZjWpTS5CKJEZSLtMrgEYTQI61hnGbuD55hKoxjYhjr9PXhdEKSmfew3W3x88Td3QP7/Zbv+b7Xafemhrtus4yo/JAtYzElpnlevOZEThkpq4fUWuMaR9O21WvmTIpLXXOaF87teoZVwrXksV/zJQmSM/NnGUSk6swTuSK0q7yiUhdDh1hqIhedJReeciHsVdBnPUsvbi8lI+NimL/3Ob7+27/E8P5bdbpZLOzajnHwHO6OhDkyn0Zuh5F+zuQYqPhcRJTINHlyiBgqeqiNoWksunW41tU+yhhIpeBTJpzzy4J1lpBiVb2fPbLIqo0kKpVujIpUYBxmdBvoNhuSbEjZMPtSUUvTsrl5im432N0VKE1Jtdukv3uf4eEOSqZTApQkpNp8UEjMfqCkQLdtkNKBUbirPaJEdGNw3QbjXP1ZNI20EKBsRUVVVctrGkPwsdYYbzJFOLrNlhAC8zSTgkeRGZnJIYBy7K/eIKuWMAdELpRSK875POJREotGyBZEQ0kjpQQCnhLriAaj6pgDISQlRfzco0Uhh4ASgjlUjR+RY40IRJ2gRoISElHMdHtJs215OCaEdCh3hVCm9hnrQkkRrTUbUegPt0zzxO39kWfv9RwOnuPJk4ukpMLY1za2cTqy7RMiRY4PJ6aTR8nAJ3/wKbY1UDJSO9rth9StXXmxQz+cyycrI8dYQ9O0lXQg64i2sPJo5/lM1VvzvMeRfY954yUYJBZygVoG4K4SIysqK9eZkZKFoifPJZNLvuzZU5aCfIkKBGXB61Lw9A/P6e+fcXj3G7z7pd/m8PZXCUPP2+/3DGPEiSONUIhYZQ7bxmEoTIcj09RjG4vRNbw89j19a7GlIJEYvSr/gbGG7vqKjZQcDkemcUAjMa7FmZpXhwhJaKTVWGNpXEMKVTOolTtiTkyLPlNbCgVJiAJPwmDZbPa0UeLaSLPboVWlyflhYDod8GOP0pJ5nshSElJeQBFN3rUI4XBbjdTQaEsOBkJEOou2DmU0QmuyUMwJohAI2ZJLQUtBkhW5vHn9Y4zagffMc0BrVet/3tfDWhWGFEFldpsdpt2DbPBMFFlb24RQNZTNFUAL04yIibbpEDJSwsw89UzTjO0yyooF8NGkDAIJOdTDr2REKEilzwi0to5clvq8aUEofJxJU8HngFIbkqyzM5NYgMVS67GYDVnODEPk2fsT77/XM46Z2S914BgJoaru5xIpZUKLx5EccZ5QOWDSRAk9GIfuPmTLWFhqm4/dJHWLVyCoWZpG64SmVelg9nNFaC+CzPP8kMVYz2QD+UFW0GOt8+xhX1IzuACBzhgsj4fA5RJr5rnWb2BVWzg8e5c3P//rPPvGVzk+f5f5eM88DDwcJt65j/g5k4eBvdE8aS1GQuMMu9aS3nsgEGlcRym5blylORwGrpxDi9oUXsh4P1FmgZNX7PY7dAiIGNBa07UOazS5CKTSGG1BKKytVLjTwx3DdKLZ7biyFp49oyiBjx6FRAhdAR9pycJiuytSiLTdBlEi/nRPmEfIFYEtC3qphEaovFAsDW3XgUoUlSlFYIuGpBChkGVtWk+lqgEa1WBti1amDpgNE1mAj3khYzRgWrxPDNMD4/DAeLgnTKelpqtqeWXT0l1fU0RFWp1zJBGQWiCUpmRRtXdyQZlaDmo7QZp3lL6nDA+k8ABZQlEI1YBySOnQ0pB9D6Lq/DBMZKGqvIjUkCVeZkzbYkxDFoIYInP2FFX7j1MBZEFKhY+eXDIFyeAzz24nnr8z8Ox54O4+EgOETFX4E1CKqphGWRyKSJjGst82bLcGQsAQkEwUmZEfVlQ6zJ5xnPDeL56Ic0jbNi3ONbV7otTyyTzPVRHh0SyWGidQygWj59F7rgjtqgFUk1BxnmOCEI/NtWfu7CoGJhbDrMSCmg8/Hgt5tekFlMox4Pueu7e+wrtf+Cxf/8qXub29JWYIWXGaIv2swANJcxonxDSx7wTbXUPjNNYqpljFnEuYaJXAuoZ58vTFk2VVGN92DdurBpzGL6TxZtPWsFxQea1Ng5IG026RukEskhXBz4hNpO1atlcbFBnVNRwP9wQpMLqG97ZtyEIjtMM2mmmoIs4SwTyPVfhLVkRXCOi6DZu2YxiHWt9Vlna/B13w6VS/u1RFvbKuvFmpVc3TpEeIQtsYWtdWWdMUKSWRAmRZSMmSEEQU2mr6uzuG4wtEmtHOoLXD7vaodlMbGWSmlGmRpqRKTi7RzYraS6g1YQlZGaRtkLmWpkoYkUqAkRQ0KA1CsmSr9GMgnCaIGaUdbrMhhkIQtXtHWk3KBRULslWoprbaGw3oRC6ZFHqmwTOcRr75jW/y9d97m8Pzif4wcjpFUqpthULWadsCiSwsqVqVXxcy4zqBdtW7l1JIOaBEojEfcj7nNE9MYwWCFjOoPZvW0bTNAgRJYvT42TNOE2FpC6vr8d9LcvoHW7z0S2p5F32ZS465es11RF+hNlfXMPn/S9qfNVmWZde52Lf63ZzGm4jsUCRISmaSSaZ36cfqSb9AeriSSUaKlF3dC4odCJAAWEShkJWZ0Xtzmt2sXg9re2QULgkCyTDLjEjPCE9PP2futeacY3zjyz3RzyL2z9K9UlnOZ374zV/y01/9hoe3PzG/+Wuu737g46cHLrOnSENVjpQrNcTmqAiRGDxrrgy2rQtEihx7yzKvZB+QYjOxCIV0PZfpQqgJZKK767n5+h47djyeLjw/PeCsQ1ndqIWy0dilEejR0vU7EKaZ0fOK3e1x48DuboeWFb3ryW81IQUymRQXZB6xY89w2DexgpSUtJDjyrq03st2HUUpahWktLAskZITUliU24N2VCWoJTdxRglUmRpvViqMc+36qjWyZvJ6IeUV/IzOMzWndiqTqZ3DGEPtB3JJnPNHOmPRumI7Da6Z5rW0DRupYhs6IhBuQIiu2cxEExwI2UQLGUFOhZI1Eo3YCAT+fIHVM0jdoh9sh1CSlNpUPCWgKMISSNHjl4gbdySj8CGyrCemS8CNAzdf39DfVlLX4hqrMcRYuDxeeX6c+fF37/nxd++4XgJhgetlZV1Cg38JELpQUsuwNaZR6J0RKN2A58NOMh4G3H5EdB3CaGQtDPrL+Ku/R3Eu8/JFuFCbplit6bqurU82BEnJmRACIYQvbGFf/PgvXGVfTtDPAgQhv0Beqi+KWG3T3E1MsEUptOXm3/bVbyuTmHj329/yL/5v/1fe/uV/pi4znYiIHIm+IDFtp1gC1XvSslBDQpWK04qhb1TzkAIhJiyVXmt8aQtmq5sKSAnd/K3B09VCEgW767m5P5KAT+8/EdYW797tRtx+BF1Z4gUZFdoqcqxMlwspBNx4S7c/0B2OIBK9hH3NPD59QtbWjy/+ijvscYNpmmZZCdeZ6TyTcwN7SatxtnkJnYK4Xsgx0tkD2kCl4KMgeCC3OPkqC9LZFqRkGwNKK4EqkThPVBI5rIjUKO5CWFJJ5M6RMmipse6ApMOYHb3uWhx9zlAluiqyX8jMSC3I0iG0RlbRhn/i5RTdFD1sZEZJyyoxAmEVgoQpFRVOxDyRbUeVprF8w4qREre/5WnOUAMlVkRucrslLHx6f+FyTijd8fHdA3ffHenvB+xuJOK4nFc+fbzw+GnizU/PLEsBNN575rWtnF4A5rmmNnwUGq1a9orVzU10vBvZHzX745F+d0ANGuUaCUT/L1YMf8fiDMtC9B62gpNSoozGdXYLI1LUktvgyDddZy6tr3uJa6+8pHj9vJt86RS/JBcoKTfDr2yTys97UBqs67PQ4OUyW6E2KZYsTbSMaMICXl7UWsl+4fndG06/+y08fcCVyDBqdK9wxrH6yrImUmw5jTYlrKkMVjF2GmskQmRSaleYTiluOtV24zJjnKSqSqoC1fVNrqgM1RjUYFE7y1j2hLjiLwveR4RSmPEIRiJys3Fdz28I88Lp4UxJgvtvFVq9wlhFVYYiJUOphKpxtamfJu/JYWWdLuyPd00Lq1R7bimJUq0PSjm2AtOCJGrzeZYGdk4C/LK0qy6eWiLKtl1ov7tBu75N3JKHeGK9vEOVBVkyGvBrwo2vSFVzeXxE1NyoBqpn2DvyElD9Dtlb8uWCkoZaM0oW/LogkkA4TfIR7WozIAhF3bTObeWz8Zw295IsHdXsKHZHyVfifGZNCeU6UAYhmxHDu4F0fUZI0fA21oHQpCVxPQUuZwiraYqsdy0W4pUfOL6+4bRIfnrzxIcPM/MsmK6ZXBQ+LCzrSkp5M/YLtJY4ufXTgnbQSI0SoA3YTmL3O/SuR/YGYTRKWiIdC7/QbO3XtUnMPiMrm0XLdq7pJYUgldKyHmOk5K0wN6HC54eC+PmvL6V2bS2yZW3KbZXyxcn6ortteSdb/ylo19v6xed/wVkKQW2NcRvNp8C773/Lf/ij/4l8fmYQBWsEx96y2/fkDNfrynOJzClQZKAb4Ljr6KykN7IRu3NGCNVWSUZje8UaM5fFt3XHupDzzGgtbuxYc0bZjmF3QzceSGiOOZOHwDx7cG3f2O3H5tSYTyyXR6QsdJ2FatBGUHKAmtHGIbShz4VaJU4I5ulKoDaP5NMjVhtqbb26cQ6lLbmuWKPRQtH1PaJ4tCn0Y8/+eI+xHbGAUKBME4QL0ZKz+nHkcLxBGNviBZZECokSJ4gXKKXFHyyF+7tfcX6e+Hh6g9KZcXBIO9J3jv72gF9nyG2o4n0klMxhZ6AaUsxtsNYraqxgoLSS3DTTm95LtIZG1ra7BI2yHfPzAyY1NKY2Gr96pG7OqOgj12niMl0xWiOdI6VMDJGwRl4dX/H2+on5cgVdyGtiOUWUXrhcMk9vHplOiRAtorSdc4oRWTND9wI5VxgpsVq1diO0gZ1R7WGozbaTr4VcI6W2HayUHQiH/1uuf397cXrfFEEvpxgCYy3O2k2eVppKIwRijD+H3X5RmJ9nqHXbNX7h11SbXvbL6PemGNqo7V9ObL8wUIvNDdDWpy/DJ9FiEgBR234pnp753Z/+CW/+7M/R84LeqALkiEwGCVjhGU1kvBXcHh1CaJxVW6YjFCRCdISUkamF1Nz0PaFUzGPh+ZSYritFZkRx9NpQpcb0A914oBt3FN2QldUm9JCJoqWGuc4gMsi+R3Ekmg7nChWN6TtyCazLTGcao6cfhub6iQFZImk6Ef0KOXOW7ao/7PZ0/Y56uEOJhNGKeV2bdnYtLUzo5pZufwTrIERM5xBaNrMIBtM1aNaw6xDaENYKsbURnWnX7xI969Vz+jRzfv+vmafAEgPf/uE3DP0Nfr1iVCGHwjxdQEhCavyhw3iHkB3gCH5CIjBSN+FACijnNrdH+fxOaiPAQs2Bdb6ga6MYJu3IIqOtpkpJSitpDRstEHLNLUdzN+DGnpQgTYGUV1S+shtqa6u6nvFmz+F4g5QW4oUSBAaLth2zTxQJ0tDE+1pgrGIcuiZ0SM2VVKXBKIFWBds7TN+og6JGsl8oqpkusAOohl35xcX5QqartaK0pnMObQwSQS2VHBPBt8yI8tka9vuFKeBzkX3++Yu9ZjO3qs9FylaMQoo2JNpE7i8ff7navogMtjtPExeUlu0Rr1e+/9M/4df/6l/D5YxJAWfanwl+4Voj2siWNXkjMVahrSOlQCmpTf2UxDqHVJIYEqEkYhHozhCpqHFPFgvTNbJkWEJC9xZjNL4mfJpJWWE7x+5wxE8rpgMrFK5rIa85x8ZE1RY9GrTJjYmqKkUEYpwRi8ZuV4Wu7/CyYpJl2A+soiBSJiwT59OZ2/oH7Hcjd6+/IYfmjbwZb5oJISZSzVRpka5HWIOqoNsQnCgSMRWqkBQK3k/o6ii5rWOUEriuMX2ufmW6zvhpJq0LRnWkZSKnlVJfYu0LOc4slxNCG3wRCGUwncWn0AQB1pBKomSPkpmcPaLIRlrYzPmV0ibuJVKyR4jUrri6xx1uaV5NqKlRIKkFqxWptgm5Gwd2N0eE1MRc0TEwlMZRGl7vKXpHd9hjhz3C9Ahpua5g3BOx6froVEW72kKVlGw74c4w9g5ZK8lHYsjU0k5wbUBqie07TOdwtnXPJUWyarbKqhXIX3itzWljkLzsNq3DuQ67uSVSjKQYCdsK5UvhwYu2tRWm+mK405bMgpc90BeTW/XzifmyTnn59Zc7zvYlvXSuLz1mu/5ICsUvvP3Nr/nX/+yf8el33zdXQQ50WqGVIJeEzwXpHK7XaKsQuqC1xApDRRNjRltL33Wb1LA2Ap9xJCmZQwDtmafMw4fCcoFq2ppFmsy7j+/48c2OIl6z390jqqRU2t6uG1DOUmveBAFyM6q3PtmHgE8eKSPUleBbOHEqMIwjSoG1ktubHasRrNeZsC4godbUApysoQpIockAnVSExdPvNaZvALYMGC0pRZAEVKUpoqK0aa6ZtDYzegUpC7kWUmmm6v3NK4KXzHNjxFatuD+85vbuFmcNQkmsqkRRsLplkuZUOZ2vvP7mW7JotrOsC9lHqB5YoRRyEhucazsvq9gMC83d1HWu+SYFaKMpYSKHhRwCUkiGrjmZEJJBjwhjUN0A0mCQ3FrHsHggoq2iGkkWbaeaS+PzDkfH4X6knBZCzBhV6Iyhsz29s80GqNvaJK+JGbiGSK6FUgJVOqTVzVOrHagGHFNSQxXkAqkKsviFmJKXFYrYHOhmyydRUrX6yIWY0kYoy234Q9sv1M28+WKI1bLpZX9vf7n53YSUn1cpL4T3lx0oLwJA8fOp2Z4AmxqovuRuN50s2XN+/yN/8S//J978xa9Zz5cG/ZUZYSzWSAoCYyW9tS2GIHtiiuAUbrBIqxCptD2bUciakdTWb44jZtzj/EIsD7j+yu2d3lQhgpgiKQuezwv/4U/+gjBNfPfVQtfvkWbE2g6tHYJGfBB9oRqFJlJKwGrQSiJDpYqM0xWha5sGKkXJHikyZI8g0fWWFAKpwv3xhn7oMb1CCChFsKTAEgqv7+/Z73ZcrhO5FkKM7QaTM2meWNYF7RxDN6A7hxICcgLZwpIRNDKf0uSk6XY77ugxwy3rmponV2uc2/r04KlhRpPQCnIpTbQy7EmlYCRUKVknT8qZXoHe1tylZkpOKNO92Iuaumu7WVW2wFprGcc9y/NHsm8YFLVl46SSSHVrW40iK4Ucdgip6UZwpSBEA5vlWkk5kqInh4X5upBioOsybmkpeIeuY9yNHI8Hht5RaiKGwOV0YZquhDV9lmwrq+l3PabvQTsKhiItRbZhXamCnAWlaIr8xQwhPk9YldZY5zDWImRLVU6pxZuVkrc15IsS53P9/FxU8Pla+xI2JD4LDH4Gd73I8n6GTb9cW9mE7u1Dn/MyP/83m0Nkff7EX/3Jv+XTX/+GNM0tyVmCGXpM5zCqtpCeGFkuiflSkBqUEWQUYtcUP9EvhOQBw2AkzmjidpWnNhH+4bBD/aHm/uaWd+8u/PT2mdNzQAhFDJnnp8Bvfv0T/nThm2++4/bVr5BDe3rm0t4UQjSsR0mBmhOrn6k1U2qTm9VcsV0B2SFthxEtMBijKVqSS8X2sfW4rm80AwGlJswWp6C05vn0yH48IJRoCdIlkxZPWBbW64Xn84ndzS2u77Ebs0mq1udRSzuNXY81rwjrjCiC/asju1eSNeWWKRo8rDNxmglhRRm5EfAkomqS6Tjsbhn2N8iSqGECYbFW0rkd1naEus0dlAQlEMgWgKRonkxhGvxLGmKV+CwaaCy36b1WBihIrVAFcvZQC7rrqabJ8pQyzdBPZV58mwdUT4kJVcDWRBQBpxKDqeycbSR7ValhYsW31mWdefz0wPS8UmO79dmhw+xcC/0VgpgKpmpQlkxBS4XQmiocqUiE+oXC98+tqpRN8NzZxqMVbVGbYmiezfRCRvj9cdCXNL2fe02B0j+ngEkltkJlK0z5+QoslGpjdaHa59/WJe1r2zIyESAyNU6c3n7P7/7dv+LDn/0HhuoZBsUyF5w1WKObk72m5qwXqiXD5y1+0ICPHuMNWrS4t5ITaktVC2skporOzXy+loy0htvXO3Y3GeEyoayEJTGdJ7RunsQyreSPmbhWdNT0biBqA6ZviBfr0Aa8N6zXSAmFtFybHSsXil2Ro8d0O2qwlNhh719RhCFVMKIlKC/r1ATg/YitlTzN1JgZZUHWzOX8yLzO6N1I0ZpYMmFdmR8eeXr3llQC1bkmRpDtTaMk1ByAQt0ICVr2dNaSUyYJkNZghUbEQj5fKNJTCVhR0aLhbVLJoFo2q94e7BnA9Oy+OmBdTzfuybLlqxrt0LYjlUrMLU5BVhDSgjoirEeUjMwV7wMhepaSyLEwaoPTDTGKAKM0dndL7m/IpqW51VKbwCVfiHkiotjd3yLSjnB5QlAJsTKMFSMHKJocC8tpJmweYG0sOVfEUolLU8V1o8PsHWZ06N5QN46wkhapLNpaitR401PUjsKI/FtK8G8vTrG5BGrTGZotTZrt6hFTwIfm28wlfxa4v9jKfq5t+Zn783Kd1V+4UX7uReWX//HP/yy++Pu23uRF8N7CkDxPb7/nX/2//gcuv/stLgSu5xNhXdrAikrQgk4LfIqkvOKUo3MGIyW6MwgjWcLCMs0McsQ5jTASa4CaOD8/U6TBSYHREqkNh0MbMuRSUMJQs8Of3xLmEykF1kVQhcZqmHTk/YcPyKHj1pk2DEKSpASlkNrhhiNVK6IyhPhADiv5upAmT66PpKow456EQHUD0nVNSSMqQ9chrUSKiJMwzSce3j0wXWZqXFsq5nhgr76Bvs0NusOR+eMT8zQjdAs98uuKjk3/W9hod7UwrTMlZ6SzSCqlZMIa0Cnj+hEjDEk61qrJVaO1Q1tLrDPWKKQZqGZs2aLnZ0IMHF/d0e+PmH6PMJuUUZkNIgayZGjROT9zeoTeNiyeUtaWvWkUd69eE7QiTSfOp2eC9xwOe6QZEKZjONzga9uV913ffMenEylm+t4h/ITImfVyIfuCnxK6NlSOXyKn54kU2hU35kCtc3vtIygs2nR0bsA6h3VtKFhkQ7hKpdF2QGlDEZrLWrGjQQiJM780ZezlRim21Kgtxp1tevsyEEo5/Zyt+UVhfinVe1mT/H7ept5+vamHhNh2mvILSftWltsVtknqX2xgQK2sTw/853/zRzz+9Z/TpYifPZ8+vKWkAjWTomBe28Cqd6WllDkFOpJlpB8Nx+MN0zyQQyZMLXTpcDNgBteGV50hFkHtDcJZejdAlWRfyalipOPbr14T/rBwfrzw6WGGaClFoSWcdcaqM/3Te/b3N01AbnuUFITFkyl03YiyHW44IN2O9fxMfDxxevcRv3iKtgx3hVPfcbi/Y2dEy9jMBS0FqlbqcuXx6YnnH97w9q/fcn2a6I3GGehfebphaLtP7Vp/XEujzstK3mx/ouTG65ESQUKQkbR0ayVqm8/pNqjz0xVdK1L1GCVIWuP6PQINWiEKdF3H7vganxTzdeXy4Q0+BYaxpyLJ0qDd2EKBoP380tHU9hq28AqQNLE7VZDzSq0LYgvBkrKl2S3TzG7sGAZH6XqyVCjX0StNWFsCmw+RSIe1FRE9y+WJ+XRmPs9Ml5V5KvglkSPMV88yezrncJ1FaoGPkegTMWR8aHa3Ljddr5aNnq87C8ZgrEHJEWkcl3nFF4FRLV+mH3+htvbzb1KN5m7MNvatjZDgX/abnwtzS4L+UgH0eUf6c//6+wX7N3aYn9ctP5+iYltCl5e+s1ZEaSbby4cP/On//M/58d//W+w6U/3KOs1ImTjuemQVlKII3vO8XFF3jvv7A85WUp1BQyUyTVfiIrg8eZ6fn3C7FvfQ9R3SGna3r+m0RFnLskZOj58Y3AE/F5arpx96xmHkdt/xh//wNTV/4HRK5FBY5syTmBmUIfuVus7I7LFqaC2DVCjdgZT45BFCofaKQTou5yY7e/rwQNUGoS36OFBHjRwkRigWKciAyglKZTldOX14wD9PTA+X1ksqQ1lW4nwlK0VdwybVE+zubihklJUomVtknywYZQk+cb2cEVSGvgMqJbeox75TLNcrT9MnjBmxbkDISBYRkPR9j1AJ4xyHuwPXuRmfpw+Jq2+guIogbSs5qQwxBXLJGOvagBDdEumKoMQEhfYQKglRI1Kk5kRJmSIqPniEFvS7AaE1wjpQhmXxCJ2I69JiEbUF2TVN7OXM8vzE/HRivUQeP1zwi8SvDVWyLKH9P2dBSYV+59gPHWtoq5+SJk7LTJQFYUHqCjKjt/4fISjCUIvhPJ3p9jdUYdoc55earV9IA19ml0ATSaeUtm9uaeL0+nLfpBHQvrjWfj5B/6bw/Yue9LNU729Yv77kBNUtaVrURF2vvP3tb/izP/qf+Okv/gyuj6j1jKZgpeD17QEpDUpqPn64sq4rKXpajGjeouodWbWrxhoS19PK+dEzLxGpJcEXVl8Yhh1mcGhTSSWiSsEJwTovPD1eCXNmmQLP4kSJma9e3WGk44cfHvj0OJH8wkTloh2Xhyvh1Yy4aTu7KtpVzvUjQimuiyDlSEViOsl4+4rzx0+YocenzDiOfPP1V6R44tO7J7q7b6nSoZVBi0yYV9Z52XSqir7vP2NHjFKI0nbD8/XSpplaYIeORGoxCyJS84KoA1JatLb0w54XYRalkEvC9Y4aJ1yvSCny/Pgj4+4WZRVKtx1fFZFhZ0nR83h6T8aBFox3N9jbG8abPdIIEIm4nLDGQM6EXEm+Zb8IJcg5ImqkZg+p0RxLmKhxQam6mR8yiIqyhn44ooeeSGNVtV21ag+ueUJs+3CpB6iVJRWWNeHXynQJnD5dmC91e4+0BHOt2vu1Fpinlb42QY5SDZZ+vSysOaKspNRAlwzaOVw/bseLIMTM4hPdQW9il47e/kLL2EtilDGmAZ5VG9Gn3FKCY2rSPsHWa9L2Go008F/4hF8I4D9bx74oQiG+iFLYTuBWnC8Wsab+ISz89t//G/71P/1/cn33E+l6xZSCrC8a3ErftbF1igs5BpIPOKcZnUVqkKPFV8WSI6WAU4YgF0IKSGpbhquCGS10BpxBqXZqdFpxOV0J2bd4cqsgSd6//4SWgtevHF+9vkVK0LZwObdJ4HReeCwnzncnDq9n6LrN0VFIGbpxRArVRBBVU4XE3dyx+/ZrgmxG7tfffM04djx8+IlSVobxDzH9PQqBWM5QDaZXdDvLd/03zFfPer2ApsWlC0FK7daT6nbF71oglOsNSmXW+XkTg0iU6ujHY0NExkQucQsh6pAahLBIIbFK09s2BEG0eA2nW1oaopBKRHcDIRXs7R2dsai+bzvBxVNXT6wwX2diquxu7uj2+4ZlyYmcFkqcSOsZXQOqZowU1AyZgCQjVCPXKykIUpJKYSclJUbW52dKzsT5AjTur9SxTa2rRAy32NTD6SM5SRSFoevawUMmVTY0aEULQ9/1SKu5Th5jVNurVomfI97AuHMooaAISmkIn6fzmZQa5V4rw+3xtlEZflFxlgJbXom1FqlUUwWVVpw5pi+utG0jWcVLjHz9LFZ+uap+xpF8Ic17CSWS8iViYUOYbMSFppeVlNpwETWs/PZP/pg//uf/lPXjW2y4YEThepmRFayuOJPR0pJFpZZIrW1KuB8GnNVUWQiyMOfKD5+eSBl+9fVrutsdMkmyXzjcDdy8PmAHgzQC5QxWS4pvoGQhxm0hnXn6OJEu7cV5ejhTs+T+1S19r/jVr26ZL5n5uqDXQq97/MXjF4+lDdtKKVQRWeYZ4xrZPIS2w7S7I7uvv0X1ll47Dvsj1+mZFFeUKkzTif3dH7BcFqyorP7KGq4k5en7gbvjgctDYr1euMwXyqVNLEtJSCNRViBRbRjTKayTWKuxrj2IM5BCE8qLbWAjTGVdIzlEtD0wjpXR3SC1IYnWH8qiMNoikPTjHt3viLV9/4o2SKXJFPJyxV/OhNOZ5XRherrgY2F3e89X/+AfsLu7A6PRqjbZm0iQZ6gVLfvmQqlhA3RJ3DiCEGjTYi1KTqRpItZIXD3nx/cYK+iGkW7I5AJFKvZ335JtIM2S23tPuCxo0ygJUoFC0PcdAss8r8zTgoqKWhPGSrreUVCU3GJLcgYpDVIahNCcz5f2HrU9SmpujrcM3Yj6cgj69ypOaEMgq5BGNvxFLYhc2pV2m9B+YRxoE175hY9zO/iEEm3HtvWdL/1lu5PXzwxaidyyLgSISpWVJEHkRJmfefMXf8K//2f/A5d3v4N1IpYAuSBqRFeoMROBXC1Ca7re0XdN/mWcbH2VEczTmWtMvH51Q78bm3qIwugMVt/hOtPI3dKgVUdvepRKaOVQuuKUJTnLuHf4/ETtOp4vHjFnLnNkDJV+7OmGgcNREH2Ei8eePPhImgLk1sPi2ppJS4EWTa2To2epGTl2ZLVDDor93YHOKea3J1Te3DhzID48kEMiFs/lw3uMlBx/9W0TOeSKEZVrqawxIAgImem6hqFUsolFhNth779h2PeYdEFRSMqQqiSJRCG3612tOAFag0ZQqwN5A7TrrqCgS0KTm/TSHFDjniJ1E7gTkT40EUvwxPnM/OETl/cXro8zz+8/kknkbyZ6A6Ks2MMB01kEEZEyObbZRkoJZS3SjkgpKKZhW0vKoC2WyHx9Ii0n/DUyX04UFkqvUXWzDdKBPeJ2d8zrhN0H7M0zsXpCTm0MVS2kil/bKi6lwjyvsMAatoeULGir0XaToCqD1D1K9uQoWJdEKS1doHcdw9A3ba0U/+Xi+28V58t09oW+LqWkNis3OUVSipvb+PcL+vdper9PP3jRyraJbJvKtlO1qTnqi+3ri95TJc96fuI//tH/h//4R/+C9dMbTJzbHq0EjNa4fYeImbC21OZUMgYwVmIt1CJaBkqVGNNo5YMzuMOe/XGPFJU4TZSuXWtrLShVURKMEo2XKnLrO3Ih+IhUjn7Y8dW3PctZcLjM+JCJ60oqte32RPPA2s5RpSHOiXleWJ6u3C4RO2pw7ZSnQkiJNQTWHKlKcDPu6KksVJYQaLYbhbE7qIkwZYqe0c5Rk2C/f4W+kfSDI4st40b0hCRYnk+YvmfoLFEIogClO5QxuJs7Xv/j/xXDoef69q/Iy4TQuiFRciRnT43NSF+sA9XEGrXKxpnNEXJAlLYbliXQQN3hZTXd4hxqaxly9MRp5vr4zNvvP/D49sL1qeXrGAf6PDOez8jeYEpAG40ShZJXIDYTgHVkRMNeSoXAoZTB7Qw5rszzJ56fLvhLpMyF5fqM6yolQQoB0wdkf8SpEb8uKCPRncLtLFIeKLlwPU3EuJJybsYOqRufudYmJsntlDTG0u0cZmh/frwZQVV8WkgxNnwJjbnknEUKRS55E038guKEdhq+FOdLwTSLWMvT+Cw44EX/WBs8lJ8HQZ9/fpHqbcOgzwOipllBygqyUkVznSghSCnw/NP3/Nn/73/kP//xH5FOHxH+ihBtENU7iVOSGgqBSO9sO6V1RaiKNoVhlFvE+ZZ5UQaG3YDdD8jOoFQLasVCDJnkPUJU5nmiFzu63mG2kz3EwjRNLD5wc3vbhiVSYjuJTwXtOk4Pj22aHRvQ2UhFt9+hekvoNNPlwocffsKOe77qxxavZ1rLkEQCo7CHkfvXX3G4uWGYe9bBEq9nalxBGKQ+NJ0wmvXs8SIiJSw+4ozCKNu8g1qjdpX+K4G7vyf5GVkDVkhEUQgxoEyP0obT0ycul4qKK5oMaaXETL6eCecTcZlRxiFvXtEf7yhFN+pizmQypbaHmBEVamC+XAipgaT3N/fbdkQQljOnT+9ZLzPXR8/540KYNZfTjJIOROZy9oyPp3YSTZf2fgGEqLjeMW763ZIjWSikMQg1YOwOpQUxFtaYuE4BlSU5FcIaGv83Q5hWUpVoDEJdSbngtCWxYAZFjKI5p4zexDC/L33rug5QlOqbQF9WVKc4vjqwu9vh9jtmvzKdznTjnq4/bNo+sWnQ25xG6/8O+Z7ehAdyW2285HS2mIUvvmApmsj1i6L8cpXS/ueacFnIihBtHK+E+sJc3QqzxYxURI28/c2v+X/8X/7P+Od37GxGlRnXKSSaZVlRRtANDr0ThFU1F7qzpNxogMuy0o0CgcHPgUbhrpshWWC1hJqIa6CkNm0T0jR63bQSo4eaMQqU0lA09CPO7dDKARLTdWQlOb6+oyAQSjXYcVoJ0ZNjwowd++MBJyTnVLiczvzVr/8Tobf8avzfInSP0BJjLE53VGvodiNr9KTogUZeSFlSciFXzeD21CJYpjNIge4dzx8e0CWR9wN60HSHXXtDd4qKZklX0nKm5EKSFiMNuh/ojUapQtnIcdkH4nLl+nzm8f17nt+9bRS844HXfwhut0OojlLEZkxocQh1XUAXKB5FxgpBmJ7wRmO6kZQTsgbSemZ6PrE8V6Ynz4e3V0TVzbpWCkVV/NPEqjVoQYqZkgp5E//Pj5HxMKKMwN3sGW46SoWlRpIWGwle0Q8D18eZcRiQdY8Q6ybgbTc3VSV+urI+npFSYc02la0N/FUQyE0SmHNCCYF1TSk3TS0PKJWE0Zpu13F4dYPb7UhCcLleQSqMkBiRccNA1o7oG7PY9MPvrQz/XsUphMDopkN8qfCUUzs5U9r8dpt5a9sgyxex+pea2s/FKpom8MuME9HCesSLrECCEIUaVk6f3vIf/7//lOn9j1g8JI+TBS0suWqer1NzlJiK02CNa4E5xmJ0IaUJRLP6GK3od3uEqPS7xj/qrEErQYyZtAZqbklgqpP4ZcJ2mePNEWs1UpSmJ/Yt91Lqnlwqi/cY1/i0MWYGv6cCy1mxXDKmdnRa43qLHQ2qs2gh+PjX77ksE6fnJ+6Xha7vG0xbS2xvsbsB03X4aaJQkJKGSpknsl9AGErVSAqdEsSaySXjpOThx3esFHbHnuFujxkcCEmocH16JFzObQhnB1TtCAV6J9F4qiiI2oYo06cnPvzuLQ/vPrGcJ7RSzFPCdjtc7+huv0Zq19qYKpG+kOLCcnlGmUrXdS1xbjrxHCOH+2+3Sa6m7x2LrjxeJ+KSUUCpHkrEWYUVgjwnHt8842MkI4lZIJXDWoF6O3HYW3YHw/DqghQKd5As6xUvKkpEnJaM+5G4Jvy0IGwLTCqSFiEyDASfePz0yOU8Y2zP8ebIdJ0Ic8AvnlokRlsWmUglbJ7kSkgrIa+gMp1THO56bu4G+v2A6QfSGpDKMHSOXe8YRotyjigcKQZCiAx8xpz//YtTKbV509q9uNZCTpkYIzElSi6fC5GXXeeXElu+VAvJz9Pb1pNuE1vRWKWldZ9IBCV6Tm+/54//3/933v3Ff2BvJTKDoulkfRR8Onv+8/cP5CTZ7a+MveYwGoyo9MbSWU3nBEpZhIxUmRBa4rqe/jCglEDUgiwCQmY+z0gU3XhEWYuuAtt1DLsdWutGoCue6XLi+Xyl291y+/URoRS5JFTJWG2wXXPbUwsxLHTO4GzL/ShE3G6g1zfcKol4f2oUOqXo+2a+FSSMEqiaWU7PlBDQNbeHSI6E5YIoAes6ioDeaIqthHUFFNYNrLMnXK+IZUbEQOwdsRRSEUzXmfkyI6Vmd7A4Io+ffmA5P3D37T1m7Cgo/GXi6cc3fPjNj5weFtaloIxF9YFh95Hj0TaI9dj4TyVH8nKlrhfqdKYMDt31CDLzutJpjV9OaNeTq9wIjpbOeXpTEXvTQpacYhx7jFKEkDg9r1znxJwr2J5SIjmuaJHYD4X7G8Mrv8d2ioOIVN1WKEK2qEHjJLtjzyIKYQks3jMMB+zuSM2VklfiHEjXwJoC5w8XSqlYqZmnFWUMXTeQwpZPKgVVNKqB0pV+p9C95uaVYTxIjE1oWxCxoFVBVE9vRg6joUhF8K3NkdsGovwXd45/h+I0X6R9CSG2T7bBvuoXqqD6+5J3+cVJ+TIcakW6uU7qF2L4zeNehUYiiMvMxx/+ij/7o3/B93/8b0mXJ4zu0ap59+rGIH14fCZmS60D0yoIsbDMEUNi7zKKxOAquq+4liaHlhByQq0rVrbEYmohBc/1+UrX7ZCqPfls16Fp6P4QAiEGrMoYrXh1f48b79jvbyh6JOT2gMmyogRYq1FybCE6OWBUgWzQxrb92iDoXklupEYqyXU6Y9cDrh+Qsi3ISQVdCiknSgxtHaQlzhlKaTxZZNu9ITIxrchiG5irH0nzAlmwXhbkkpgW32RmQvN8iuToyashTpksA7Vc6W0mrT0JRZw88/Mzy3ni+hxI2aGMQJXM9enEcu7Zf/2r1hKETFmuXB8+4h/eYEVgcK9RtaVvGdGM0uSAlj3YHoYdqZu4ORTW0xVtM8J07O937Pb7JvN7+8x5TpymwiVXasrEkFBVMJr2wBsHRY6REhdSOKN0h9WCtEZUgSIyyIQ0FZEVQ3/H4eaOrt/jp5XptGCMReG5nK+E0IzvxbQvmZwJ1TdusxK40dGNepOxwoEe5UT7mArUNOPnCqmiaiD6gF8EqwVMJUXH7q6lAOSUNn7wLyhOpdkMogopNCk3GVNTnNCOyG3KWL8YDInaTsCXNUkTDrVYBSk0SuoG/aJNZ1/6TH995s2v/5Q/+5//OZ++/2sefnrLOi18fSfoDxZtHKFEUsrkkFGp4qsn+2afElXhlyvSLPQGfJKULEhR0A0tSEfmTJSRNRQuc0DJyqAL67xi3QEhW8iMEgqrKzV75mvztSptEMJQsRgzNrdCKFjbsfpIXBYMGdkZkJbOKFgXVI0Nu1kUaW08Gb+GNsnLnuvpPePNgLWiycoqiFTRpQ0PYlzItPgEaQykXXuYSaiyUnVbD8W4YDvD63/wFZOtOCKqZlISrFNhmlNb22j32flSrUDZ2trExXNdJ0JssRA1r2RVyKqldBml0QpqblA3VQuiZnJeqMuV+Hzi+ukZRKSagfvxgIhNq+ucwWqFKYUiIwqNEoZ+UNx/40B1dONIf+zJCtKnCh8qOUMVHf3OkSTEuLRMT2oTbGRBFg1RkwqI0g6HsHpK8AgJwS8UYDzeYd0R67qmcCoKPUyYNZJls5mZIom+kFLAyNay+XUBWdgfRobbATdqum4TpZQMpRB84PphRagF2+3Q3UBdVrRpG4mSM8bCYdfhbJtztPSgX7hK4YVWQIuiK5ts7+cr7c/Cg8+eypcedJtGSSE/A7peOERfmqflJq72jx/4zX/8d/z5v/wfyadPiLCgSqZzGrcTqK6QSU3GV9u1I68BoyRCJXrXQ4yfh2paK6zTaGtbMnPMdAHEKNA6kWTEX1vAjh4MTkn2g8OQyMuFWBJq10J/TH9gPFi00ayLx6+JmAT4tu5IeWaeV/zskWhM12+DIUssKzG0j9dUCbMnBo9frtS0ULRkuj4xnQ8MQwcKSs4k0VirFI9IgZQ8JQTICaFBGYNxDpEWSombI6hi+p6bb17jbMbVhbSsnJ4iS0yUBCGuuLFjP/SIkol+xSiNEh01wxo8c1gYxwG367i52yFZMbS0L9vBzcHSG8U6X7HOIEWmyIK2CukUMTbaesuDyQ1UVgI5TBAjiUQKM5UEJtEdFHYccMOAsBprFGMMuMFgXKSXGtH3LDmQlaDTGkPCmg0uXVvkAcIgVbeJWlYu13NbeyXPsNvjzIBWDusGvJ85n5/R1jDsdxzuI1IszGdPiAs5RYyzLeaP3AaPe0u37+i24pSiaYKXeWZeVqarp6IZ9woTJNfrSrcbyNmCbJPffjegjCAljynD760M/17FqbVu9IKXSW0p214nfr4rf+arbybo3/Ny8kV8nxCojUbe/uA2WK+Jy9Mjv/2Tf8lf/Yc/xj9/QoYVP88opVrP10mqaslQJRe6ruf+fmC9zpTYrs7WVvzq6Xaam72j7xTaCnLSCBRh9QTvITc8hu00N8NAiR6ZItZZnJaIOLPMEZ8jcIcbDyi3w4wHjB3ABrK8Er1Hipa0HZIn+ZmaMsrothqSkkQip8x0uZDXhEZT1oifr6Tkcb3GdQapEmE9E9cDqmZEkchaWviuyGjRAl1rjI2ToxVVJnyYicuFGFs2je0HtLaYsSLqgK6FLFa61NGPPT4kSo50WtE5Qw4zMa6UVWEWjTsMaNvcJd3hwHgjscPIze2V5D01JYbRMex7hrFDyJZkprVEDBb17R3aBNbpzNA5YpgxRjL2Di0yNa3k1OI9QlgQNWF7jewkbjDYXUfYzBPDznHzes80FcxqyUqQ50x1EqfBCEFvBa5rCM0qNdp2aNs3LKX2nK8/ES9LA6kN7fSqOuPXicv0xHy90LmOruu4//o1Sp7RemnMqHVp71FVm0DESYSFqgto0UKHc97mLQrtOlzVUFRDlXjP6iMBTz9F7C7gyCiRkcWT/dSu9+EX7jm1MiilP/sqX6Lcfw/kVfm9vvNl4PNCNBBi22l+0X+2a7JA1cT50yf+/E//HT/82R8j/IwomXleeHg+N56L7uii4HC8wS8XUp5QNvLqlaFGh/Cy4RVlJHXQdYqbo8NazeoTp/PKPENOghgTGtgPFVEKtSZKzJSYqSHgZ8/uuEMrTRaSjKGqAdXdINyhCbdV484oFdtUeUOl1JyoKVNlU05pY1DOcsnbAG1dWGZP9S1kx2iJ0Zp+sJjBUcvK5fkDpTtgpGlRChvioypaslduV8nwEsqjHUKK5ic1GmtdG6nJFocwdjuSX/BXz+EwcFlnakw4UxEEtG0ZnImMT4lcJf3xgNWCftejjcbuFnb3E36dqDlitGmT7vGA6AyKAikjVcWOHYdyxOpmDJDULXWt7QlFabSEPE3ILb3a2Gb/QimkaXOHHD3aSO6+uSGjuTxl1gCuH6jRYSRIIkMv6HpBvzNI/UIVMPiQCalQSsuh+ZxIV9M2UFqIYUYB5EqqGaU1bugJa8asGi06fIxt324VatBIo7cbSwc54teFznUcjzfc3Daq/jKvPD4/UyiYTiKcJSFJqSWfEZcWDlU0+DP811vO/9a0VjdekBCbhrZSaiaX1AqybNAlxCaAbzaxnxe2bVDSNHx8Lta29kxcHj/x5//uX/KXf/bHmLhSU6SEgNIKuaEap3lGS0enLKUWjLRt0a0r41AQalvmmkZMUFqgTWm93LQyTYGSHTlBXCvRKS5PietpwdjaAE2+gC70NyvHbwx2HFpUvO6Q7oC0RzIdZEmIiTWAqi2GQoit0ant6r+sK5TKoFrK9xpXlNXsuhvUIXF5eMBPHus0prMNneI0IUfm6YRMuWETa24TQadBKXLZ7HhCUHNEqmY3yjWjbddE1kpDbZjImHvQEd31SBMxXabbC9JaSWLaTk9LbwZU36H6ATWMqOGA6Ry6txt9cMDsD7gcYDM5KKnacMuYZt/KhVpS4/bk0nCfFNYYScmg92ML400ZHzylgO1GusORLBSzL6AMyo5Y1XCkBc/9tz2Hu1fMp8T1vJLTFtKbAilMWFcbsPmmx/WNau9j4+v6mLm5u8cpRc5rY8ciyLlJEZUQlJC5nM9oN6J0h+t6xFEyXyfWJeIjxFoxUtJhoGgkhs7tULVAbPlBxhi6cWy3QyPJRGYfuBmOyO4W6XYUYfBRwrSy1x5nB0qJlBR/WXF+Sc3Lpakaai20WLTy+b7/5Y9aC/BlnMLLuuQLeh6Vdbrwn/703/G7X/8pOpzRJRG9RyNRRnGz7wHFNC2okphOzygJSwloWXFO0/c7YpnIKeC6AesMVdQGsEqpQZZlg2P7tbnqc1TMl5aE3I8Sg2SdoFqBXyHJgXH3uilATDM+V+UIEUQuxBBZZ4+smRhWkGCMREqLkAWExA0d435knSa60UFXuRl6+iqwujJdNFI3kbbpepQxWJ0Ji+d8fiSZld1uaMwa0WDMTT7Wbi5aKYRUGKVxw4iokFD4IsgionWH6Hb4OqF3B3avJdZ5XG8wi0CpFgPQdz1md0D0PcL2VNtTuwHZdQhrPj9oX5RcNRcojf0ryc0NIkAWScjNAu+MJeSmJb5crkhruOlHzGCIMZGEQO12dMc71HgEaRmFg6rIpSCKB50bnsRpzNBRxUrMC3GNRBYQAakiUhfM4HCDRts2wU61UIVoUOxxQKFY12nLX3GAbhmwaK5lpfrMmla60bIfD6Qlsqyep/PMvERiqZg1caMUUhtyEERf0cYw2BHImwG8tuutMfRDjzCGQI/Z3Tc6vdIk1WIjfdGUJFDaoH9pythnjk8ru0Y9+0x03+zq231WvPScTeX+8wm5id8lLx8XVL/w8Yff8PY3f06eTzhVyYsn+7U5G7Kn30bMVdfNYyhQylGKwvvIOge+/eYOn+btaxXUnNrpXCUkiZWWzlTWOiFqxhmH3jypoFAYqJBqaA8R23iubn+LGQ8oO5CqJIcm+xMFgg+kmJo3Uhhiip8j5U2vUcayOx7arq4Gyr6jeLDaoLXi8NVr9OBIqcGcjbUgFTWLNhgLM8TcJIVWgXJAWw1AS7g2RlNqhrCirW3hQTSyO9K0dY1s00zlOkx3IO5XhuNCHyakqKiYMWqj4RkDxlF0R9KWas2mXBFtMtpEz7xIwGSNiFIoOZBExQDUjMqVXvdgBh4/nnl6ujDe3VBpcYXKQLfr0G7fxPC2pwiLUB1KGPI8kcO1WcRqJc4rVcTNBTVzvZ7IMXLYjxjTkaqnikytiUpuURJK4QaHLAaFQGNRdqQhnw0pZDotibMn+oxfPKkGlNBE5bheJ+Y1cJoT0xxJGUwS2D7R9YXoE8t1JUtJiZ5amhWxpozpu2Ze0KrF1+ctnrHbg3T0xyO7fY/pLWhL1R244ZcVp5AvsXytwGpp0r1aWjBtpvw8bBKb0EBuAagb67WZjvRnK1hNng8//obf/um/Yfr4Bi0iOReST226LCqiNPpCLZmxd40zu0ZyrGjhCLlyvlyQPFKWlb5rrFGMIKyZdSksc2FdMtknLBKhwGiBNQLnNNpIOgMpBoQrqFFjRot2BuU6lNtRVUdc/BZDECBD8P5z34y2jedjHJJNOjj29L0lLVfmpwc+fv/XiNjSrQ73r9gdbxGuY76eG3t1C3NNIVCSoOZKKp7lWpBGUDmQRSYvsckHu02NUzL4KyXbJsYWmnG/pwrVwGRaAxm59cCqj+hjRPlLMy1PKzIqMB3KWZJoqyKsJZsmGCGVzbq3WQNrROQVkTw1rtRlZkmRZHTrs6HFEfY7mji/p98d0NqhpGUYHVUIMoaUKzkkqhSUUFBoSlwpJZCBKh12u7VFAsZeGAaLqI7dbodUsPhKLoEYInqdQeg2BMyJkttaKkkBdkQqizKWeDqzhJlpWplmTwqtr/TThRwSy+LJ203ApybckDTSRKmVEDz1WtFSEZeVElf2g0XTVjhmdM3sUTQpGaIv0Fc6Y3HDgX5/gxn71qoIgbLulxWn1l8Mg2hX1pJzSx37AltZN+q6VC17UsjamnAJVYg25pYCReL69JHvf/0feXj7A3G6YK2i5IyqAqMtSorGJMqpfVwqhHT4AsFXqJlljpAlz09nelWw1pCywFhHrS3CPUQ4XReWJSARyCbtRZSKM5p+FCi1oruK2DvUfsQddihjt9O/mZJDaEbtvHpEzpAzNSdiLfTjgMwa6yxWG1ItCFEI60K8TqzXmcvTibp65GVG2Z7xcIPu9lh0w5MYi66VnK5Qrm1VUjNxTcSlsK4RKzTEwjjukUYhosBJSGGi5gXpdgghGbuBLB0pt/1tCHMjCIjAywsi1xm/ROJ5IvmCdBG3P4BSTSxBG9SgFJKMIm5iiggpINMCfmJ+emL6cCaFhB06sizYzlD6HlEL475D95rXf/A1WasGiVOm9eXziXWOxABKdSjjSKUNZrRpAUtVsLGStyFcP+FqQQpFFbLRIra3YA6ZvGacqUjRUCTJB8zuiH11Q5IGtCWXijCSy3lmDiuplgawS4VQPbXq7WaiMKpiGlqJftDt6twp0IpYaht6akXybUAXYrs56KoQsqNKTa4N69mLBvnKuYJoNHvTde1Wqv47tLUvUQy1lJ/x+Ntttm5DoZ/1QS/7y7oJEJpRmm2Zm/2FTz/+JU8ffgA/Y2tGhIIqFbWRr7XUm8FV4pcVpTWlSkrKxJyJKaOkYHcYiH6m7w22Uyij2sQSi02Z8zQxR8m8FrQQGCkwuq2DlBR0XYN8VWMYXMfw6ltuv/0ON+5ASHJKeN8Ks8TMOq+IGDBKt1DVnBGi0PemKRdzIAZPzKHxT2uzA71+/XXDVEpJ53qE0EhtcLpHG9d2wCWRtrxPUTKiVpRpmaDX8wU/LVihMc4xmlus7SBte0IKWvU0SGBE9iNGa6xt2NG4XppoJHqW64nLh/fMj4/408wyZZTbY3crqpPcflPo9D1a9A2NiWxYkJwQOSKyR+ZACgvX50ce3z6g0HQx4UZHEZUkGwFe6dowlGSs60gIUip4H5nPFx7fPXN9WBHV0g07shLgBLu7A+IgQLXXQMaGXZXKII0lx0wI8bNqSkoBpV1gSwLTteTuGAL+emZ49S0htLiQnCslhkZlTAntHOuyknN7TxsDxmr2+555WZGmUpTmeL/neL9jPDpc3yOlbt/v6HEWSImQMw5FlRopNclXQoaiZDvRTXs4XK8zRWnuhh3G2hZn+d9dnPAzyPnFRC3EJn7/UkO7lapopyab1UfWxPTwnk8//BXL80dknCFHQixoqUFAiBFrNMF7hLMtkkEKcoxIK3DSEK6BftezHztSqI2QbsAMgiKaOTbmzBoj0xJYfcYq2Z5yueA3jmo37ugOhqQDenfDzXe/Yrh9jenGJsVLuQGdlSJry7pNnruhZ1AD03QhxwWFbp7JNeJjY6jWkrFKY4Vi3N9QtcMNPW6/RypNlYaKQiqHAGJYiXEFCkprRKlo43CucXRyCNRc8DmSpUC7gSpzw2lsgTpCaiQZRGlgKyXphp5aV3Iq5BJIcWJ++sTlzSeWU+RyhUxCugl7BKHh9X7A1h0KkKI0ZEqOWwSghxxIISCkxN0dyKmidwPHuyOiJkqaSX4lx4RyPcs0sx92aKlZUyGnSrpmnt6ceHy3kLzE9TP3396zux8pSyDZtcUq5kROFVEFNcUGS7sGwhqoKSFFxRhJHQ2paMLk0W5ocwVRmecLvPldM3pLTUy18XaXdUv2VkjVoidqroQQscZwuNkhrGS3rFSt6Y873GBxg6EfBpx2Lc81FPbjnhTjxloaUNpSlUHoQhYZxAsovcXZLz6g1rBtOv626vs70vc+Fyn1ZyzJy7/b0q1+D/1TBXVD5YlSUTWDD5w/vmF+/oAsHiEymZZ4nStf5HyuVFGxgOtb0rF27Q3tU6WjIKwgC08/apQslBqoUuJjYvVwnZvAOaZCKqVFFdZKLBVdK0uOZCMZ7o4UK3DHV+zuvkZ0PQX5WUthpERtxcS+UR/63iBlASJpvZKuV2pKiKwwFLxfSKWtBoTp8TGzzjO3fYeuFVUyUjdQVE25MVhLhC3jxI0DSjR2q7SSnW0hS9knhNV4SusJq6aarkVHKIPUol3/ZABZKVU2WoFTBC/QvaHnBoQhLqWlgnnd6Hv90OBgywzZo2pur1leqf5M9ldE8ogcoGRECQyj4/jqa+YltLDcrqP4mRKaBjYsieoXetPjZ48zHeSKn1aWx4npyXM5J/wqMctKNya6PlPxWC3IIlBqRFRBDoXsM8sceH66Ml08Ask49gyDQxqD1XvW6FmmQO9agl2JnuvDW4zrqNoy+8ZqakAlRRUCbS0iSXLMn00d3X5AjYah7klCfAaNSaMwVmOtQmZAlram7C3G9E2DLgUYjeskSnmkbg+KmDK6bnElCEqqlFQ3RtEvKE6l1RfALT6bqV/WJ02eBwi5ffylcNuT4sVlInLi9Okdz+9+ai9ybW90bS0KQ04VUmTc78glbwQGSRVtOiqE4HKdeTjPVARDr4khk51sSiAtqfXnCHhQpFiQosn41IbaNJ1jf7tjfz/i9nu6/R1q7HGHV7jxllA3ofm6iQRM9/kJOww7impVWzcnfshLs1LVSt/dNJapUq0F2EbrGEvSmsu6UIzipuuwG64FCkpUlBGU3lKzRUmHsyO5ZHxcSALs0FNsaXECRrIUjwK0HRDWEAFkReuMsZVUYxMXlOagQBSUNdjhhv3dE5fhQjpFipXEErGDZn8Y6DqJVBVBoqaVGi4QTug4UcPaTs9S0bVhKCWRzmqsaS6Sea2Ncg9IFD4U4hJxOyipeSXmy8T0fCasgSUWplCQqVB+eocPO7791Q2lS8z+jJAZg8BfVpY5EAKs58h0DgihSYsk9IIhSGp6AA3iODCosbmbcma5PlNKj3Q9YEBZpDYIIchTKxqjmkY6pUiNgV6PbfKqJLEW4sbgMbaFXildETQhf0mxEd2la86jXKGYbY1YW37nC/1DSLQ222qyoFJjAf+i4mwxfRuy8oviqy+w37JNaF96TlFaKpUoiKqgagSC+fkj7/7yz3n84XtyuCJqm3BSQFbRQMZbIRlpyDURSyLlCKLirGN6iJwuGR8LY1/oZGKxgrsbw25nCCUipaa3lrwzXDpPXD3FNKG91pLh0HP37T33391yeL3HjTvsOKKHsU2lQ6LmFghMjnS2cWUT8TO2BCy5NGK70yuy3zFfnrmez0jTQluFUC2sRoDuOw7qBkpFaEu1HXa3p2ba4CIuyJzoXYeqTbyNFKSaG0BbaFDt+iqswbqm2upcT14VqndNx2wkShmkNpiSSOvc0rprxmpNmC4UCm7X0X11xx6NOE0s84SUj4zHrzh+/Ro17qgaZJkR6ULJC5RIXCdEasGwKVfWJbI+TpRqWhDvzR6jFdL1yCKJaaaqihAWYUdkf0TGSBafSDk3aNdeM4w9YfVcl5n+UvGrIUVJSRFBJqZCuM6ENZCqQ5s9qSzkKpl95nyZ6S+R7nFB6cr6esTUSje2kKCQK3G60tWM7o4tXU1JKI38mHOmhoSPgZgTVjfinu0H6tZiKNGg2lYLtBIIkYFAqQuQmgjEaHKGkgM1LkihMJ1FdQ7d9yhtG+9I69Y2bdfaFx/037s4eYFv1c0itg2EGjFuk+xVPoO6PsO6REIRULlwPZ/4+Ns/5+mv/5JwemrBtqqSlUSwhQqlTCclQhSMUeSQ8X5GKOj7js4alFSUIpvwfJ4ZDQQlkbkgq8LawjgajK5YVxgHyFFTskBph+0dbmexo6U/NOBwjIk0LxjVo6oip7Sti9ryP7uu2dRSEz6QM1WZ9gBJK2UthLXy4f2J89NEP+64ff0abSy1Qi615cAojdQCN+www4jdHyBVasmNiVNapkatLSI+Rt/yRURtBadaZLruDLvDEWX6ltTcOcqLtE+UFsCUIpQ2rY05kxF03YifZ0pKuOPIzR9+x/jVdzx/fGI+feLVqx13390zvHqF2B2QSoBfCSk2HE1o6wqVUivMkLnOHh8Vbhy4+IgIEUHFDTt6d2QtH6mxoLo9yu4oQmOc5u72jvXwAbtkvn31in/4T/53vP3hRz79+D29TihbkKatHsKy4MOWAFehFMnsYQqCxQdKLThjuKwr8umKdTDNExX41T/6Btsf0fNCCGdiSqgUETIT49wyfnJEiUpJzcivlKQfHd3YY/qBqgxKNOcUxSNrQJERJZHySs4rnbWMu5FC11ZDG7alqEq3G3HHW7RrrKndeCBuM4+cCvW/Hs35dyjO7UfdivFlStlUQu3uXMXLgnobygqoZIgXLu8f+OE3v+b07g35dIbgtyFHJuW4RaL/zIbJJaArKJXZjYamjcmUlLg7HrheGvmPKhiMJK2eh48TRhl2e4FSniAC03VFyEQ3SHIUCAW7Q4cbHVIVKg1S1kjitMwPLJVCjr45bqQkLoqaWgKYsRolHSE1feb09JHHH77n/e9+4v2bj9Q1NMo4mptvXlNU85gaaZAbIlKYDrRpT1BFWzuQyasgptT64hS3mIQtJ1NpqraofsSMA6rfYccdVptGR5ivQGmrDgHT6QOypvaE7ka0HTFdhw6N4tANcHQ7lOy5+W5luTzS7xRu7NBuQGjbiDPGtRtFjSzLgveRTgi0NCidUUayO9xx8+o7fClEEdBKUrQlR0nt9wz7DtcfQTpybAC33XDk9XffEZQDO7LfJx7dxN0rxW7ouPnqhm40xAQpemJOFCBXyeoLz6eZ0xSYQqLUinMQY6CzmvvdjqdpJf7uAZ/hH/2Tf8Dx/juen9n0tBHiRBGS5OdmL0wRlQOdzKhdx+444A4DwjqEcfTGkkvAr5maAqK2g0dIMF2HMx2ZJutsel5JEZpUJKYbOd7cMY57xvEGpSyrX1vLs1kp/yZE/e9ZnD//4bLtN1v2ZusrqW1QJF7sYhVyiHx894b3v/5zzh/fE6aFMoftStBw9V3fsd+NkDM5RhQFJSu1JrQSKG3JJSOlwl88Kmv2nWZwO5QqOCWZzlcu5zOtO1f4tWEkoi+QQW+9XUOrrDihEKqgRPuLAp2xyH6kakPwgfwydpeSqUZ2Q0cOK9Mlo8wB3Q0s05UPP/2WH/7Tr7k+eD6+vdLVSI2F50+PvPr2a4Q1hJhAaZR2KASFNrSa1pXROqRuTgpKIYRARrXv5Ka2KqW2PZhSBCGwxlF1I8wVNtSLtlAC1Wf8MuOfPlDSih739K8GdN9TqyFXR66t/9T9gNED4+GGeL8DlckSSq6ougFjpEF1I7IW0rwSp7U5U6Sg290gBgHuiLQGUwu5ZkqOzMtMTY1ztKwL0yWziAvf/uN/graGKCr98RWvhGZeF66XdzgXMXeOV6/u6PZDC+2VNEGLarS7mDLPzyuPp8ISFUuuhJK5Zk+qmUEqdJLEOfE8rRQM3/2BYbwdcGFhvWQu5yvUpQ0ao6fmQI4eET1CSrrBYcYO4WxzuWhD1/UEX1uERZFo0QBmUndYtwNhmOfCtAZyejF8KKrp0HZA245h3OGc23JomyLIWMPfpIb8guL8eVr74g6rn4UHBbVJ8hrPtqmHnt6/5/1/+nO4PGFSbozPSGOqDD3dYHFdSwZep4Vp8tSY6DvDbu8aJlNISq6UWPDXmes10ameLCPjrmFIDuOew23FWtV4r7FCVsRFcr16KmCspeQWP4BKDAdDChN+CRQlUVYzDHusGSAl1piIfkWISmcE67XZe2pMVOXp90euT488vvmJ9fnMfIqoKskhUpJuhIESsLan01uYT0OjU2i0/Ol6pRqP2cTqWWqKNGA6RC7IKqgiUxCNmyoVyjqGfWP3xOXScmukw2gFXpB8YH1+Jnz4xPVyYvf6K7r+FVVHvCjUJFFYBJm0IT4BtHVUERtYy0fI5fPqDGlR3Q67i6yXhct5ASrHncXsdlTVSIcqV2pqGMziM2EqLKeJy+PM+RJhd0N/+xVf/+oPiBm6veCb3cjDxzes/oo49Fh3Rz/uCMkzr6EFIReFqJaUPatvcrp5KWRtKApSraTctr1KaN49T5R1ZTSS8RT48O4Zd+gQooNqmM4PUMTmHirI0kJ9MU3nvHt9h93vEMOAECM5F5ZlJYWVFALkjFRt0JgyhJTJFbwXLGslF4k2DQmTlaQzA64btsJsx5fWbR8vpNo2HL8QU/J7IUP/ix8VSgHVVDfUTCmR86f3vP/Pv6ZeznQU5uRRGvbjSDdYdscBZRWn8zMlV9IaWS4rYQo4I1kWj1QZo9upnGImnK8o5ahiRaiAqgGJbnjLwbVrd8j4JUGEHAthbt84VX9+kCTvietMCC22XAlDTh21rAh6nJVYJVlSAiVQCkipLeLDis+xUQDmheX5RA2J4ld6NyKUpB81/aDJZUWbG3SWpNreAEKIpqwqmVpS84KKSkEidIfQCSETlUQu7comjEZai+ks425oHkoKq7+QigE5ttPVL4T5ytOnT+RPz5yeHqhY9l8F1JjQVre80aKx1iKqp1LJJSFp9H6RNVbpz1pVWQSiKEx3QB4ky3lBTbHFchzuEMMASGquLQIyVcIc+fjTA6eHhfkc0NUwXVfqnPn444/c3+2RJVLIlLRiTEbrtvQX7oAyPfH0CFUzXWbC1TOdPH5KrCtUaRG6EF/yPVVLWNfbsGy+zJRQ0EjmJfHjD++xB8vxIPFr5vI8YZXFKIu2EoulDC3NfP/qjvHrryn9rrlIqkbU1B5WuRIWD9VjekMRjoxu781YiQFiBKRiWjNnf+UwfIPtR4zrES2FGEGb+CpjP2sGfjHgC14EBeKLFcrPFHcAUcsWLpRZpjOnDz9h0hWpMn6ekDpz+2rkdndgGAYShcfTI09PTzjjMKI5+qtvn3CZAsFPuM5yPN5wd3PLOSS0siQRUUZibQYSOW0L9ypACbSWpNh2kH1nqNWipGmgY1mRCBSiQY9V/byvrMUT/ISRLa7NOIvSipoLRkAKkRwCfp1Zl4AWluPugFpL48wIzc3ddxzuBtyxA5FZliupWuLLVXXzFZYsoZqmopICrSxKGaJPVLGQsyKnpqxyxtKNI3YcN3P0Qi2REK6Iohn7DtNZ5jVTlGJOiadPT3QbkKxSMZ0l1EYjL0qjOtHM5z5SUyaVRM0ZkWvbn6pN51gLJSWWlMgFxrtXWNu3fqnrqMaScuMvLctKjYllSjw+TJweAyl22KrbZNZHPv3wW37qVsadw+x2DJ3E6CbRzFJtkYINO3I9LXx6+4g/r2RfSUEQs8RYx+FoSXNCKcHsI742OsayrG09oRu9YA2Rd+8+IIbK/+F//4+Zp0DOkoIgrIkYwEmHsgY19IhhBNfcOUiNqAqhagsNzi8qJQVSU4Qi5URIgRgzq8+ULChkPjydKHbPnRuwrt9g7C8xJM0T2upK8F+f0/6dirN+rvAmgN6mgmJDYkqx+TkzdZmY378hPz0R54WwXoHIYd9zc9wxdB2xZJ5PJ+I8M0oYLVhTYRQEpymxpUvJpLndjdzeDqS8Nv6pLlhVsb3BOLGhPiaylDjXI6rACEVpSUrYzlBLo8JZBdQmrhZCgeqwuyNKSWLJ+PkRmyN2uEMajbaG3khkTmhRqQlk0XSl4DqD6nvc//ofEf9h4M0PP6GEZPzqnrHvUSREKcTnK1koUAa0pjpLS21uVq+XR5+UklwjqQYymSorUoNSEmPavtdIgcqR6iOpBFKMyFqROVNjxvYH5HDHr46v8RHy6RPDzY5xPyCtIq2FgkHKTMyCUiUKiazrJrift/7HkLNsJ2cOyLQ2Rg6aqCSl70jrTLye8CGjQmKZTzydHzCmx4gB240c7joe3y8EUZGj4Xjb0w0ZVS9oWlIY7MlFUNh2sTkQfGa9Xnl8/8zTu5m0FEQWlAxJCLISaCHRpSC1o9o294i8OKUaeVVJQz+OnE+PuE9PBP8P8VGDGggpkk9Tax16QW8an3aeLGb/GmOa4KBKhZIvKp/QQpukJWGhSGIoxFUSYiakSt6OwYRCuB3S9A2J6dqk/YWa8bk1fDk5fyl9r92Tfw4mlfJlp1k3/H0jChA8l4/vePzxe5bHhyYoGB273RHXWZTQLHPk8Xxhnq84Cp1TtGzRjLKK25v9xthZKckxjiMxt9PKGoNWsPgFKSRGd1Dr9mtLjZkc2gspSkWLjQbQWUSBFCNTWMkxb/usQs4KgSLFhBARayRKGGRdkTmzXC/kdSXOCx/evOPydMZqw1d/8B2vftUz7Pft4SUlVmvoNapAuiam5yuPj88IZzHjiD0cUFoCFal004nq5iYpNZLCFclMbzJSR0JcoRRkBl0qdbkwXVNbgncWqwXDOJD8gkJg+j39/oakDP+b/9P/kaff/ZYSC3n3CoY7pC6IeUHEayOT14rKiRqvpOWZECaUcRg5kmshp4AoCyIu7Y0kB5Atav56OvPw4SPzFLBZYWxb5EOm3zm++u5rnh4eMVrSdUMThWg4HDrGfdcW+KrhHarQhLS2MCA5syyVy2ViWTNVOAoZHxNKqLZmSitFNsnmaT4jrOP1/ZGrX3k+nak5kkshykIIipQj2SvOj2dEVkRfyNuNwWmDthWdCiplRArEMCNKjygSgWlrkdKoE3V7T+VcKTk2ukVo9sFa23RfaYXQhn7cc7i5Zb/b4TZL4Jf68xejyN9+qf279JzbJ3251soXf19pjnyVAsvTA08//Y7w/EhZrgzOcRhGtHasc+R8eWJefdO1+oXjILj9as/h2CF0bihJUUghonVbbyiR8JeMqgrjdIvkqxVnLZ0x7Q2Ua4vZU4acV6Jvv9/Jxtp1fU8umUU0nKLpmlWslMw8TYjaTlalDKUqUqrUEEjzleX5xOXjMx/ffOTh04noM9pophWq6fnaOvr9wOtvviLlFpt3fThx+fDIcrry6eGRbjewuzuCKuheU2uzdFWlQZkGK0sRskeEmfD8xPT4SPJXpFQUbyEIUrhQU+Bwd4s63qDdnhwmYtV0RiNFZlkueH9lefwdu2OHckeuKTPkiogBlT1pcwzldWF9/gTzMzlcKClipMYIMNpwjRNxvSDi3Az3RpF85undI9//+rd8fPtAqZZOOw53PcfXt+xvD/TdSPYBa2/IYUYqjRsPLKunH0eU0aTc3P+aBvlenhZKbUqgsHqyz4jSrp7XawTa7YGcMErT7weUT8RS6I97xuOB+MFjSwEh0EZjlMCvE0ZLVCmsl4llurJc/aahLayqEvLEDsXeDlghKaKFQclq0DW30hGyWQKVgs0y5peFMC+s09Lsk1q3QKpaMdZxe/+K4/EGY8xnnbmUP/NIvjwtf/EqRWwq9heL2MvJ2axjFZ0T/nLm8c0PxMsTpka0aUVQheDj45l3Hx6IuXnhJDA4hda0oY8rLfVLtRu41RKyxk+Bx08PLNdC9oJOC1Ja6ceOvutbmCrNWhR8W0Akn1r0dWn7y1gCw4YbEVpyHI7s7nf0h54cFs6PC0I00sN+36NVJaUVv1y4PDywPF14/njm+rQiUgunpVbOJ8+P3//EsO8QpqJ7B6oynx94fPeOT7/7yHpa2sDJGfArMns0tQkHxIvYoH3dxESeV85vP/HmN7/jw09vKdlzPN5inaIbCp1NCAJOQO8cscC6LLjxiJGF5CeEqyyXB3I8Y4d77GCZleJ0euCoNTKvSGUpRRBnz/TpmeXhDTlecGOP1h3FOdAKVTMxBaK/4oylCs3Te8/7H595fOf5+GMkJTCdZ8mJ+3/wLd3+BiEEYblwnZ+oaWZ/2FOFQOpMFbAWyRrWNqVcWzy9dgMlFWrUEAQdClsEIuSmRdZNpVZKRQsQOaFKwomCqYUyz/jnZ0wuaNnaBlEyNReslRghmU4ncvCM3YD3ieADBYdPkuv7iawK471DVEktrYWTVNASKRxCQAhL4x/V1qcv68J8vSKFxPU9Qil8DEg5Mo57rHOfoy4/Q3u+yA16Oex+8bX2815T/FztjXiwidmTZ3r8gD8/4hTY44gUPbrviUnx+HzhOkW061iDR9bIq5sDX309MvSRlFdENWipkVu2YQiJyyXz/v2JuoIRFtlLhrGj65tTJaV2jdDasKwz0+I/W9NyrMSSEIJGGh96jLXYwXLY76miMM9XYk5QFdoqLieF6yy235PSCoKtGCUIh+k6QoLrdKUTladPH/nwxjLsFcq2J2OcT5w+feDy+IxIksN4y27sGsxaVGQtKNnEFjVHRKIppJaZ5emZD9+/4fv/9CPvfvpEyIldv2Ct4O7G8epoMSahpEf3E/1Rk0rCGkdar1TpCdMJKzLcfI3odhS3w+3u6fs953dv0KHQOQG5kOfA49uPnN9/gOK5/0ZyvAcpWgamkg0+JnXLCAl+ZZomnp/OzBcPxSJFT8xXTNehOkvVmlwjVRXGQ48oGmN0U1qVlWXNpCxbfIVRGHtEZ8luvCeunulpIi+Z9XpBZM9hNIzWtCuzymDlNkRbGFx7w4f1TA4LB5mJMlGF4nDYscwzcU1YqVACZC0M40BOiXVugUvGDMxr5ukxgFrZ3U244x7X5ybZc5tXWRuqrM2ckAJCqS2EuBHblRCI2uDZIRWyA7MlaX9O1pPyc0at+GK4+jdNJH+v4vx8en5R9S9id1kzYTqznB7ptMAoAwW0VOToefw4EybPaEdiKhQfMLagdcE60KagNFhjMXYg+sr5tPD+zYnrsydPmk5IhARtDaZ3aGfIIjWrj1T4lFHOYaVuQuo5UmTBjT1dt9/8cuBM3/iqVbJOM+u8EFMmxoobHMMQWK5XChIpFN04ootlfyNAZmYvIFZMymidiesZf32GvODUwOxXkr+2QUctG00OUODTAqulzwFKQpSIyIJaAzl74vWZ53c/8OGHH3l4/8TlArFa/JrQFMKlMn9aub0z2JtIHxMHbdFCUmNzkABEv3Ac96D3FB85vfsRdRvZf/Ud42h5fnqPLAFFIfsrl4dH/HlpILFXsPoFNXQgzGZy16AsIQRQhvvXr/GLQKaKcxPLWtDDnvtvbrCjRThJzRLTG7Tr0WIkxYBf1hYePEceHjwpC7SFr77Z042Cw76jppnlesYvF/Y3HVrBk76wLoGuCmxvtp693cByqdxVy9PzQoqBw72gYkEYusHyMV+YU0bLiDKO480ekSNTWBpuFMXj4yfOC5AF09VzfnrkeLW4sSVuvxABob2W2mrAUmUlV3BjJK4RWaBW0VLMEujRYF2HUmZzcjVCglI/950vhfm3nZr/zeKspWyZhJsvcwNDKyEQOXB9emC9ntCxkcvnZWrBOijqmhmUAyxr8ljXcf/1jm+/3eNcwLqW6KSUZb4m3vz0wMPHidNDoAbFqFrWoRIBqSTzMhNL++Yb0xNSaCecaUJwHzLKtqAlrSymd4ht5F1ywSJY55V5nlnngA+FUgXOKIqvrJNHmdjA1QiG45GaHYmZ6WnBKEPnemo5c3M8MgyWmj0lNY7Mbtfz9bev6M0BWQy72z1rXXh8fMKlyPjqKygZLcBJkNmTlmfS+gB5preaoeu5uEqIgkRCUllWCTFR5crrqhsekvZil5KYnh9QSuH9Srw+c37/wPnjew77PYdvf0Vc3zZL2XRiqprduMPKzPHQsXyKTRdNi7trb5xCpSFLcjD4ELGD4/71HV03sOsl19OZECtuuOHw+oZuP4CWLSrDGNQWqtzrHfM58fb7E6fnytt3CwXLd//gjoePbxh2jm//YI8WFzQR4xS26zHDnioL10tbjx1uRrQW5BIppbVUYS3sxp7asFGkVIhZ4f3CYaw4Lek6x/3X99zcHUjzhRwl1kpSqShZELLNUHKKhPVCWntIO0g95AqYZmesoI1sTNqiqU4gDgdUkcyXK37xIBUxFKxULd5BNY5WKWXTAdAke/UFgsfn6+0vKk6VKrLUBnQWpSVgASZXnj994P33vyWfHpFxbXzVCsYO1FxBeKSqRB9RpTJ2hoOzdEqijUR1ilolwUve/3jl7VtPzh3TvFL8it05MH2DJ4nG1xHa8fyQWeOE0M2cLXPEKomStJ2hbOP51a8IIVvuiXMkUfBpZQ6RNVWyaGlRUjdSesmBEpemfzWaNXm6u57Oe4YrFB9YpomQPfrre8avf4W+uWP3+p749EB3c9NWLPczORaUNJRroVt6qm6pz4aMTjOKFREi+JkcZoSomM7ges2uTy2aIhW0biGr1Ul23x3o7u9wu3vMeEMtC3W9NBnamigXz+l5gWtg/fiAWVeOvSRxBXtEJEWOJ9YaUFJx+/qG5C+A4Par1+wPr8iqDUWUEqisqdGSlgVrKyUlhNH09/d0h5tmBuz7Ruozm9qlzMRUWK4TXa+ZlsjlYyBeHX7K+LBiDz1Bj8xTxH+4sD488urecrhR6IMCWrJAt9dUbZBC0o0OYQRayuYnrZBDIq2J7As+JNa5IrKgHzWdc6ypBVG9+nYP0lPDijCRbigIrXBDR3fOrOuKsbVBuaREyozIV4SfQUg0kooGYRBVIGXFWskqegSyhUsF38wJtTB0DqtlaxFaFSGV3Rhc7aFVPru75C8PMiq1idwlkloKslaU0pxPF/7qr9/y+LQQLoGeSicLg2uJWXZwVKnw/ooPc9uLWkOqV6ZpZjzu6PqReVqZrheenh8oUZBCYj84zn5CGoUdBMJUfAoYYwll5fHp2nqXNVJSy/vUEnaDYxy6hkdMkRAi2lqcOXD/7bf4FLlOz+haGpcoZ6So6M5hhwFlW9BsVYacK9oaptlTVGW87ZifZ/Zfa7rq+OoffcXtd9+g+h7hbun3FlkkcrkQORN9oKSC2Sm+sr9COsNxd4uR4C8f+PDwhjxf6JxFG0ONAZEjWiRUXZC5cHPY048jJQZcJ7n/5qZNPEUPQpFrZfUBXT06t5iH+flKOiXCVCmDZL6sjWjw/2ftz34ku/ItTezb8z6Dmbl7DIxkTrehqmpJL4L+f6gg6EXorpKgVqO7a868lZlMMhjhg9mZ9qyHbWTdklB5u1kiQCAQJIPubmef/RvW+tasEfYE2pCVoijF/KtvGJ8ulFLv4nqPcgNCCfb1lf24kQ6ohyCKyDgqjLb4U6+glFLUJru8UGv60s6SCtzWHas9LfWb5eXrC7elExhLTDz/+BVZMuU4KC3gZERLi5INJfpKwg8G53tgkHW22wgznY7RQDXd2cFkaslk2diOjVIr0grmyXN5emA8mR4HkQR+1DTpkKFRmkI4wRAs2lQengbcqJAyo3SltR49glBIpUH01Ze++5Sd1lijsbJiJGxbhljRtmtoS60/6wNag5LTzzfmT4jYf6Tl/NuHM7dMuXM5uxtMsLxe+fs//pl/8x/+yvd//YwohQen+DgprJak1CVOftBczg6nBXpQfPz1hdFXcl7uJthETn0A8fDhhKgBrydiiAym4LwlC8O676QMzoJzgsu7R64vB8ciqKEgdEY6TUqC2hQtVyod15lrIdZCAoaHJ+RpwpzP+G0hbAutFYbHM+50RloJ1qOMJxyRcT5xeTcQnyJ5j1yfX1luIK3hV//k95w+fCQ3xXXTePeB8dFy5O/JslHkRigbtTXMODKMZ8b5wuAd+/aKZsPogFWCWiXeGx4eJsLWuThmjVy+OfHh0zfs6xvKRs7vNA9PZ6wf+NmfJ3tSVQ6J9Rb58sML7U2xrgfj+MB+KygVuVw6m+fttnaSfe3BwePjGbRm3w6y1Eh1IkpNdYb4VliXV1pWUA3bGlHzyDBOFNkZxipJWu0PWX//S6S2KO1pVfVJpvf9BSQyrQpUUxy3ows8WsErgRIe0QzpaEQTMU7jjcc4Q66ZSugtVkxdkSYVUiik6fJBrRStRGI6sM7y9O58b04K4ej7T8jYQSKswzRBrA2bK85ODN4wTKCt6CZyYxBC0VKktNKHZKoiZC+pa20M1vfQpKw4nGTZMqkmaqtUITrYDnopW+vPA6Gf7slGN4H8rbbzH53W0jqqkio4tsj/+D/+K/7Fv/wf+P7zMzH1t0gsPQPCK3BOkVNAtsbD04z75EEWxoeObYi7IobIvkdO5wu/+/1vCHvgj+GP1BBouTCNA0eU/Ie/v7KH2rElNjHPEiUyTnb5m3cOaTPWaGpJxJg7Lk32w9laRSiFHSfGxwd0zTRnEc6AFrRakH5AjaeOQhHQtMNOjtBAS4m9TIyXiW9++4lc/hmpFOx4QmiLRhJiJu8ZpwZSddTmux+zdDy/NR57OaNGz1EqZph5ev8tablRasH6obNVzYSeJsZ31w6XspbTI3z63Tf4CdxkcLNBG4VQklQN0s4oZYnbjWMt7NdAeDPUrHh9DpjRYkzk9vraD2UGYRs5FkI4EHdrVC6g3QkzXWjNYMzIwTM5FMZ5RgyONUaGbDlNnoogpfizKuYnxltJjZwEKUJQgtPwgPGQ6mfk3Vy/bJEQN7RUPJ0mhK68+/Q7fv93H/jy9T9S8g2lKkYorLLk1EFrHY+k6Gai0gfpUmGco+bCOAn8eMY6gzaS17drJyYqRdx3WutOqPEykpUgUUAorHJd6CHuwbhYwKPNgNKFUhNNtE4vKPekNwnWiK7VjguQgdIp/H5EyT4crbVXnv8ZqIA7W4v/SoZQK5WaC5XKclv57/+7/wf//J//33j++koRmnLfgWahucXKD9cMwEMtGKtZwsGxv1HrwYfV8+7RMfycM7FyTW9YYe9ih0SpPwkC4Icf37geiiZmEIJriHy5BrSqnJ2h7CteSkahKLVH18fUeriOESjTF8PG3dOute57sBJJWYPu+kntR5QdkMYQckRUgbEWY7uuNcVIrplDVkoxNO3IzaCqpNFoJbPuK9cU2ZadtEf2NZBjwVjD5fKEn6fuIaxg7Yg7OaQ4cVuvHDl35Mh04mGYMeeRFA+k1bhhwg99nypUo0hBlqCExQwaqQeIgXBtHKGyr5FUDM5NvF4D4it8GGe265VSCsN4otZuZm85st8y2nvs/Ih2nsE5bNP8+N13PH//J7SIPaZhMkx+ABqGDsmqKVHQfSopNfEIpP1gfXnj9cdn1NMDsnTG7zAPTJcz+59/JC0bUPDGMp9mQrjyti7s8YI0hnw00pHYVSTulWXdOfZAK2CUxCiBMX3iK5wgU6lSMr+7YMxIDAf7eoN79mrdIO3df2yNBRzj7GimD2TzXtheF5SspCMzqbmXws3SaEhlkUr077n0FDulOvcp7QtlXxG12xytMRjraUJSWqPUel+XdCcL8LMA/r9aIURpXJ+v/Ot//W/57/7v/z3/y//yb1mWlZgbUna7tRIVlOR1O1hrJqSB5WgkEThqY1sPWtj59sXzT3//yMd33d5VjsIRduIS6YrIzgVVGI5yYDyM1hKqpgB5E6QosVLz43VjNg1rKoiGUAJzjz/PpSBEw3nNPJ1QstPxarmDkWvuE7I7GNr6kSY025EpDYwEUGg7gHIIBcpZjFFsxxWjHTRJWFNfL9VMOhaW27Wrir5+ZX1+oZXKME1c5iem6YJQXZ2zho2NhNWW6fQeaRQpFpbrQs0JaTSTH3HzCesvSD2inUa5TnFTyiK0p0oQJlPEhvRbpwZKw1YrpdSe0jZk/FUwG894mqnWg/YIBU5OnZRuHWqYqK1xe/ncB1+f/0jbvxBDD4I9z4bBjqSY2dYrQtUerqRk/2xi5FhX9tcr1y/PrF9fee8HUnxleb1ixUHcI7YtXHztkk4FH58st5uCsrJeP2NMAamIR2IrEdkay2vm7XUjhIhSMA6a09kxnFwHsA0WN47M4wOlCNqtsS7XfsPGTFjgODK1KfZ1Rd0i53rh/PGEUIVaM/t2I+0boRXMeEYAuTRqy0gJpgtreyhShVYTadso+96T32Lul+fd1Vx/stwB3CNBaO1n29jP/+zuVPpFh/P6+sY//7/8X/mX/+L/ycvL0sfKSNrdYC1bRZCRTVOkYi/wEiRbzNzSwUEfAgzVMW6az88JysZlUlAaolSO605MjdPjE61liqi4QTFdDKoqZm3ZQuS67hyl0YSnxMLD7Hh6P2FE6ONq0fo4Pee+ay2N27Iy26GHxdTMT1mRrVW0tQy+h9cY48nlIJWOw1RS3ZfMva8TCmJJaJ851oX9BmTwxvd/NxyQdtK+8Pr5M7evb+RUUeaGkSOGnss4PwwI2dj3haM15vmR2Z9woyKsiZfXK0YXBu9Q2iP1iDIntO1BPVL3AOImBYV6l8JVtBtw48T5wxO7UZRQsfOIHy0hRC7qhPEDbTwj/aUzcCXd6E5D+Im0HaTbK6ZmPDunQdCs5TQabMvk/YowtnsRTfepgqbGQow7cV/Z3t7Y3q6YKtA1IVNhVJlfPXnerqE7PeRwl7oJRLtxmeHhQWNl6IOdXAh7Yk8FsmW7RpbXRAgJbfqFYbTCTo7zMOLOAyhJpmC8I79lSq3kVEhLJASIoVKpBJEJzze+7Au/079mOtOnr14DhtP8wOXhglKCKEo3kLeGog/B7g5wcoK0R9a3G8eyUYpCC4ezvu/W+U9y19YqtfWbs/YmtB/Pn1V7v3Ba++/+7b/if/gX/5J0y8iiMc4ipSSFndIKQkBNjSIauUIRgk0I1mrYS6GUhBMZYWA7Ej88K0oGamPS3brUSuVYenBpLhFlJKfHJ6wurLeMriCCQJYeOlRixiiBlp3wbY0hl0rJlZAiTTS00jTRidtFNoRptBaoLYMsWK/xk8O6iSZUBzfvO01WBjMiRJebVeR9KpkQSpFrT/qO+8Lx+sbX1xuySc7nR7zMvO4bYTnYrolcFKllnp//Da8/vPJP//f/DSVNnB8dTjtutxfeUqW13ptqJThfZvbtSoGu9WwJKfcezFQag7/QhKbJSqsN0WpntzqLmh3n3/yK8X/3T/j3//P/m3L7jiIKdpjQ4xnsBWVPSDuh7IhAIVtFhp34uhL3WyfskRGyMJ5P0FofQMnGlg+G+Yxyjyg79ZIwZVQ5sLpHE/a07I3Ba1LNYAzD7JjPFswrwieGeaBpS9g71d5oELqT7XOuHKESNohbosZMPgoldXatuFMdsRI7WVCJFDNCdtldaI1aAlYrktSscWc/JCF0Sx3Wk1vh9uPK+PjKND2inGb65gnfBM5PuMs7mvF96FTotPta+0tRCErrlVjaEmmJHEsmpozyDuct7s7B7f1mT4oT6O7bbfysFejnst7v2l9wOHPupANZE7JWjFKdsk0j1dpxlKL73LZjp6bc1xBKd4mf6HW41YLWCp+/3ri+Fmqa+XTRDFKSYqNVzfq2sYWN6ex5ei85zwOQuF0X1h9vmNhQwtIknIeBQXdtqhSFwSmiglwlpQrsOOCnGeU8p6cHrLcI0ZC1IltFi4a3lmF07OvB9vZCjgdVgvca+xPhXOxoQY82CCsqR/KxUY6VbXnlh+//StgCv/3t7xnGmeXrlbAEShKUomnC8Ha78ff8gB88RzgT9onpYmnNsNyuvD5/xRrP+/cfGZ2j5QGKoOaKVPkOkwJRLfWnYVet9w+/Tw6hIhRIWfn1tycexv+WP/xPV5ytjA+PuNM7xHChStMX4z8rVUBIjdAGbWyHaVWBG85IPwPcKQ59OGZOH9HuEdDkHED0ECktJVqJTrkYHA/nE/OjZ3SedV2JIWNGzcNw5nx5QGD4/P0ruZROGjgyznhShuOo5CiIG9TYRfDWORAaYQrDyXF6mHCDRQlBjunnNqWURqMitEQ4RXOKXEX3fKoCpjI9nPndt79mepKY2SN1pxYKbZHS09xMlRZajx2h1M7Vl53cjtI0kSn3YK5SGvt+YIRFn3rG6E/9Zl+X9MGPFP85vOAfUwf9o4czbkevp1NG1ULaVoSkE8lyI8ku/E5CsKeCLA0jClYJrGq03NAIWlOUJkhNo4RmDYrXG4jB9sa9REpNaKFx2iJpDFYgJgWpImZNqbonftXK4AU9oT332Hg30ITEC8e2J6oUCKvRg+shNSnemUEFmRNQkSVSj7WjPa4vncamJek0oRBYCaSNFA9iDhzH2q1xry/kEGkx3VEameX6im2GsgeMkHhrycUyP35k2wPX68of//AHWnpPWifMpBgfJrbtxvX1mXk6MWpPcZ5a8t2mt3dHkBTQHG5qvSQ+ts5krQVJH8hJKZlPI7fnz1y//BtKPPj07SPWO8zpAaZHmjvRlKUK/fNDUu4rECEkTVqkmzBaobXuJX7riJSGQBmLHD6g3Ln36jWwvCbSnTKhteD8MEH5yOgd3kFpBXSDXHGTQ0hDroXblxsvXxe2tZJSQoqMc4pSFLc10bLA63s8eziYvEUNBeULp6cRM1hSzrTjnmR3VzlZY5GykUtCTg1dBtwsacnhTifOT9/w7ptvePo0scdnYl4Jpa8KlQSjNaE2VM7Immi1kxBKlwh1oJdz/fdl3/HmWvsu1txXO7LnqHZCJQilfr715f8XpPY/5d7+gsP53b//A7pkLlaztMqaQndhILCqW6/kncpntekgZtFQSvbkaHpfmUpji5FKRVnPy9bIR0Hj0aWyrwGlBMY5tHIYoWnmfuWfDLOUCDQ5wxEOukWuYHTtxHMJRioKFWP6rZ1yxhuDVIpw7KRbxxo2UZBaEnIgNNhf3jjebuRcGB8fGK1jdI4cd/brK/HtSjw2Xl5eYQ2U2HsaaqMmgREjaS8ke4DIGNcDeYwwjFPjfOqIDG8rRmVEOdhfE/HY+fpy4/XrG9MUCLfGNDukKAhVOT2+4/LuHYaGbI24rQipenmkdB/Vt3rP+ahY73h6f2G5vSIVjE8n5ocPFDsjhkcwU3+rSk33B0n6ok51nu59Mn/kQhENKS1NSKpQaG1x8wVhT2ThqK30UCfRIwKlqHivKM3g7HtKzljbiMeOTBI3WYweqE3x8npjPRJvSyRmQyyGGCNDbYzjzGvo0+7RKZzu/FpGzdPkOD8ZxskR887t7UCrLq7XSiFlRZuG0KoHKklN85azGylKMj088vD+W5RxTGfD/qW/ZHOqlJCRqqJ0RcnQtbBk5H3KqpWm2n7J1JwoqcsIfxoSqbt1sbUMNWNNT5yrpSAMPZLy/hKB/3Rr/le5UnyNnGSjyUYskdAaRgiQsiM9YoXS8yC87fImWsEZg8iFHBOUDqpKpVBpHKmQQ2ZJAVLhwUtKTnjhyUfBbZXb7cBaweQ93miyjQx+4FgP9i332LdaOs1MGlrpmtqSKkZplFJoBKpJKI0WE3FfKXEDUXCD7RmXuVLWlf35hZgq1g6UI5L3nZQPrj/8wP58hSb5/OevrM8LWlm0tazrRikZawzzLGjtDT971KBwMWHsCCLw+E5g9SOfvnnk6WnuuMcl8fLllR8/r9xuhevrynYtzJNGyohzgnT0wdajMd0XeByUJpHG3oNt+40mhSTWRlMKM024dJBy7XkdbsL4C9gRYcc+z+gsNrgTI3osu4JcQSZqShxp7ZR8ITteZZhxWoLSpFwpJUI+Ol1CCbSCKqHILkO0wqBEZV1uNNEY/IAUltstEGOPkM/Ck6RDOsfb9pm3644Pirej5+esccVqyegFo6JH3BtLQbEejWPv/b8UoieplYLzFW0UdnaMDzOjkjQjsfPI+PhEqHRvaq33F5SmlUROvSUQtRLSrV86LWOVwLsextNKgVwphyQcKyUEtFJ4P7CXLnxvx04JW28DW0E09bNhW/+vY+n9rz+c6+fv+Dhaokjko5dBSEHIGW8l0+CQVVBKJUpFVvKeapxxAqxSvT+il0+VHgve6Lj+W4gMzqG0JJTW3RDLQRMV5wQPDydOpxkxTP2t/vKCkD10ptF7gFYVKRVK6fFw3g+0JhApk/cD43oqWI6BfOxIWWmqUquCUjG1M27CEnkWL/jTV8ZpIMSVr9/9QHgLKDny9jWyHYZYCqXuvSRsomeLPG/87rcP/OZ3H5kd1BaptRGOxK+HJ96//8DTx0fcbDm2nc9//sIPX/7MuhZi7n23olBDQ8seM2HUgjVfe6+3bSjr0X5kPJ/BKdw4d8oAILShJEWRiuF0QSdBlX0fa5XDuKHndkrZp4e1UO9pa0JISin3Hkp2UFvYiGGHWnDeQ41EP+D0jLcjJRxU0cvpdDdEpNQT1mIGP4zI2tGe1lqsMdxuB9t6cCyRmhVGGbbYtc/KTN3mtR33l3j/qxMIFLkV9iMgRF+DhZTJqYdopZjRtaClQIiV82VkaILT44lCRolKLTvhWDiqwwnLth7ECCFq9lApReDcQGmamDMlF0xLCFFpqVJUx2QWcwCCfV3JMZOOTDwiMRZkldhWqcdKXK5IZXBG06h3Gay6J9P9LWje/4bDOQCPl4lkAlaBOTIvubLGTFOWcXD9EIgGTRNKJVb6OkLL3m+W7p+T9+SnUitNdpxDboUmFc5PlNBYX/d759yo1TC4ilIJZbtGcw+xD4G0RhTIRSByIeVOs7PWopBsW0AaUDaiKv1ryF0wbYRENRClkPaDY9nvIOnCvi4sr88cbxPLuvDy+YW4VqzRHLsgNEORitdt7WKJKgh7RLUMf35hfnzg148PICXOGmgaa0a++fXv0JMjEinrirlGmjY90jD1klzcc5j7gAz0NaDUM2k/utTLWuaHC6fHC+5xwA4jxp+QbkJIizQe7Tyt1D7k0J7SMqUcqGzvvZn+OfEqt0qpgpRTj9prvffsAceJ4/ZM2G5475nycafWe1AH1ISWraeQ07Wn2g1Yp3HWUZtge33FeMfgHa10DawyI8dxZd8y2wJ7UGyp4bSnmUrYDwyi+15rwyrwRjDarsn9utxowlDQXNfAFjIxZLyQTMYwWI1ShZAP3KwwE4jcENlwXQ7EcKEkQcqN2xJZl0gImYYipQgiQq3keJDKhjdQVaVKMNKSVaS21ivCpsghc6yBFDPeK2Su5HXhuL4gjcM5j5QWJTK6dhH//88G5ZfK9y7jiMgRmVYeDTjradfI3go5VW4EBBm05hYLtwSxdG9nKwkdM64KlOwNd2uFVumcmgpNSmLu/WtKgZALpiqaNGg7EjOwRljTHUjdk7WNsRR6v1VS7cwXpahFsm9dsztYjTUGZw1NS2p1NNEHW6SCUHf4aCucHif8RbHnSi0bYX0jrjthO4hR8fb6zO0t8LznnkodIpenR2ptpNRXLte3yp///kfevR8Z5ob1HmNPuOGCf3hEOkcMC9oVxtkxnyzedy2wkQIlK01kYu12NrV1blLZD0qqlCY4Hm60TwvnPBONwk4PjO++7eloXYiCFH0tU0qgtkBkpZYN4y8IN4KylNp31dA6FrNVaLJPFpEoYxi8oxxXWlzIm2IRnfmj/AVrB7COVhpCKITu8fWiRkrOcI+0qFJSrMO5M7O6YG47+vNXhE40eqQfpXbtbQi0GKAqtAQlGlZUBq3wWlBzJgXYUuZtD2xZkJHEJJC5MKjGxTakOnBjxL/Ak5/R1lOyJIaIIbKFK9c1sq53zIjo1cPRtp44khNp3xhUwj4MqEFBzmQg5EwIiZ6UoYl75rj1cCdEpqwr8tgQ+428jazKUEbBOCta9ffh3p1g+RNRuv5SwFeJ5BS76gbBedB84yO6CV5K5boumGHsPrcmEK1SUiSGLmoeAf8Ts7PVn78gLWW3drUuz2pZY1rtWYut81RzzaxHZNkL82gBRUV0QBa6TypFo2rB6AdAsR+BbQ8YbZGmUxNCjIyDxzVPqomwdhPvNHqE6IDf8dzJ6FtKhJLJJO7WB/Z9ZV+hZYk2ljVEtv3gIhTODlSXCftKrpK3551wy8zTgJIe6QeENxxpw2qFEpbBjhzOcr44Hp8cg7/HwtU7WIsCtZCLIFVF2Qs1FCiKo2yI+Uy1G2u8If1Xas3MHz71KPmU+mETgpYzraWO2YwH5IiqF7SfEE13BlQDWXvGiWgCKF1cojVumhFkSjo6bpL+4FrRUKqvxlI+yK1Pv5WA9bayv92oqSBrRvm+b67aMT1MKBpPTyNpS13fe3SdbckSS0FqRSl3M78AqyRGGUS961qb6cb9+wskpow0ktwye62ImFHrzkzllEZa88DUwW770aMuauHr6xvLulNzuQOeJTl3TGfLmbQf2MmjqkN0RSq5Nq7XhefnBdFsd91kiEdfTAkRcbxR5oF8OiG9JxmL9wOS3IdFSH6KMaH9fE5/2eGUrX/xTVsUAqcaH33l0+XEn9bMf4yNqhTVOJqR1BDIVG5L7JECznWubM2UeNBaxci73UY0Wk7E2tlBzhmcEtQSSVRCTkjVJ79FSI6YkNIiWmM/MjSJ9WMfAFhPbaCEwqAw1lO1ZAsHZQd7MkhjcNOEFI1yHChjEaaB1j13cTTM0tD2AFbizcDD+yeO7Sv5KNh5AOEJ1xvOewbvuwxx3ylSda8efTKnhenSLFnJIhILtGgwdkKpETtY5kfPx2/P1GSQ0rKvO+vbQt4aNTaMEj8LpqXUlCyIobLdIlIWQlzJ9g3zMKFGT0MiiiDmhDMKryGlgxJjj4vvuwKEFsjWxSCt9v9Pq6VnVSqBtJomHEJUvBTksCGUQQ8zxhqkaHdWMUCmpoO8LKTlxvf//k+8fn6mxMzjw8TDr9+jH/0drF06he+hT7dz7muUdem/bk3QlOh7c9EHi53gp5GKzq+tEqUNg+lCDpkSuRXWLRD3HVkTa8wYr0CMxGi5boGG4OvzTq47qTbebgtHOFBWobTC2C6wl/2nSImVrSQ2l2j3WMRCYT8yIVS0kPzEoZRGdSullnirIEdK2KDmDhgzitIKusauMJK6H9CfxPB3ksX/5sPZhEJKjdQV1SpGNowVON+oTWDfzXyNghuRJhRJZZJqKNmoNIqUVK2pubDniGk9zs9LgeuobpRsFCSpgTWuR7AoS64CoxRaG3IV5FhxWpFTIqbMNJ14+PCJJPsSWEjJ6BzD5ScWbCWkQBW9fNbaIk1nBYlc0UJ1GV86OvrESZQCK2XH+yvJ9HjidEvU1G/35ch3h/2EyImcM2FZuolaJ+aTp4hI0w1UpbadlhNJGEBjjEMbiXaK4eR5+vSIrAPDeGZdD55/+JH1+YW8rmjR0LJiBsc0XTj2jiNdSeQl4kfPMCvsMHOUipSOaZzZU7eqCdlQ1K4PpRMYhBTIVnpYZgzUlDpTt2YyAu07EK1qDaIDqv7hnrPWxrosON8YhxFROpxs+fGZ2w8/8vz3P/L6+bUP7I7EcDlzQt7p8p37ZOcT0zvFOUMoBaECOQmENJ31NOjewrTut3VGk1NmPwq1CmpVVAkpl747lBIhM6X1FVauUOhf/7JWfvj8lf3IHfwsBCEljnRgXA+UyrlfADEW3D0uUhC53Q6cXCgnj7OSpgUCg9K2A8B0Q4qGH0y/5Y3GOksVqhvlGzihehaoVIhWSGFHGteFHa2bJlrJv+xwhqo5DyPkgsxdeKx8P0DvLop5Hpiukb++bLwchRQz4ci4UklA3HeWGFGtIBs4KfGy4URhcharLTEnUqnsudf0EtFJ2zREg+1uUlWyqy2OI1FpTN4jB48UDUpGW9tLXnkHL9WKbhY/db2jUN1kbZTHKIu6s3JkOohpQ6gComCdIORCbA3pLcPDiXXJrK8B3SROFIx2rM+fSUdA5ox3mtNZ8c23F+b3E/5xQJgukhAx0cJBNf0BEIOhxb5LKwVSaVzmE+P7D9jTxNtfLcfbV0ROIAunxzOXx/eEAMZYjv3K8XYFpxkfnjDjmepOXQYoDcrP5HQQ8wFNkFKPyTNuQrkZqRXhWGlpRaS+t40pUbXtgcVmvGtoDbIkqhAd2SE1tEqKCSkUW20c65WyRZbXjc9/+crt+WC/VZyfuF0zy3XjvCww+H4b6wH7MDDrRBSeICRm2gCFtSPDPDHMBiUbombIibQfbMtGbZ7SCmFJ5NgIVVGlQihNjomaI0KWTjQQghAzt+3g65cbt+VgPj8htGPbX8lIlLdIPXCEg0EPCFl7pAR9gJmA636ghKB6g/IGKSzOdQO1cxKl+sujmyoye1H9Vq0NFTM+J1rJPTXg2AkpM8zne9R9F9+0FH7Z4XxZE87R95YkmpaoaUQ7hXGVqQq8l7gaOS+Vq9Q818ZLk9xSH3vb2lBImjR0XnZj0JLz4FBawd7INVGa7JmLOfYFe5IUVXofWwvT7GlGIKxnHBz2fCLIrovS3nUq2j0xGAnKGKx2OGcQSvQFsOxdlXMeYydSyqjs0NFQ8wEkJAKpCqUWhIPpyRBCY91+QKWCKhFZZP9eDFijuJwH3n078tvff+LpVx+Y3s/E7YaIAREicdm5xRek9h2pKSumKo41sR6V8V1lnizT+ye0qeTVU/JBEYmHpwem8cx27Q6I2iolNZquPUtFGoQ0d1d94/zuI/vrC+1oSFlouYKeEHaiYskpkVNEtkQrOzVs5BgRwxlBQaj+WUl+umkhk8ilIun2NmrpvfgRaKWyxcrz287z9aBmiWzmHquw8eW7v3D++B53+QZhZ8x4wp0F1Y6ocWS9vdFqu1c2GjcYtGio1nNolhbx1UI1lNxvvW1J5JgQxtMKtBgxNAYjGL1mHB1Ka9brjRASMXaYWymZVARHhbwlhCnEWGjXnRwTRoJsdx2vVcRSWI+jq5h2hXAGIQXKSbTRaN3TwsLRSKkgVWMYPNPDA8YZrGgQV2ItHHtAO9/NIi1S89HZw/vyyw7n2y3h1ca7EcgJ5TqqsEhBEoXz4BmGyG9+NfK4Gdaj8rIkvnveebndg4SUJjXYQsG0wmQMk+tm6Rhit9TQdZFKSEQTlFhIpVG4A9CMItYuOJgfZubLGX+5UK1Btgq5y9iMUqQYiLWXfc4MICOtRbQQtFKJqWC0RfsRnAHV4+0LlZZ/4rt0p72QEnVSPImBVCVf/vADPguETPiz7tgOK3n38YH3v3/Pu998w3ia76YDQUmBFna2txuvrwHjJuLuGCaF1QotGst15/qy4+fHvnp4fEReRkpNCEUPa9Kaqt4o241c4exPCOUQZujAq2NBCoW2XXqolKIogxATqioKuiuJYiSFKzW+ItJBvF1J+0JFoM0ERXREaetcJQEI7ZDS9KTsuNFKpsaVeg+Iktog/YkoR4SNOFNBZLwR7NcbrzYynGamJ4UeR7AjNMn40JDGYsaZFO+3hxBo1/valmPXt5gDZRvaVLRuWCUwNaNzd5TEnDC14Izk/dlymSWnk2Y+GW6HRq8KIiQRKaJxenogvd2wzhOOzHFEbm8LksY8jIjWUBKsNUhTqVoTaiZtEZMz1pvOxkrcE9M11RbWY6fWytk4pmlGe4+RjWN9Yy3PoC1P5xEjCjntrM/fkbcXajp+2eG0ylFi4SUsjCbhvEA0jZb2zkYRKC2wk0Zb8LGiTUY1yaPR7HvpE1IEo4TJOJ4mj9KKGCNHiD2aoPWeVt7Jb+S7qqjWHnSkLEUoqtIIO+BOF6and8ScIQVyO0jrSjOaEFYyhXEyeAcp7+SUUEJC7QObnA+OsIG2ZHo8fKiVEhMlRXJtCKGR2iGUxs2Wj7/7DU7D7fUNicBZi7WK+TLy8O6B86f3zJcZpSDn/iaupaK1AWXQpgvW037cifUCL2Gome3HZ5ZhYHwa8SeLNA4jO71cWQNKYEWXo2lvIEdS7HjF/fW5276E7ZPFtZf2zpwQstLE0INdY0SJRtk7zrTuB3FbSWEBo/H2ERcbapAI2cg5IGpFuxHvR9aycewLcbuRY8Q6S9MDQp949/6RX/3211ydo4UN2QJWNrqcQCGU+RnT0YNwG8r6HlcsFPLoSdNGa8bThJKCeOwIunJpj5lS915V5YQTUGUjxgVZCl5kHk+Wj4+ecWjYAaZZcy4jiYz0XVlVmmacz6x74Dxf+OGHvxBCQNQuv9tqQCKwts9a3ODRg6HliNOmkzc0IMrPGTKtNbTRCAX7vnJ9e2Y8nXCtUEok1X7DPH38NYPV1PTGfvuB5eU7ckgYMf6yw/k0D8TthZRWtHPdha4l53kEKbBaUFVj324Yp7FOQevi8rO2HDZzhMpRC8JYRmcxUhNTJsQdkfqe0SmNkRU7WmoyHEcg5kxukELGyC4Z1BYQCjeMuHnG0Njffuy9hoFGYpwcxirm04AWlVwiKR8IumFWKQ9KUErqe1IhukKmFGLOPSDpCDQk1s9YJ9DGM5xHzPCRx/SILF02p5Ti068+orRGDzPz2bPvz+S8E44dJyzWGsYTHMcrotb+wTfBsWbi247aNmL8kRdRoL3r0YGmg7C1dQjnKLVQ7R0JkiolPnN7fSOvkZIyBUGRE5/VhhtmfvXrb3h4/4A2sOw3Sts5jogIb6yvLzx//5njbYOScLZgTx57ahgUula2sNJaX8insJJ2hawBbyt5D+R8631TjYQY8OaRf/rPfsN3Ho7bG5LSpY2Dws2W8fLYcZS1hyw3uiRUWY0XQxc6HZ1A4cYRozXGWGQp1G0lhMj17YXlFsihYdBIK5hU7TwjrXj34Jl9ww8CdKHWA+8F33wc+fD+RC6a59ed5faMkZm3ly+sS1+naCWRWpNKQ0nR/ZxZYO5/twJGCISS+KFnrLR7wnuIEWUUwzDSOEjbG7fX72mqI05CEpwv75ncREuBsP/IsfzYb/zhE8Z9+mWHU+YdXSPGdBEBrfUl7bEjtWY9MoLU94VK9wU0Basbykp0VuhScbVhvMA7Ca2xpsxkBKdhpLRKqgWpRGeKaihZcMSCtCN7TITbilGK7DI5xvsIujKdBlpUyOLRs6HFiKiZcRz60joWaurezPJTSrKQ3QNYdxqaQk+SFnQHe809aTv+dPMphfQdaK3nd2ip7il5PW0tWaiiIFomhJWUruT0Rskryp27JM4Z3MkjauoQtFxYtszbkqhZ9CSqGGB5o90KiJFWR2od8V4j1E/Bu5BiYXsNfPnulfS6IHKlCMPLdmXNX/jd3/2WchR0070c9BLvB5bbK+HlldfPL3z97sb1xwWrBecHyUkVjtdXFvs9NiykdvTAISSphP71W4exjjY9YO0INZPSQYwLocLoH3j8ZuLmC9Zo3DCjjMD4HktRpO7M1np3u4jWg3/uqkFtuyZaaN2HULJDsrb9YFlWwnGQc6IWgWgSbxTD2eAmg9ACoytGZobRIZ1Bq8Kk+82WQmO97diWUDWhW+T1eoWser5o61EP8q6VlUqSa2Xbc19GlojVFSnB+YYxjiIqKSfykTHNUgFtHMo5hDY02f2/CM3l8oSoG9eXg3SspKIY519h598gzPtfdjhV3XFeoYW5TxcbpEIKmbJ15o91Cuc0MWRy7qlLtfXIhFr6W8cZyzyNGKXY9x1REl7BMPZhTa4J4wwNxdevN9TdDVBqpZZ6dz6AyIlwu/L64w9MZ0/UGe8UullESuQSWdflnhjVlRztLiYQQmK8o0RJTgGlUk9/kgqUIsdIPGI3Sy9rj8FrjeIdkhGlPEJOaK1R9HWREIncAiEGQjzY9wj5DVF2RDkQrWeQSKtwo6MmQW094t2cJuwmqKkzT9txkF92Dl6pq6OeL5j5jNEN5Qdk2inLK/vXL7z9cOX5u4Xwunftp0i8HQJ3es+f//hnfvhLh089fbhwehwZLp7RDYSmyBGk8BxhIcaCnwz7Fsnlh54n8nTGTgrl+6BJiYJsAq1OGHNCiAd0LrQSEdtnclk6V1hF/NmxF8sREpfHC8PkKLnvvPMdeCXuiVzynr2jlEErhamma67vn3W+R9lLo/DzQFg2ShRQ+8tbmYafFadHi/MaSe5GaycZTgPSG9ZQWG8H63VnXyq6GC7edbH7rHltlRA6sEtrgbMeaz3LulIpJNloWWBkwUiBoCBlR+Dk2DiOrZsvqiaWypY6pUFWh1VnhuHMZTozzRPr9pltzSgxM55+hbucqWYg/42Uzr95OD+8GwnHDrUhlUBITWuat7eNY90xzjDMnlobJQda67R1tCLUg9o1emil+sRX0N/AF8+ybWgK3nV8fSiNI2Rku1t1WiWGiJa9lFAUVG3kNfH1LztSRd5/+w3n2dFyoqZIij2yr+SVWu6CewFo2XNY9s5otV6zpoMQI1IbjOuL8mM7uH698fb8zPnkGK2hxY0UFqSR6NynfZnGNI/M55GQBUepKFk5zzPr1xstC1ooJHaMMIicSNtB3A60UT1ewhvsaNheVpb1lclJ8l7YRc9kGffAI5CnAVUzdT/Yv/7I61/+wtv3O9vLwXpt5NSINVC1Q8XEcSxorfjjv/4jNf6GelfHTGePGzxm0rwzDwgl2NaN6d2ElAdCZGLc0UGih5HW+k0nhEHJASEnmprvNqlMqwGZF1Qp3dcoK9pYLh/ecbuuLMcBVvcsUimoJVNoGHomi5DiZ+eL0JqUEqUkWkldMOINdRrQHx4ZTEPWjLUb8aik1NBOYyfN+DBymgdy2FjeDkIJuDZ2iNy2k7bK8mUn7JVhMPz2d9+gvhTGk2W4Jd5eF2qVGGPY94P1eYX7blZQEEViB4kWAqcF09A15Qf9TIDAOE8tHZUjxwdwTyj3nmG+8PA4ENOV5XYjFs/l3Xvc0yeU00BB5l94OJ1V5CyQwjKOI9Y7bsvG85cXakyMpwlkQkpPioWSA14rJBqvLXs7fk79KjGhncHaDsg6jqNHN2hByz2aTVTRSXSA05raILZKywWaQKEhJUQqpOWNtHiyOVNzpoTIsW6QCy1lWi2YodP3Smss1ytHTH3F4g2xxA4gvh9OoXSf3q2BvCfw+v69FGra2d4iulXiGtm3yHR64OO3v+bh/ROpZUq8srwEvJoIa+K45T5FrYLtuvDy+cqxRqZ54OHjI9PFwkNFHQPhbUPWyjDOTO/OHKJiLGhtqbmSlo3teuX28sL68srxFkh7ISZNqgaMwHlHCAsxBrz3PH+5IfmBnBpu1PhZ8e6jY34Y2deNh28nPpr3PD7NpLgSlhtFdJiZMBaMR5oJKQxKjggxUYWlyM4dqiKTUTTp0EYjjUE7Tz0yTx8/oJXjttyo9U6DR/ycWidrL2O1MffsHXU3JzRySl2AoCTWG0R1KHFG8pHt/Mq2bcRcOwjcWbRzWGO6HzNYak7UVqgxEPeDcFM8/7hDFpAOrs/PfHz/RLHwrkAIhZIlyy3w9fnGl89vHftZQUvQQuKMxluJdxpJZVuXjmFt7Y7rEQzjSDZgxxPKzNRmMErT8sG2fCUEyfT0geHxEUZF5p4nm//LDpV/xGTmkLaRamKjxx3sxxvbLULTTOcZIQ37kUkhI3KlioZR3funvEHWAkhyuKdriW5UNbI/UCBIpSJRiEaf0AGCipKFUcq7m7zjBhH9FnTSIVIjrQGpJKXUO4Cs/7fGStx5RCpD3iLrcvD6esVYw+lpxI6q93ItE8NGa12FRK2MvmMitZLEcEAtKDQFw/G28+Nfn1n8xvH1gP82EVqHRIfSzeQ5C0rtJMCwr+zXjdvXhbBEWih94OE854cHJjeTrq+EEHCXJ56++YaUd0pLuHlCCEU5NuK6UkKklkYr4o6IKUjjGOZT78MrRBJHrMRSyCZw+WTgEOzLDiHz6bcfEXrBiS5/nC8T6+tnclxouVJS5dhq934qiVGeWnuwkiQhtERLQ27+vmesaGeRUtBaphJJte8QlWmU2LW7ythOn28CpQ1NKGIVva+nMGgNCZRRNFHQ8p5paccew9AicCB8xFfQxqP9jNAWWqWVgJk9rah+8yVJC7DviZYbonTUawgH3374FW2EqixKWnKqvL2uXF5GHh4dr28LR4AWA7OFcWicZ8fT4wVEY7kuCCVwzt2L0oKiMDkPppPhFSDizvVYeF137MO3nN7/CusnWr2balvrqd6/5HAeawLTFRcxZHZ1Z7Pm2sXs2iK1p9ZAOASidAE39eiWKSHQVvc9ZoV8RKxV0EDJ7gNNe8QO7v61FqTUfdckwGvJ6fHCum13V4KgqO4eF00xmpGUE94Y/DCTYybXTqUzziGUodQ+bfXjDMtOrI3SJxA/OxJareSQKTWDaoyzZ5wGjNakmEhr4Hp9IyfJ9S1weztYXePzlzdu64IdBO+ePM5pcIlWI1LUu12qkmPhWBPHUihp7w/9PDI/fWA4ndhtwdYJOZxJGkCiWscwytYYrSVZzSJA6S6ls0ZiYibWzDQOvCxX9OhJ+0HJjT0mUrvxr//Vv+fkFVY2li/QCFx+dcGOA6lWrutKTo3r0rh9vVJzQzvL/Bh4+NAY5oYyidwE8vD4aUZbh5YeP15Q1iEVtJLJOZFyQmtFbQVrLbFUYgj0KZqkNck4XihSsKWM1A1KIuW+xkDKDo0uFaUMZvSkZIj7gjAWWWy3HRpL030AQ+NedkKLBzJBOBItFXSOXEZBTQXnA9M4YGzGn2aKGbpcT2mGk+fyfuab3z7RMIzje/a3V14+/4Xj9gxUQuvx9rkJvHb40RNSYt8Do7I4NyCdp1mFEYFjeSOnjYzmMj9g/YySsmck3ffEVfxCV8rblzfM4Di/u1Bk7jBiQDuN0B5Ml085Z1luO9fX/WeuzuWiOc0D1I7AUEJB7XmJJXaKt9CgnMK6gRoSIQZi6gwdozXT6UylkUJAa0lN9OlrgecfX/HOIzwIoTg/PlIaiHmixgtadgxEDJ2YMJ4vPElFERU7uR6+Q+2Sv1bJ7NTSQKcuH2yV6/XWS7jQwcPLW+HIgpglR05dcRJ/4DQ5rt81zg+Op3cON4JUFaP7kKPUK+GorLdMTmCnRAoBIRvuMtFkz/sQyhJDYH17xTZB1jt+6rs1WQvj6EjnkRYrOYeelBYqX798YWuN83yiiY0jdBrC4CXLyw13Hvj46R3H8h3Ls2Y8wTi5TiMvguV68MP3ga9/fqNuCa01w+PB+9/CwzeZy7sL1g/UFMnH0b2zg0e6GY2n5EBJAS0EJsU+hVUWpTyyVBS96om5cho97Y5A8cPIOI+sLz9SQ6V/wD3Z7qd+11jX4/L0QJWeLHsWa9UDWVmEcXBPCzdG08JGXXZqCShRmT04IRGqoSeY3yuGSTJMA9XO3eOqFNIa/GVEKs3T+2+YTx/54S9/Znt75Otfv2N5e6XmRA4J0TxmnLuTKd+68Vw51j0TwwuPnxzknRCulNKYH3/DaZo7XpMuUOkkhvbLXSn7lslVIPXG6WmiyYpxBo/ryokWUVVTkmY/CnsESsVbzTCf8LMlpR1R4TSdkLWxvr6xhYNW+6GSxoK1tNLIdBxKBcZpRGvBbdnw1nRmTy79Gyxwfb7iveP0bsK4jNaGcT7RaiYeihoPyJUYA6XQhxXvHslK0ORP65PWUTqt4KS8IxYzSoieYBx7vlzLPXNTa8vDdKIZz5eXK2WthFukXA+MgbIbtHA8KMd8HjHOc9RMkV1AUcjk2pfWxihaSzQadhgRtSBKY39+RYTCy5cXaklc3veJK6LiJwucaeKGMLpH4r02biGDnjmiQmlHawdOay6jJ203RE60dNwj7iOtRlreITeOmHn98pW3L1duXyPlaCirOJThnfsVWQ7sh8RYj7ESWiWXDVEawhiakEjVA5lqjjS6aUAYD9Vz8gNSSm63NwS6/zmmV0pCacKe0HZAG0XcocYus4u5oLTo/BM9Iu2Jphe0kzQhSVVTqoIme+kvOl8YVTnSzrJv5BIZThPTo0f6hp4N47t3XYXlTxhzQRrV1yC+32JCSaoSXLdXlJfM+oSffsexPLG83livC2/Pb9yOSBEGZQdUEdy2yHUNjE8D1kDYnxFxQYiJcTqjlP1ZYsmdhvBfFWTUtGKLiXRdMJPFj5qHdxdSGUhSIYSilsT15ZV1u909cZnpNGEGS6HQZEVqTTOyW5VEv9YrAik1w+mCP59oZmWIB1sO1D0T6walInVBCcixoyBLbuTS8NOIRGGk4t3loZMORM9eXJaNfbnRYqKEgkBhJ4GcHFIJsmi9V7j7SsVPHlMhkDWTt4NYCsY6pnnq5ZHUDINiORJSFkYrMcVAkqTj4P5NkWNPcEY7mnboyWNOG2pesLFwmi2ndyNuNjTKz2WSysC2c/z4Qgkw4nhdVzaz4idLU7WP3S2Ys2S2nlwlmYSrF5I48bp2FIuhokQlLs841d0pby+fsSNII1EaYtopVZH2neXllf3ahxzH0V9+ZoPrJjm9P9Nq7sow0aj1DrgSCatOCKUwRnc5Zslo6+5PjwAhGc8P5FJwJXdjRDqQyVFR0Dq1oeSMVNzXWgZluvRNqBEhDDFHtgR7kixLJqcCwiJ0waba96itYo3EmhHlA6lV9nwg3MD7Tx94/PRAMgI9XpDzE0KNNCxNKBqtywbvtI5cK+W+3suiQBTUUjBSchpHaiy8vNxYt4AfRpT1rMuNWARnazFaEY9MSxlhJUVaauvYln+IKOlZnb9wIJR0L1tjTewpomvl9Nh1ps1ojLLsy8GxFqwFLTSg8aOlyUIiIFQDJdlCQGTYUya3hjaWh/fvmZ4uyEnjTOHJPHJ6d+L1deHtZSGGvaMGa7/hukdV3LH2jSPuxE3w9fNnpscHqhBo3WPAR9s9jst+41g3Uko4ZsRkEUbc9a89kkEgelqVkAg/EmtDVhimEX+ZUE2gtOTrDy9MtueCeJmYT5ZwRNYKVkNtiYJF2Ynh9B7sSGuV8f0j36TGcn5jGuD0ccDMGmE1RUgSsO0b9fmVFgsvn6/EJdBkwc0j4zCTVCTuESkd8zCisGh3kHjh6/cLpjZ8PhC6L9WNFky+A7KkbBgruXzzwPTugj8NPQio/pR7UilRUKqiSNnVL7cr3/3P/xPxdebbX58xaWaXGWTFn09Mjw84eQ8Vug8Ca20g1N1koBjGE7nUjtlUEmkk63ajURmmS69cikSJTnIU2nTWsTZ9uFg1KXVOUM6CdW+st0KKqSNBlaASwTTs6BnGgaQUSmvseaaWiBoN9jwiTxec9wgz0KQHock1oeicI6VE//NqQ7VO1rdGoUrh+esLP/z9fyReV7z1VCF7lZMCDYX2hnbPhHl6/w5lHEJ6XrdXjBK88wNVdJBAL2n//8AQiqoxjp6SQBiJn0bW7UaV931mqwhRmCaLU2dqBaUU8zRgPZTaMEZRCmy3SFxiJ6sD4zBghwHlLU2LToTTcJpGUA1tJMt1Y1l2OivtLo6XCq3F/bA2Ygx8/fKFH1++8vj0yDiOKDo2MoZIWHe224bNCe0V2tGX4EpgtKXV3NGZW6Dl2lk7TaG0RGjJEUN3/bfA+OipoRKXK5PTKAkhJMazxcrEeBkZzhfef/o7vvnNb1nzRlyvmGng6VdPTCeFEgf+1HNhjhRQSjE+vec1ZNaYOfZe8pecOwMod6TL6fGCsP37FnLC2JHpkpDuM1J/x/b5hgg7zQ+EULBWcJ4t02hoLTOdT/zd//n/hJkKRSw9ntA60tG6CdwIkDtSVVrJ6BCoX1eYZ+SHSNt3lvVGiBEznvn469+gjSZRQSuMH/paRPVBoFCSYZ5YlkyTDal7lYUo7PsVpRTOzRjT4wtS7tT+nDsH1jhLDHAcCy8vr3z5/JnXH1+5vb2yvK60VHvPrAvKNebHM+enC8Poma3i8u4dkzc9z9N1t5JCIMRdiSRE9/FKaPeDQ+0Y0tZah3jXnrkZl4P95crLX7+HBsPlEWVHlLxDu6rEDo754R12MGxbpDbDePnI02/+CXa+dE/nPRZQ0Ckg/xhW+m+vUrTlbV14evRoK3h96cE0UsD8bmJ+8BShsY8DZ2s64Fn8tMLNSCTUSjw2tvXK8XbQcmM6TajZku+SqBAi+3qw7wuGkdlrrBw7qFdJnl96pqe26m7ABqNrDyt6eM/b6zNhX7m1yuQtqUSOsJPDQamdedtao9VEy5YqJVVITNOUsLJ+eWZ/OWiHBCEYTxZ1NojWEfqlZk5PZwbtWd+uvHs6UUIibBE9d66RHSb8+cz5V7/l8ff/B9zTO9bnvyB0wLSGmipNOSoSOQ6IpghvOzH9yAf/gYdPf0dbC9uXBdqKcT3mTs2OJe7opFBmBO1AOYp2SGt45x/AT3wZ/4j6/Jm6bDy812gnOZ0HrJGUAA/vZoYPI4gMBZqoTKdHpDnzzU3z5U//Di0KSvb4eKUr44Pl4eHC5XQGmdm2N4gJEXd2J7FeEWXFnUe0kRh3RjlHKpkiNOsRqaKhtEA0QRUFiKQjEbVFVMGxb2itOm6ThtIaoSyFXrLXCvmorF8X3n54ZV8C2y1SU+O+eyPVwnbdqclRHxpq1jgtEMZTcuFYNmgZXw8YErFVhJkRVdxvsop0GqsV8dhIIdJa97qGdSGFRKuyg8pCxLiI9lOfMbggegAAfsJJREFUTdRMqxrtHVVklvWZIwJy4PHdb7h880/x/ozQkiYl5c7bb6L3nvwNEt/fzueMkdPkMVKyrwstJfZlRbSGmS0TEm0t2hq0bIjWqKXT3H76YWsh0EKg7ooLqXvsvHUCqRupBGI6CPsBqbC83rBG9RCh0mO84wk2dso9kExYSbuHC9VWMVZj7UwqkVwLpfZVwrbtHTSlBcIIqpVg+yJcFCC3nna2BpbXlXhr0AQldgaQdeCtR+uubzWj4Wl46oCwWnn9+gpOoFTHoOh55uHDB4RzvN6WzpWtdIgVosObpUZEuH555fWvr6zHX/jD/+sPuHFgtnC5OGZ/xqiZqiX+3QymcuxvKOdotXWygxqQxuMmyTe/dzyczixffiAsb5R0MI6O0+wQNHLKjOcLuSScM6QieruSM0eOpLLifOLxQZGOnuhsveTy4Dk9TswPM1IXSnwkXq99X3i7snz+DjU6ii4UO3Wmk+h+UKHU/eFrP09f293OFsuN77/7A99++zukch0QZlQvbUVvOYzSVAXaGEopLNeV9brTiqBkRUqJWhs59zZpCytVNlIeiIdmNIKwrqSQOizMKh7eP3L50PAPEjM18l3dBIK8G5LS7Ovaq0LlyTFxrBvxCNAE8+XSva/Ooq2mVogpQuo4kpwbIjW0Gbk8fmI8vwNUb51Uf/F3sPRP5W375bHzk1a8O505wpVjX4nHTjx25mnGec84TmAsTTRK2knH1rM9cuJ2fcMIOI0jBsnkPM11AYI1oHRDqUJtAaUrVgiqUAzOsCxLd4xUEGjmaUDK3mSnlKkalNWYwfN6e+0DC9HuScgVpQ21SdYQybHx8PjA/OCQXlBEpdbOJpVFoDBYNVLrRjgCNYFWinEytIdegozzhPEePY44rXsm59sVRoe2BudHpPaoYUT4jkMhd6eLEo2YAiUdXVihBMRKeDu4/fDGfqtUsVJOA/ZBk95Z1ABqlIyTx4yC0lJXrawJ4RXgiPlATg5lB7SfMB81/jSR0gt1X7CtYEW9v8wUxk8oP+G8p90qOI3UmvmiSJ8ulP2RsCbCGti2nWFyPDyeePfthfHdTM4HenWoPFH3TNgiW3nGXQa01rQhUG1HbijjUeYfIDgbaN0xLZJKMoV6P6CXyzvUwyOpgJSacXAgFcu6dWxlrUjRh4etavY90ppBG8dxBFJppFpoJZOS5PrWe/ObrOR9Q9QeflVz4eXHxOmHlV/9buX07ozyPQCppkJEdqmeHTCDZFsWQghs12vfEmjV816HgaYVerCICqXc9d9CofUEcsD5M9oMPUzXGrSWtPuhbO2nodA//PUvOJy6VEyjex5TJsWId5ZxGvDOY50npn4Yj20hHTes6nmEk7WInEnr3rm1teGMIeXUD0eN5CYhR6x3eGPYj0DNFWc8Wz4oOZNaoRqH9gphFC30t5VRBuMt5ILzYx8yjD0K73q9EY9IS6CE5vLwyDArYttJpRCPSErQDEzDyDjOeL9xmIq4w61pFSX6wGgYp45+lL7zdWQGB6qazpgdRqS2aG9JClJLvR9vHV9xxJ28r0yDRTbNfgSu685tT6SocEahs2J/WRFt5fTNhJtHlLv3TLnhpAY1gBnZDrguN2w1jA89HFhrhfQarQxKDchwIEqktoK2Hjt6rLPEIyCkZjpdSLVAg/PHM9P572ipkI/MvnWa/TiPnB4fUc4StoQaRtIaWdYr+22DqjmnxnDptHQpShcVqAziH0xtOwqrJ6QhsUYxjpZ47Fxf/opRAj+cifvByxJAGarStCqIIXC7XXl5fmVb+1pMSofWjiM2jnh0MYnWlKK6kL1CiAdxPTDKI4oihsr1+cr1eaWlzG9bQc99Qp+2wO3lhpaWaX4knyKhKNZ1Y9s2hBBM84lD276CM5Ime4DT+TSRChSpeuBUkaQEuYq7J7gHQcGdtsd/fiB/8eGUd3Owc47TdO6JYUZxuZzxzvP1xy8cIXZCXlgROVC1QBmFF5JaIYVESIVWZXeOO02TfbKnpKJp2I+dkiMhJWJK3X7WRIdPA02BlBJpBU0ZbNXd+Hon0F8ez7zdroRaqPvGbd2IqaDuMYDbEcE7hJT34KQuK9tzL5+G0XN+nLsrRUXcpDvLRje8dzg3IaRH2IdenomE9AotRmgNaT3OGYzvidkxRawQNAo1R5QUnB4upGOD1tBWoZymGkXWipQy8fXG7DNmMAgMbjzRhOHYIjV0jfL0cAHpQBT8PVDnWN7AG5QBQepc3lKQSmH0gNQKM44dnBw3cqkY41HKgaVH3SlBKZ4SAzoG5KGgNfwwoP1IFaaT51Nj+XJlz5n1CMgqaK8b4usz5sN7PIVG6uDq+60gZU/janfpZWmCKi3Oj4i8cqyB/eUNERUxNTIFaV3v6bRFy9Y1rqoPU1LK5FJ7srWyaOOQRiJUz1w1pvN/S5ZsW6blAy0MSuo7oKzw/Z9eUcIwPWmcVZAT6+uVWjtM4Ngz2k/s204KCSEV2jrcNJNKpslCbZlcElZZvPdcYyXmgrWSkAtnqRimqT/jPyEwfz6kPXmslP8yee8fPZxh3Ukx8vjhEWOG7mbXvYxYbytHzEitONZASxuDhtE6vOkIy1wKqhiO0jhiogowuud9l9zTg+3gyK2SSiWWDE0gSiO3RhEgtMJ4Q259paKtQpRG2g+uR8BNhr/8ZcefRtIRSctCia1rZWlEGsUY3OnSBRHErrhR3RB8xA0zOU5PA40zMSROcy/33CiZ5gFtLGY4E6sl10ypiiYsyhu07qh+I2WfIpveZ2klCa1zUFvosGQRA8o3UILTyfN09ryGwFESfhw5P85on1C64zJBUnOXstEqMSdqCvhhZtCW9UiUvFH3RjwiSqQ+pBBQlMIMXVtshun+AlyRGErtD5LUDuO66z/EnSNmlm2j5sT5NGOHEZQHYdDaQZUM54W6J1IqUATFJIouVFl7fmkxiOJBdVGH1qq/D0uhVEgFanMgKjVvHNeNL396wbmvXB7fMTx4/DyRaiKn0mWQg2EcHaV+Zd13UhawH103XTKlJObL0JEuFmo4yCajlOXYK0UUnp4u1Nqp/vum+cO/e2U8Nd49TjzMDoUj5Q4216pQ0kGrGalEJ+7pfutrLSk10Kroh1brPgvIB2+3K7MceHw64YcBpdVPLIh+QMVPN6Xok2Zx//1fcjgFcOyBt7cbw3kk5EReD87nGV0FfhwppXBbVpysGGOQ9xK2pMix7r1xb70RLqkQQ6bKhgiKRGMolVQS+3YQUkbQB0bCmv6DcQZpdPdWlkLNlRA2KIUjZexJc3q8MJ9PfH15o6VIiIlSCllJlBsYn56Y37/n7e2ZVFeEzmibqCGxHyvKZE7TyHv/iJSSwQuUiCgDqPvoO1cKPVlLKtmnrvK+vJYKIzXKCAo9VFjQ9cGyFcrtxi0FnNNoozBak53i/cPIkAW3rWEnw3g2KK8wo7/TBCXNRHJp5JLZw5XTwyPGaY4jIkloCWm/UcqCtwJhRhCCKLoSSbqBYjyyNITsVPOYM6oJahF30biE6rH6xOUsUKKntWltqXIA5UCAERr5bcZqyXD2vYx1AnmasINHKY9qI6IN1BK7zVB0mZq6ByZp48g5EePG8rbz41+/8PWvb4TjL3z67W/49p98pNDAdG10SglnNd/+5hPL9SDEH+HoZgptLUorjliwzuG8wxlBJtMOhVGS9pP7RQtaFsQCae0v+te3jfX1IHyYeHgYmOYLxg/405kjRNC6K6GkoLTWZXo/79oVSnWOrjQaP42IsKOt4vxwwXl3fyEVVFUIRS9zRd9zivuk9henjCEa3lukUKTUaE0TjsqNnWmSaJP5+vKFY1sYHs8gDeseSPHg2DeO/ehBNs6hNCRRqUdfeIetcHtb8GNG6PutLx3Kaqrszn+hNcZ77OhQ5v7G3yNNCQgJT2V+OjGcB2KJaCNRWYJqVCVQ44QZPXbyVAwpS4poYCUKg26JHDM190nacJ5xTqO0hBqpZadTdQOKiJYjBUkVuj9souCM6sN4VfoBbQpq4Ugr1B1ZDwYdKDX0DJCWeqzcCP6jRY8av/Z1gx4KZvQMlxNNe9Cyr2F0xYjY1yj1oGLRWpCOg9E4ntcr++0ZMVi0Tx07Yj20Rk4CLQDRsZfKjJzsGeVOVARNVFquuEGhxBM1H+R4UEq8Z9pMaOX6IZYG/UQ3uB9nKDvee4py6OHSX6KqIsg9PrBBbQWo5FJBNpyzqCXz8uULP/z9X/j8px95+3pw7IpSvuAfJpQbcVOXUtYqMN5z+XDh9//sNwit+I///nuWt51jOVDGM05nrBppSSCtYHAa6QTNC4rwhNr48vpCRrCGA7JCNUkrHRIg5YYw8P4yY8aZ2CToASl7enfPSy3w08u5L18oosPrVCk4a3j/dOH07j3z6YGmNJXSU9FF5zpJcU89uN+Yrf1X3JzVCMaxc0xza4QjU4rseI9SAIHSmuE0ob2nmT4V9IPFnk+o67VTtJXqGtfWQczHUdj2QhOKsGcKBanBDo7x1KkLWIEdXX/QphFtHbU0lD1QWiBL6qXkPPaHSFTc2K1aNnVxROcGNV6fnwkhdxyGs4gmusLJSNLRFTGl9vg47T1SSWSztCoptZByQupMZyxLcmk9w1FpjOoKnCJrd/cDtSZaC1AjiP79YyxaW0rub2IjHQy672ofFYJCZadJcK4bnrXxNOc5NoCMMB6hZ0qRnTJ47KTrjdvXN5bnhQXB+Slxrg0rNc0JUutDmtPpgdJOaDfjxgcK9yi8WqgiUe/xDa31FOcmeiknXA/c7Y40SXMKzYAZO/QZunka40EPFPkTXEJ3CJboGXM/wbmOfeP6w1/503/4e/78x79y3BIx0RVo+8K+JWpWtKr6kM1ZpDC0Znl8191IosF//MP3LLdILj3pSzCS84ZSmsmALRKVPesV3t4OUoItFkLoTFwj1d2Y0AhBcn0rDJfK5ZOjqZ5fKiqo+2RVKWgtI2qBO8o3/4NYhZQySg6c5hPG9vmGUD3ct/Gfylngflu2O/X9F96cZhwYLyeakCy3jRQyCo1MiiwSWggeT2dSDozjwDg4xmnADZ4WA7U29vWGMRIjNTkkpBI4Z8m5UKrsYOVUCDnjYiM3cCfD6XTCeNdhXOKnH1IvMXseTLvbvbqrwFlNboG9Hj1DVAhyzqSWUOHATyestQgJtTSqLGjlaFRyiPesT0mVAqns/ZaDVndCCCgZECagpULIRm1glLzHEzRULciWOkGwBODoB0qANAOiZdY1ICko3XDDgB9GhLO02ldRNR6Imgn7G2q64MYnYhDomok54IcLubpuk7OeLG68vj5z/fzK7cuByIrjGim58WQ82jeE1zSlSUKDmWhmJNEZN3ZwHOsCsiJ0D4QqRYC6TxhVJ5Y3oSi5UYQGLVGtzxSGaboD0RYEGtF6TqrQGlqXRUIPIRIlIfLB8fqVH/74R77+6QdEUMgqOY4V5STKdUGAkIpaBbJprB1Ryv5M1Hj/7pFPn95zuvxb/sO/+yvPXzdCiKSwoJXH+8o0afADWlWEFhRtuf3llRYVVo6keoAEawUUxbp1JI6ZEx9/J5H6vqOVoKUFan9umqSlHt4l6fDoWispRF6XjeoKH+5x81JKtNZIpSm1ddC05OdStrUOOpPyv3j8/vbhHMcRoURPVj5WVCloochrIIWDNymYJo9VIGKmKkn4KXQ1RkrMiFJpKaOkwA0O0Iijy7XWrQ8WGh30VGsPIRVNYbTtnrcChEKOB+RGXneO205Ngagkw0mhRo/0mtuPb4R169VHabRa+rSwFXKO2KbQ3ctNawIpDVbehRRaU+lmawX3iWOfONdSKSVhWkQKgxCyy69aNw8Luk+z1Y7ZiGmjtIil9zbGTTRZuL727BJn+gertcadR1I5CLmSaqXGgxg2xD0hq+eZ6DvLtduqakyEHMjbRni7cryuHC8FLSe2Erk8VEQUlD1hbI88TzmD6GGx1hhondCfy97p41RyzpTWc0610T1PRtiuKCq5p8S1CHknHwd7uZJjJO0LwlrcOCG177eE6T/X0uj5JwharaRtYf3xC2XZkMmS9oT3Bn8ZePzmgnKwh4UkCpPVKNUwBkQzaNWARBOOf/p//G+Yzhf+1f/8R77/61fms+PxaWSeBafZIKtDAKVltLP86U/PKBSt9YtCG9EljEpzpE5eNC87y7pz9jOlFWqrP8faI+hM5Z9iFKqgFEGqkJsgxoByvXQXUmCMQWndrxPRE9yk6HzlBj9D6v6WhO9vl7Wisu4LtMLj04ndbNSQKTmjrO1A59If6FAilExJGSUhLivH9YbRCqog5tz5qbmy7Illy8TYEFJ3JIUEbVWn3QFxj1ATZrCgG6op4hZZ3xZuL2+UnLDO9ODZciFcF5br0q1I9HyWJiXGOpyzvS+sXTCt7kR48bPovceFh5JQsdJqBmXRIncxRCl99UNGt0i93wYxHrhxQFDIxyvp2KhxJ9fQgdi2m61jhRwSOcNy3dlrJI2FUkT3i+pIpXTRuFBQE7ImSAe36wJknDcolZHCo2onLGwvb8TrBqEhqyDsR58PbJX1ZWGQGm1nhHVAv+FLixx563Hz9zxOeV91CCG6+Vh2G1itqiNiquigrnR07u3zZ7bX597alEQ5Dpx1nN5/Yv74a5qS1CZ7e6BM5+c22QHiQmKUxDvDNUSGSXP68MT84cxwGcFq1rDy/jIzTpaUOltXNEUuHdamneXdpxE3zgzTyB//8Gfmk+PpyTHNMNxj+y7KU9qNr98vDK5CasRUqK2Q4j19T//04srEHEkpkXOk0gUVpfXkNaXUXfNcKLlRc+5JablSpMQYxXia+r5ZCoRWnfpYW/cNi75Sqa1LDn8qbX9xz6lnh6KiFEzjgD1r0hp7xohSyFaRUmCNJJdIaZV5cEDl2FdSyp2i0GqnZudCSJk1BJYjUavEe8V8mkhtw/kesR6Pg+W7G9PDmQfpGN3Qp8Zfbjz/8MLtulJaw4+WqhvW3jH5ttPJU4Hcws8lkpSq54OWRDoCWteOMdT3oJ6mqFmQc6CGnXJIUtMYLVAajHUYbfoOMR/U2jjPI0sptLxQayLvr4SX555R2QpVaLQHJWwnPaSKlIqaKm/Xlf16sC0H4+IZ3zsuHx7wj+/Yri/sty+EGBnooux13Ykl4l2FlhE1U48VkTK6SryxRHNfPShPq/D25YXruvKhaewRwCiG84iyBmE776eJe+rVvZroutqu1JHSkAuUmroAJQdy2EnLwvL1hZfv/kpLkLYNVSN+cOSjMD2+I+0CXSVCGaQ3dwSpJFfJMD/w+O0nEoIyLPhp5ulXn5CjIwvY9wwpE3LhOLqJu+VEqYImDPPljPUdKjefBb93n3j/qws5pY6TUQ1nJFUHhA48JAlJ8HjWyBx7SHCTpCKIKSNERsiKdmBHjVCNGA96+VXJOXbukbbUKu9VVG+NSi7UnMF0EYix+mdDxk8Hr4l2V9N2W1Vr9T8ra/+WQeVvr1KMZJg81qvut/MWNWq88ShlyftBXJeOIqkSZEM5TYw75S4mblp1eV8R5AKxNZpWqKGh0Tw8nJnngS2kn7GIMURiLrz7MDAPM0oKjmXn+rrwdj3Yj9b3ojXhLvAoKtY5tHFULCW1O+IjghSUVmnpQJeK1f3FovqPqkfjSUOVglgCMR7UPUMU0ArD7Hh6/w7ZCnG93hmyjbh+RSp6Dovohu1SM6o2rDbsd+odNSH2FY1EW4mxigNB3BKagDIFO1XC4fHDCe8m8nFFaYO2jvGk2OLOHgIQoSTytlCOiDUGbS0xvyFEwTowvlciohXevr6w7Zn58Qk/T/DNI/48oepAkxahe3ZqLd0LK+7OjFozrXD3IBZCCZ0be3TbH82x3hq3HxfqcWApjGcQPrJc32hT6ixXoRFNkFsDaZB2wJ0aT7//PWKeucSIsQ47TXx9u/H1+cqXz0tPrCuWbY2Ie36otY7pZLuTqGlKSf0BtoKz9Ry7pOTuqfXekXJXpmkXMFbw+ORpMdJKxciRkBW3tAJdd3u6eJ4eZ2oOxPWgtYIgk3LsPydbEdLdh4T9Z1NaAdFB1ELJn4HZvSTuSqVaG+jOsG33A1par8T+lq72H785KRhZGGYPquIYeop0ldQs7gqNiRrDncwuOcJOTAcoAVb9TB1Q6r4vcT0h254FVhnmYUDWjE59S5tS4QgFaTXaGZqq7MfG8vpKuAXiIcjNUFqmFoFS/WaSVqGsg2b72DsKRG2goJAptZFThAxWjQjdD5JsCqkcTShqCMTc2K87YodwbKxeMCiJTpmUN6zp/VgvfWA6nfsHIw16uODuFUWVBzX3lYqrigYYW5hOhrhotj2TUsMJkLa/2Gq9K6eUoqWD7e0r2nSawFYaIlZqyuzXnXVZOI8n7Owx80qumUEPTO9HtNcIFDnc+P6vf+bhfeL8+NQrDNUxj2iPEn3w1SjdKJ4zNYaOTJF9Wtq0pmXIR6IeERET+5ZYV3i+HogMNVSGI5HdxvDDF9zjSM4OhEbJ/rko7UhNkArYh09c/IVT6xrnYzvYb8/89e+/8B/+/EJB87LUnvxdM1ZZPn3znt/piWFumNZlllIIcqn3slPex099h2iM70Z2E3Gj5+lppuxHH2BlqFVirUTKyjhYHk8TD9NEXg+2uEPLGFF7/IIzbFneDRCaRvexomVXKiLgHpfZEMSSEPG4K5ssTXYhRuv4j//8AvwbV+ffPJy39StFj9iqMIPBmD4cKKlSa0KaLkg+akA2QZONWA+aqijfJWoI2XuTVjHGou7EbGccqoHImeVtQ8geYV5rQTnN5emB0+VMo7EuG+u2U3IBuhNBiB42M7r76sMbmlW0ovpNoBVS+A760pKUA3HbaQK8FBgSupl+QykJWtHcQJKOH18/w9oTnPd9Z7lcMa2Ra+wlzOh6iZcTOQSGacb6AW0t1jukaChnyEdgu74RW2YYh15yjRp/sqxLIMmEufdNRhtKCtQYIGdSil3qpwdiaqSlizRKzHe6W3dQDMYwnQ3KgvEefzEMwwhV4MNB/Hzj+7++sqxgBkUoO0/qfY/XsJ6a+j6ytR6jl8JOSQmpNMoUlLKUEMnbSj1upOuVr999z/a6kpJGKsP1iFzDwSGfEU7y/psLw0cQREb9EWUNSiqqdFQJ1Ra8MrSWOJYbJWzsb195/v4HXr4klqL5/u2vYBq0gqqC331aSFlj7MR5thgNgkKrhVxFfxFo0Yn8socwA10fLRTjNHK6nFnXK+tzoJSM1hHnDP+f9v7s17Isz+/DPmvee5/h3hgyK6uqB6pJykTTzQGgYRs2DYgg7AcbhkBDf6glaIA5mIZk2aYNPlBqts1Gmz1UZVVlZsS99wx7WKMffuucG1lqZonZBsSHOoVAREVGxJnWb/2m7/DwILzS08vK5fJMTBuH0XKYRP2jGUWqkW1FFAiNoSJmu60Ucm6gM+Fe7layFrC8ctJWQZcLuvFFlUar7xjV/qrgdKNBucZWZoJ/wA2GluUG0C3TSmNZEtu2iWW6qnivxaGqKkHCNEHNFKUxg2F0Dm8spol3xnpZSfmKsRbjHNlqJm94+4N3hP3A9XLiepqJyyaHhoZTBWMUkzfshwFnLc0bslVUJT4cNlhRLA9BmBlFeIwxbyyqoLYmrBJrsbsDyjoYAnMzlLWxPK/s9yP744RB4wDQMnVMGdsE71tL7v4ginDYobyh1CQtBgVnhPVQa0Jrgx0tx88PwqbXhsO7PS54Gapdz+hSoFTKtkKGLS8sc2Y+r+TU8M4zPr5hGifSfCUEgwkjvnnMNPLm3Q/x1ou6gVJc5sLXX1+4nM/88R9u/Di/ZZyMqCMaT21OJpAI3a+WSK1i1CMSKpF1XlkvL2yXJ/JlRqFwdqA1xdNp47wUIJOpHKYTZkucLye2ZZZMowRr7V2gRLA+oJsiLTKIUyUy6MSbUXGaDG0xvKyNuErJ6Br82b/+mhHHu8OB8OMjuCIDyaq6wbPDaI3TDU1h2c6U5UJNMzlegcLxYSLlylbPtGtk7y27/Y4vvnjP119/4Kuvn1iWFVqjHhsOJ7ttCyjNuizE+YRRDm1BqSou3z6grUzWm8xHERa3DMG4iXl98mit8p0N568Kzh/8+HOWvGCCLP1KLV0OvxCvK6enK5fTQk4FRcWaJqWCat25STRn8Q4/DZhgGUaPaQ2VM3WLxDSD6dC/YPHNYJTBjIZYEltKQnbNBd0KVlWmoLBO87D3TLtJ2Ba6yagfjbZKmnfExtwaTfABNY6ka5RVRUroFpgOe0zLsixvlZorGsu6VHJcMGGibGIP+PD4KA5ccek9v9zsKWdi2NiZI6pWVKmdhJvZ7UZqceRWSK2gDNid461/xBjH7jhhjSJuK2UVo+FaCiVWWirM54XLS+RyWlHK4QOs+Ynjw07wnkbhxgHjoDkvzmlNgW7sjoHf/p3PGPeOl+cZ0yZs1SxPHwi6kq9X3HBAaS/eMMgqQFYm0LSlFM18EQPb9boyn6/UqjDThJsV8flCzGJIta2V7Ro5p8T6vLElePP5j+G4keJzV5HPWDeRE+KbUjPBaz5790D97YVpvPCTbwpfPhdOS6HGhsegauH09dd8/NmXPA4b2RUMjeAGzDCiaqa1StWNUjfS9YWyda3ffKEUcSh49/mO5isPq3j8PBzf4r1l/bOZ56cTNWusdazXwkWvWKuZJhkIRi0+OrVmjBPwidYK05xsA0oVMEt7DcBaBJxy43C+BqdUuN8bhOB3IzXBuB9x3lG2Ql4y8/PCV19+4Juvz2wbUmYp0KqgWiIYhdMKYyCMjuMQ2E0BHLLAVQ2U0MYKBRuc6JVqMM4SphEdNHOcmeMiyKNJFNsoBTM4hsHz7v0Dfj9ivMOpRKvdJSo4UJV409fVYJ3B7AcGXzHV4csqzAYS1A1dLbomVKsi3FwNa26cTpGHQ2AJkemNY3/YcX4RBYcSK0orvDWkbiFIRkSqS5R+zhuslqV9TRsoLSrqg5YJsBeEUl43yrZS9Q05YqnNUJImbo0UReNGG0WZN9Iu4AZLNJrgPWEUO3NvHHFbyNsVSEz7xg/MyJu3A6bsqfVMvly5lg2lPmL9hLGBZd0oqqKdAtPxnxhS0rycIs8vC8YYarGkVFjShjKJEOSzaqUxONllKusFxpjl+xgHw3l7YV2SDHRUo2WxyEPB7njA6oKxhTdvR95/THzxTeSbj1cul0grCprl8a1j9I2WN9YUxfXOaHR1EjC6YY1iXk/UdII8Cw0uLmzbjAojYRr5zO+oWrNFEfh+eXpCafn70LBad3HtSDp4aGC0EVbW4KWlGizOgGqVZjXNVLY490GV7I0FoiduZLfA/DQYvyswf2VwxtJwYSQME1CIW2R+mXn+5szPP1zZsmWtGtUc1ELJCaNgNDA6hbcNO2nRiLVSHmilIItuT0xRUChKGmllYDxMUh4aQ6YRphFjPW4cSNcVDIzHHVbDw+FIOApLX+WGLRW8ongLBmzuZjm69cGCw+8O6BIwxWMRMeOSFtGvrelOTUIbamksa2GeE9cQ8XNkOh5R1pNTpjQlQwJrOK+Jy/nCQCMuF7RpqMnRukxkcA7TSkc4GShVhLLTRl1lXdH6FM86L5q8zZGTQZ03moGtJFox+NJY15U2TlTrup5wwSmFcRs1XWhlwXQLRK0KQzA4vZDWq2SZ68L1ciXGb7BWgNlZFfByuSmlyalyORe++mrlw8fI7viOH/zoBwwBLqevMCbxeDAk61F4jKpYrzGjZe/BB2H/tCaqjDUnIdXXq6glknHeEVxAG3Hr3r3bc/is8IMfRZ6fT7ycr2yboirLw8MDP/zxG/a7ibTNQKZ6Bc7gXcDqRopXqQCMwjiL3TRUTVwyRlfcoPDBM+xGrjEyzxE3KHZ7z37nSBqclcmrMYqa5dwTHLVWjDPYwTCMBmcUFmjasOrGEhfxe2lFpr3adK1acWz784Lxe/M5U8o4b8nrQk4rcRYvkXWpJCays6xNVFFyLqRYGazpLBTBu9ZqqK2SW2YwgpuNGZJSFGNQTipv7xx6GJgeduhpIlcwVbEfd1ilifOVmlO3lfPUUvBOmBu1ZmqtIhAssEic0jgjkztF7dZxfVimFUoLAil3+JW1spMN48B0nDh9jMSUKVsRTuWbHWGcmJcFTSNHWS9ot8dgWdcVU2e0gbptNNtotmGnSQAbzmL61LY1kQ5Zrhe2y4xpmv0g4tG1RLG084acGm5yjA+BpBIpyZywGSWq9WZg3B/5+PXP2E4zg/U4q0nbjDGSAW4DtFIiyixYJeAMZRyqbMSXldgK49ExPQyo0aCDRSnPRuacZ8q80q4RHRTeHDBD5fgQyH7lcJioq5SXOS1oXTC+Mh4Uw14RtwslvcE0GZYIVFIQNsbItB2t0MMOrzW1rCibcENmPI68WVdyaSgz4Mc9u+MRowxFZUoSc+a0XtgZhQ8jLWkwDopF6ULTmZYbJFhO4qGjncLogg6GcTegVebh/YTSEK9VrD9SwelGU424JrJakE1Nw9Ad0LUS1QutMLXirKwCa6uUKtRAPlE9+DQYJWj/Aj2npqFbZb2eRG5/U2xLYl0alcCSCuctsZVEiYmWEjkbilZQCnYUBEptlVyTaJUqjQ4eZwy+CbWsFaEFKWewXm48imIYLSVXdC04b2iuYp2oqavgZEdZK1prnB4l22kJAIMGJQptunuLtiLZXSGlR0MJITsXvNHYYcBg+c3fMZQIH778KLzUHIXWts54F6hppaUVSqLF0r8cwxgmnKlsNVL74VExo00kxSi0oyZKC3FZSctGKzBMATcFcgKlK6lktvlMKpoQ9uzfHzCjZZkTaRMbwYalFYsuBtsUOS5c55VpGKhW2CCD10wukOeN03WmNhj2B8K0RylP2s6o51VkPKoQD9whYAaPNgHnG/NVMw1X2FeczSzzidFbHh4c5t0bBj/SErRciasnbmd80Bw/2zG+eYMbgrjNVfpZaNi++tBKcLtK6a687kibpdaEcQ037glxFRU8bcEGlGvkuFLLKvTANZJKJS1n6uGINZqUNkrO1NJYU6Gkim2emgPnj5mqImmJhHcTbgr4oDg8DvhgyUshx0paBO2WUiZdIm3JKO9QDprKGGWx2lCsiIQZrRm8DKWg74g/iaUbAutTaZKbwdP3Ck7vpW9MsaCaIi6J5RJJW6OkxjIvUk83Tc4VVSAW6cNsyxz3HuUrlSSAai1lj7aeyViUMizXK9u60FQBXck1onKEaqhb4fJ8JS8zNUe0LgyDIUyesBuFkKyd2ANYS22KmJssexVCe2rSpEOhkWktUqv4d5qmGNxAUYX5fOX44NjtBswPTWc6ZK7nE8YVisqcXr5hF94IRC9vvY9MGFc5hBFrLHoUh+9tnakxkrdCLTMxZ5Z1BaTX1GhGf2T3uGMIjVxmqBVrPWkrXK4XUmro/SjQN8wd55mWyroupCQi2E4bUJYUN+bLyu7NjnnbMKWyN47gPa2dmLeCmwzj9BbrRtKq8ceZmi3DcSTsDphBiwGVlh3lm7eZeBmZAqzlyrz8GbE5Pv/BA29/8J5hnLDaE9eVuF5Zl8A4Oaa3bxkOD7jpQDOeRmbwHhDV/lJVz0E37xwtiBvlMN5ilKxDmmqyqqCAWiFH0nVhfTqznma2LQGFOHjK5YIfAlVVIXM3SEmjGNBKc70Uni4zpW1cz/BGFR6UpvVEZHTDTpZsM1Zp1tkwz5llSxQq40GGU80oyA2y4J0VWtBmPUv2cHwNTESp9haYn/74rsd3BmeuGyopQd4vmW1JtAxWW5GPUA1TRCGbXDqtCGF2OIsJGu2RftOI5J3SCqUFpdNapWlF1QYQY9UaI7XOxLXx4Rcnnj6c2C5RVPxsZb9z7B4HVAF9lLJCW4tWMmVDN1IRVJDWltYUpUYBHDfpzVqubNdEXguLykxDoa4NXRR2FMb7w5tALW94/igsE+M1Ja+sy5XgBDNZayWtK954vBnY1g1/3GH8KLKgRQK0xJVtE3Ne5waaUVzXJAr2R0s9gFIbUFHG04w4ZS2XK+vpIzXCugg2t2TLZV5JsTBOK3VbeXhwaAXbWvjFzz7y42GiaU8ujaK12A1YS82wzgsfv/lAGPZYr3n/o7fEFNk/PBIOnqKj8GFbE1HrnePxBw9sD56qNMV41tTw44jbHVDeMU57JnUgbjtyOXZDpwMmDDQjDBijFMZoWhPKlbHCPBF4rkx7a+nDxVt5uKUuIQmtZlqSFuT81QunL585fzOzrRVjG2/fPZLGhB0Ch3cPqDBgvWW/U5y/vPD04crpnCnasyWpXnbnSB4iWYlRbykZraQdU1qI0jFVlqWAhnFHX6tpTAWVigh3KcAIuF2GQO1bv/6uENTfQUv5bq+UdWPTFUMjxkLOAgJ2zjG4zN4btnVBZek1nXO0XNFUlGkY6zBGLMunMWBoUAqlLNSYqE1TUwfPa4NWorS9Xleevr7w1U+fef5wZV01g7U4VagPDYrCKKF4+alSisEpWakoY1AFUodLqa5lVLLs7FrS1KLJqyIvmm2NlAEG45n1GZc1bvBYC+NkKHkkLnTbNiMIl8Hhpj3MC9t1hXJi3DXUOKCURWmPC40UK9saBYSfGxZPWRSny5Wnp5VaDbv9wrv3jsOxMkxGdqetQTGUubGcZuICMVZiVhRgq0YmieuGrQ1bB7SuXK+ReSkc31fe//hzVI20EtmSAN2drShVKduVtSb2D3um3cTePjIc35LKQslQW6ZWOaxFN8KbA14dRDmwQcZhx0f8MNJUE86oUtC9T7XRAtF0RkyIWsIo8VmhKfFsVZKvZASgoCmBwfVzAJWsuqO50WLLEFfWy8z16cTp6xMvX61sq5gUtTRjvUGHhdYCD1+8R/sgWfK6siybeM16w/HwgNYbTjnymkgUUs2C/NHiJ5tykguY2kXNhd1jmsJiIMmfUU10mJoqQsyur5NZmlzgSgid8l61/v9P5lxSYwwercQs1TiLHyrbsmFaZDCF46DRa8ENIyEMnC5nUc7Wmpw0tIAzozjA5dzBwkmoYIjWZ6ughgEbBhSG9bJweVq4Pm9sl8o5NlZV2RnJ1tZU/CDTwJoaxorFHcFImWttl+FADhSOZqAaRTWWeblQ4kxeCiUKxG4OlWnY4RkIOOYayWmmpMQ6Z+KaGI8Dw+FIMANuH9DGk5N8KTHO0henIgfUNrK+0rp/S60a1Sznl8gvfn5lnrWANJZE2xJtNah3BhD7gnXOrNfGfFbEVeGHR57OL2y10KxjmxUlVjwbk1U4p8krXM6Nl5fK+NawG/YolyjqQtOe3ZsjthPf3eiYHvc0F8B4olbkalBuwNSMoaF1ARXxxuPGiZY3lsszznncXoSnpXOPtNr6IbaU1tjmF0onP5M92nqK0ii0UNAQRJnqgYiqMsRSAPKZmlY7aFwW+UYbUWTPgpRKW2XbNAVFUzKVdoMiDDuG/QPeT6zrjHOaECxKOdZasc4xTbJf3lZR1m9i3dIZI5pWtTjFDZoRg9FSapco++qmC0YLSkhZi/GSLFr/303xAF67SmPk8r2Vt9+VNX9lcP7rP/2az98f+eFnbxh3hpouDE0TvEhztJZEGq8rypWWCE7h3YCtjWUuXM+Z46HgnJQfNSW2eaamLHo2qYlxjZnAGGLJxEU+eJro8yS9kXKSaR5K0DbKYVRGl0SrivWiME6hhj0myO2bkxCuW5NyxGpP1Q2rN2iVnDb208R+NOwmCL5iWub88QNLzFyeZ66nyOnjxvWcMZeIHQ+MD0ect1gUflxI5UopGzoF8royjHusMjjnid4TU5QSaS68PK9czo1cAutcWVWkbg1dLaDZAc01Yi7MW+U8N+ZZU68zT2tjq5VCQiclNoEJtrlgJkuJmmWu/NEffcmqLJ99duAhwPWa0Npy3RrOZYw3EAyb0Sg70owD5IK1WkHJAmmsmbieKYB2FkEBZVqboTpRLGgNZR2tK0NoJeT5vDyjq+8lfqUhbI7WNGRpC7AeZa3wJZvQ4FqNWC3sj5oTqmZAcLTOaLy3wl4aDMoqqlaUplhihroxKktrRZTb1yCgB28wThOMxZuAHwzDgIAVShYOJlqgdyXjjEMrj/OVsfvktKrQSlYruVWMqaIGWRqqyUDIGofuNLxbz9l6dN4wv/Dt7Pm9g/PDNTHsM4+5sHdasIvIwKAURcwKZw3DMPJy3thSJlhHMJ6SE8s28/TxxMNRMwYrUMkENVbimoid42hcQfkJ0FznhbRGrPMC0RoVpIw2BR1gOBgObzyHNwPTg6gIUBt5u7KeDd4YzOjBgM6RWiI5i+aLcHULLResFlNYP2YObwbGSQSQa04s2yYAg62SzoXl1NhmGVCdjiuffZ5pQd6MdYYE5JiwIRPXC9visF4OvJadPik3rs8z6yViTGDJheuWsC3TEAMm6wo6FOwuI+TnwmXJXBdDapWtKpSzpC2zU1IoDMFifSBlyLESYyGdV5aPMz+9XFkeA9Pg8M6JQ1gFYxzFWJr1uGkvoP8cBV9sjFDS0krbImW7knMhv1woOaFNQbWMSifhbGpLaWJ4ZfSAM468RSgRr4KgpfoQrtHIMaIztGZQ4QAhiC9N2khP3zBfntEgh1xrGQ4FR7MapcGFgD/sCG8S/tzwTUrLd4/vWK9X/M6AV2QSz8/fYAy4fcAfRs4fr+R1ZnRHbHXgtOzZgyc10d5VtaKMRZeKLgg6SUPJolNLBl1lH6D6pLnkio4iuSqUtEojI1q95q6M8JpDf/Ua5VcG5+/89m+wn0ThfM2J2qKoe1NxoYnL1qIwm6JOlhFHbYocux1DScS1iKT9OGAHhQaB0qGk1KkNtCHXjfNlFXu3mnHGoNVCjAmnYBgNx6Ph8x888IMf7nh8P4LN5Ay6NPKWKNtC2WZMHkWOMW990NBIqUIp5HUlLQvKWuzgKaqIIvo4ELzDGE1NimVbMFWjq1gXWqOpJNbLhfl8wg+yJ8zbwpaSaACpQsoLl1kTcujqARXnDElLsDkXSC1QtwTGdOuIyOmSMa5hd43DYDB+oLqNtSZiaUyHPSpVlBEN2aFu7EbFOHrG3cQyL8SUJDsthZ//9GuGnePt448JowOdGKynqcyWEyomglYYb6jNoJRHGQtGY5qhJhl+xcvCcll5+rBgrMPvNGao7N+6bkkg8iaCJZaeVCQjR5wbAbGWT+sqwP4c0Wicm9BNKFgtw3J+4unnP+X88QNOB1Qzoq43OMJhwE+ipO+NZZpG3nzWoGjCeOV6zbQa8c7iXK+gdOF8emb0AVUrb98+4rXl49dPLNcXvH3ADo5gLW6aSK3KEKwrXGyL+JkarVDOgOt9YtG0pKg1k2qlFStqH6WimqxVaAi+XEk/bazGGClhv50x/wKZM+TCg51QZRUmmjUcDiPzvKC2hgsyjfLBYL34Zq5bYqsJZRODUbx9mJj8QF4yGeGFVgzGT4zHgPVerOJ143I64WiYqmhp5fNHx2Aa86Z5OE48Hq2w3Q8e5cBPI3rVlHUjxUiMkaIURSuaE/Gmkho5FmoVmYmmLbgBqxzKWYyuRBSpaQYbhOWQCmlNlFhQpeKN6rdxgbZQ8kxKgZI2Sk2oUXiLVRwIOs4yyy1MxQ8D7D1ujLzbv0W9NH7+9DUNTa2VtRZ240Cqli1q9i1gR8P02DA/22hzYVvOeBcIPhAeJiY7ENrK47sDn3/xnp/+6Z+BatQsJsQsGT8OVGWJGLSqGG/Y7Q8s8UqzhtIquSRyzVhlsVZ8PXRtQsRWclF985MX/uxPn6lNs3/r+fy3HtkdDW430YywGFOR4Ucu4IcDuID2gThfhfc7n2lpobWCHgPWDCiVyBVRm9gW1suZeLmIqNoiqxblFft3E28+P+ImD6rJtHynqO8dw+6Bj79YePlqJkeBdipdiXGm5JWPH0+03Dgc3nB8PMqEPW7EGkkRdsMBhRV37pwpRRQQtNKCcS4V7y3OWwnc2jCTxzRPzYlS5ffosjivAWhEkEDL0KvRPil5b+3oXwC+d/3ZC/ocGYLCecWWZoy2onqNAAJkIhUZVUNbz6MKbGuWm6QIgKCWRFEQ10ZplVRh3O/YD2/wu4E1LtQaGaYdeV4pOhKUInjF7s3EmsCHxsMbw+6gwWSWlDF4lHGs25llXckx43wmn67iJK00qYg0RUM4l1o57ODQrRD0hLdKbBJUJddGnlfWeSWnLNQzXfGDwlRoRuEnBbqIsZENOO+pqYkIcV+BTGGUYc+WSGUj14L1AT96aIbHtwfGb2aeXq4yobYe7XeUplk2KDXgTON4CLx5DNiyCOB/1KAi7754x2//+Af84qd/zMPne3ZvRsJHh/qmoA20lElRc7mufHi+8vij3+Lh7Yixhdoiu/2OMHqs98xxIWew0xFjOn5UO2JZySGQjGWdE2kDZS3rWilZaH92GLvm8FU2ecrJ5WQNYTyyrTNxjcTThevTBwyFECwJIJ9xY6P5AYFMR5wTEa+8FrYrrMsKruKdoj1MrCkS6wamkVNCuYppDR80SmVqjeQGSss8RAvMhKIba80Mg+Hhi3fM1yvbtuGnCec9W4zMJ7kUtFKUWjvl0FJVRimNNQKgKGSBkyrd1f2zaAG1SquCqb1JvygUSosawmtQqm/9+nsHZztnSitss2bVGWUVaQQ3ThRdUUY0TEUYqqGt2JMrLCUmtlmEoGiV0hTzklmWSKoNP76hFFHkzk2jlMXZCXQf4uQmTmAl0tKCGYMsr1VD64ALO2qTiyI3DUo4g+t5Qy2A9WyliXErShyTg6Ia3cHwjTEExv2IphDnC3GOpPlCzmI3X5pM8sbdwLg/YKYjZlQMk0Nbj9Ka3ApmMMLxyw2tPGlt4idyuYASxfHdZPDB8+WXX7FGUSScxkEA9NvCh+eZEDTh+JZ1A/TG5OCzx8BExWpFGAxDmDh+FtjSR/ZvPe9/4x25FvzBE/aOcYmoDTKVmCNffTjxV8KBtz/6Tdb4Qt6uBA+tZZY1EWPEWFGYo4peUC2SXZRqhP2Odz/+guYWUs40vzEdRzCKWAq5G/koJX46kAVXbBR5q5QlE7Sj2JG4zOSqGJys4MiG4hN6cGjdGPcTqjauKvPy/MK8rUzOEdPG9Xql6sq8XcQkyRiUDfjJMUwLzmtKNVStaErwydU0shXtWZzDTANjCDSrGesB7xw5JWrMoqSXq8wdWhPerNJoZaFoKArrDN4JaEI1WSla5yhNiNi5t5KSIU0XnjZdYa99a1AEn/76ewTn++NB9kNNo2xj97iDrNHVonBoKtrKfspYI5hN24TjpitOCcukbIW4JPIq7P/cFOu8sVwXjAc/GswQKFHRXAK06P4oBVYxHHc8vNuhRzi+e8QMgwhXmYC1s6gZFMvpqycuL1dqXTk8vCMXWNcNGxypRezU0ENDOUOYHONux3iY0Gmj5MT1emHdVlAN6y1+sGwxMu0tbz7fw96hB4O2De36DnfwxC2xLpF42di/HdDF89VPfkK8Xjk87Di8P6C1jPOHwTKvG+sSMXpgOhzI3nN+PqPdyDi9pZaNHK/oJvzYenA8PB54++6dkBBaZN2uDNbjjw5dLW9/+I60rVT9zLw2UglcssaOI9UMJD3idho/TKi88fL8C3KcMUZjLaiWoSbZ+6WNuFyhZuy04we/c2T/2cbp/ERqV6bHnVCNkwhG6w6N1Lqim/jjrOtGWRZevv6ArzC/iKZUbRF/WvDeEZeG2gcsgdYKdhrYhwEbogiKf3jCeYMZFZdtFtRXBW0t1kxovxM1jlAIO89aMm60MgXWjmVd+PjVmUzj82ni5fmEViJ5E5xn0whEb4sCdNeK1pUK1yVRMjjn8F78XV1uMoADas5UXURZwgmGubZCLbWLdsle9LbzVErwtL/MSvnewHejK1pVgh9EVr4pSsy0oPF26HA7kSQpnSKjNd2ZWGN2Hl0V8bpxfVlY2oJ1ilIBlbmenxj3nv3hAWOhoClWoQ3kknjz7o1gMH3h8GaH3Vn2j0cSmoqjNY1xA2FUhLeBeC6cPmxczxvXl6+wZhCmh7rSnMKmiq/gDkF6sRxZlsbkuulQLUItcxZiJewCuRYwGWUK/nAg7EYxQkUxhJGWFcv8wrZWlmvk6+0JnRWXp5W0JjQb+7dHxjBSsuF4rDw/P7PNF1KNaOsYwohxG/MS+fkvPvDb0yPBH7henila9aHIxLsff0EqlbI8YVsDB5f1jA8jj5+9pcXEMFi++XDl43Nhmt7wW7/7u/jxwBIrj48TZMPz8xPbktB01E4plBjJtUHJtCyWhdSE9hOHzz9neizYF8MWgwSMHaVkqxWjFVuMsiZRgbQlcsqs5zMffvENKhZyzBg3UAs8XZ/RqrE77vBvd4Q6MuwC2hnsNBCM4cE19m+DIKtSRJluq9ClSbzfYcKBhsOYF2wwvP3skccv3jM+TKQtsayR5bKitKLGTCyJ+TqjcqNaB156/mmcSKWQSxYvT2uo1XB+mZnnK9579sXDqFElUpCJbEZIBGZs+GHEua7fpPvwqAtL3yRUJEhfg7U1qLX+m8LvV0hjlpXWKvMm/VxVGW8sy3njsNuBaegwgIHlulCzwlTNuA8E79nWjWXeqKvGFIdrGWUbjcS2PFHLyOA1plSImZoS1sDuODBOcDiI98XKCqPCTXvWWMht634eBwrhLrq13+3YDgs6G9Y503KhpUopDbwm6YIaElaJlULaFtaygmkQV7QDYybQjagXcq0UKjUmlu3Ezr7DhQEzeFKpIh8ZN0wulCg+Ms/XjdNTZNkaXi5xPn59JmuNcg4bNFYVvIK0Ra5PL7RdVx0oEWUecPaBuD13EIX4odjW2E4XAcXHlVIyxjQm59jvH8gFHn70nrCD6iCpGT1ofvjDB9RO4wdNVZXT6QPbdkGTupUElCUSn0+c80rwmsFbaUVyYVteyLGKzZ5p+GEvEibGk8tKw7A1Q7UBpRvL9Rvxs7lWXj6cKbFiGFjjxnKJ1NooEdHunStLfGJYZuq7PXYMjAeP8gaz8/jJw7MizwlS3xVaBU0zzyujGUTxTil0UNjQODyCHRo5V+ze4x6cJIxBYKjKyRAseIvyBa0N/s2R3TiwbUvHXheWpVKtIX+4So/pB3JaqVF2wNorMA1jqnjkmCZaWRaMk8pREFDicKYw9yx6y5p/IT7n6EeuywXlHDFmBhTTuGPOJ9btzPHtI2jFsmxcnhbWS2IZEusu4YKnVbg8v7CcLwzaMQ0OoytNg9MKpRsuiC2gCZpUMsZbHqYDxuwxplJKxKiB4fhIxRLLRmuw5shQN+bliiqZfL6SrhHTHE41ihLrwRKhFflisBmfK86IyY3pk9UcE2xJgMwguq3W0UhY7UR2olTKNkMJXfTaiEhyEr2fFht1adQZ8mpYrwv+waFappWFVgNT8ISHictx5DIu5JTYysbTk+jcPjzsyTnzs5/9gjcHus5SpLWC147T0xPLOmOd5/C4ZysLRgd20xvO88r+ccLYxOdWMzwk5ghbfeLH778gqUKMjdo0ZtjhB09LCzVu6FYwqrDMZ8qGWFyUKhmwKdb1gvYOOwi3tulGbgXtAtaO3YU8Epcr22WmxsT5eeH5mzMlKcbwIHvxrfLx+YX9OOCt53KNYDa2spBV5s3n70mr7ENbhuWycvrmzPK8CB7Xa9EjCrICK5yxNoJSvHn7BjNAU5V1m6kopsMIP7TSEzuPBdSSoBXmeeMwPYgy4fGBqhXHhz0lzaS84Y4atztgwzPr6UIsG7olTFd/x2pcsAze43cTDIGo1L3PdF4c6FQfMBmt72AEOWTwnaK1vyo4rQvYqfK8ZVZVGa14HE7DiBtFyX1ZMx++PvP0zYVtzgSfmafYtWQ127qyLYlspaaf3gwMu1HkBrUjjAE3BpHhNAprtSyOvWZdz6xrRpkJE44icFVbZ9AbchE9n9OHJ5anE/mSUJthOyXWJRGz0H9aa9jRYqsS0ehZZC7s4Gka5stVPCJzZdzvCfuBUjVUK19Ga5iqUTWKpEnaUF70YQuNmBsGiypiL0DeGLzh8THw2ecD7gDTzmGcwjTND754w3yNnLdnljnT0Djr0fqGbMqoOtCio2RLzZkXXqg1oQy8/fE79ruJOhfOpwsmXBimI003zHggqMbjrnDAoAMs8zN+eqQpjXEBpRTWNOIpUcsVZwTk7r3Gas16XSgxUUsVbxldyWukUfF7mS43bVDG951mpSnHcn7m9PWLeJOmLCqEVfP08Ylpesv5vKCa57B/T9yubOuZ42PAB4dWRg6686SYeX66cvpwZjslatSUsmEz+KYotdGMZY2F0i4MIXB8OGB8I+aFUiqtQgiOfLCkWNgKEBvbAstzFoSbTzx+ccSOB5oVgkZWiUwhN7B7z/GzA6Vt1GUGqlwQQWOHjh1Wmi1XASfYQXpgH/DO3+F6pvvMSlS2HqQyxf2umdB369buJtZt5cvTC7kUmlXs3w7sXaDWxPll5vnDzMevr6zXgqoavzPooKhb5Xo9i727sRQlUvjTfsJPoKzGDBPaeVowKKMJfsJ1gbBSZWepbcD6B5QeO9KkyoqjiYxmmkVPNS2JvCS288p2zZQEuYrkSPBSZtdaqXMknhrWDaAVWy0sl4X5stLWQmkWnFi1pQ22q4hQ+ViI20rNEeuD4IeNw48G7EmYDCaRamXca97s9zy8V0yPGnvco6cdzTicDRzej7ydIz9/unKeNzQGbwOjG0lLQhdIY2B3mLguF9ZZrCCmncd6RUob1zWDzizxxA8GDaowThPbdYedLKrGDirYC1ungUKqBj8c0Hkj1oqqhVYTOW5i7JPE+VusH+A6zyhn8EMQgWlTcSEQdo8Y7Ukpymoqb1jzkcvLlaAVu+OB958F1rnQ6sx8vbCcr2gz8s03LwKdPEzsDw94V2hlY5lnUmqktZLnynrOrJeMqjJdN8pijRHIn3M4KwLi2mq2FFFd1a6kRIziCFZRGD9imuf55Ynzy0ZZG1YpTqeN6xzRU+fZxkRKslJDg3GK8eBJMXAqF1HEHyx2dFLtKVF7vG6V1DI//NEDx+NbfJgwzspISGsB9tNL2V+Ksdq+Z8/5NF/56vnEvMkBmpeVLRWG5qlL4+mbK6ePG9uloBDFaz8YfFBQq8g4KLE98D5gjOyFpjCig2M47LHDTqQqBi/u1tsMrYjGbHVQNTlqTtvCsqx9f5WhNLbrhevzB5zSeGPJLYGqDLtAihmVIJGxTibKlErbCuVSqJOhBUtTtXM/Gykm8stF9pk+ENfKulWMgoymYmnK4vwAJlCbF/fr3YG2u+D3BqMK427H7nGHHzfMpNG7EXc4sDs8opJiNVeO73e8/XzP+ZRYloJTMHpHcJptO3F6eSKtUOOFEjO6rHhrcc5RVWP/uOO8yOL862/+DFRg//CW6ncYP6DUrQcawfhOQM94lajzibpeYb6QzhdKKzjv2NaNliFGQXlZbajV4Vugro31slDViNmDNoOQtmMUxHheCfsju+MDrmUOb99SS8XYVcTWPqwcDzty1mwpY4whZ8jV4JWmlJXr+co4KS7PK5ePG8tppSbxCg1DwNiGuLYphimgw0DTRsTJWsNbT9w20lq5nlecU6QiVC7bRDFw24q0L6qhY+XnP/kFKVfsKJVN2lZQBZxIkjgLD48TzjaR1buxZKxUbvNaeF4au88fmI6f48c91olDm9MO54yA/Kl8yvW80cm+Tcn+twjONUZqzBzMiGuasUI8L6xOUbaF7bqhKgyDQ1kIOzg+Gh6PIy1VQlCkrWLtgHEeYwvXdcUtiv3k+8JYY51jS5EcV9Z5Ia0rLTfSmolboahETuIERRWvTF2gporaEk1pnBblBKXAWM+2KtymWHSlkYT9giyMMYZ0ccRg8XuLnwb27xSLudIyrEu3KXxeqHNi2jmyFdHoqgZytXg/inYSjWE6kvdnprIwft6kbPcO4wPeyUS5tMrz80ds0wxWMUyZ3/qtI7rAT3/yRNpOUDzDNOBUY3d0KJW4bqIrNM8Rq1eU0Yx+oCoj+F1dKHklxo03b98xPLxhiYmmtUwSQ5AWpxVUWYmXbzh9+cfYFMnrxvk8o7Rjt3tgmQvPz89d/6aBKtRkUHUWfaWgMcNAyxnnZMAVfOA6z7SWMfuR9z/+AuJMMQoXBvZGHMfmayR4GYksMXOdN96+O/Dx6YVUNdOu4ZRB1YpF07r6oBhzJYFeaSWW7yqDbULTqgVtFaoosaNojrpG1lMhm0jMjXnN6BYw2jOOO5b8IiLcKK4fnljnC4fHI9M4kJeVXCLDg8cdR1QQX5dp957WFMt1ZdsSMVbOp5WXS8I/fsHx899h2L9DaxFaM8ZijZUg5NOM2e7DIKHTfU/1veO04wfHynzO5OvGqDV6q+gsdgjDGMRhzGhMaAxHw/7gGZyCbBDbNkXwgVwVYfDsHixhFPb5jSuH0sRSuJwvzKcX5ucry2nh9DSzLrJALkUwuU5nKBtOKbx1GC3CXeKMJQrzqRZ0EBcvdKVEsV9TWlT5GtBSo6QKTROGkSns8EbQPrU15nkVxBANp6FZiwojuJHcHCRkKNSqoGXGPa0sjEPDj45mrJDAtczpUJVSMtu6ohRMQ0U9GvRvHjEqcz5ldiOMQaaB02PAhz3joHj+xRNxLsRcacoyjAdqA6MDylWGcCAHQ8uZwVsKiqJstzM0pLKhVaXVyPnjL7h+/SUubeStcrkmjD9g1I64NuY5o21jfxCV/6qhxSagBKOlBM5irhusES2ilFi3GR0GxjcPpIuoUTgfUK7QquFtVNR84vS8Mk0GHywPD0dymUlbpA3Sb6/LQk4wDF6Mh+JKCAY/aPAd6BIcxhm0M6gKzgVKapQMMSbimklzYY5nlLaiNdREy3YKATd4SobRW3IrbMvCtRTUtCOvog2VVourFX30qMmhrCFVkT3Z1sJ1jpwuicLE8e2PeXz/G0z7R1wYsD4I0+YWkk0y592XE1mh3H58r+AcRhiDYj1lUJpktAhzNcX08AY37Gi1UlvBBsO4d7igu2lsIhwM496gm2FdMk43HJqchGZjrWJbrqJCpwqxNNKiePn5ytPXV15Oicua2T0EMXeNZwarCaExjo02FpzRlFoJQYNRaKsxWehLVomXyLaulCQMkVorShmMcgQ14FQQ7xRrmN4bUIblsuBoDA+JNBrcYaSNI273gA4Txo+k1DDKoPseaxwdJTqqaWAcYZrEiRongOoyo/TGNAYOw475fKK1hXEq/OZv7djWgmqWGMXAdtjBu/d7vCuk0wLXmRozpWbW9QPN+Ve5DydwsfPLlSX9CcPhAbWbcLsdWivhvLbG5AeejUe5gZylr8+b+IXmUrF2Yj8WtEuMO001mc1kmlGo6lHBgbU461FNs84XlBUXgCEcKFS2lmgtEs8nyI39/oHgHxjNyhtlKeWnxJeG0wqdV4IuAsC/7QWVY6lXUiu4o2VyR6yGVGYqDRcC+8e3Ar1zgZIra0z4MVATXM8vpFwwfWqbi5hV2cHyME3M55kUI0PwuN3IYDSczxitWa+zZG6rSdfI7K5i7lQ16xpZS2VZC9dL4nzJaPfA5z/6S7z5/EdY51G2s4sQ3eamVOeWvwah+qT//PTnf+vg/Pk3H7lcVqo16L0Fo9hofDhdcZdFnoyGH600486hnaLESFUC7zNN0YrIg1zXlaxmBjMQlKemyDJvzE1TlKYlESVerhvrUli3SkqNpw8nKI1gDX70KAzGislpHRwYRQ1G+qxS0U7WJ0pZQnAoq0X1rTZKkqxtchWZia2graA8jA/EWKhdFMpZx7Tf4yaPso7zZSWMj2gzyhTXOBpZzHuHA61m8nahNgsElB5QWlzXWss0NlJtLJto42hTcV4sy1cHyxxRKWGtxxmgVawB5wTbnGqmtESlkOKKRpG2wnqKDH5HjJUlP/PZj3+D0BraGrJRImqmFNWPHN5+jqdw+sWXNLtxQDNOe5Ytsi5RiAwusNsPNFcZpsZyTgR3wOi+t1NQ44KxipwT4DBKyXpFN6w1DIPFaUAVDm/f4oYJQ0brwnFV/PSnP6HqSvAaO2iZlLaCDo7xOBAmGPyAUZptXUhXjbYWvz+iXEDbgDaOkiNpTSQqKVbWy5WyJkyDyXsyhtJPekobMW5QG2mTKmY3jYxhIK4rOa6CBLIOMMznjaYVQxnJRnNaI0tWLBv46ZHPfvBbPH72I9y4w3svzJPbiuQO1RNAgjHmXsregvIvtOfcouJwfASaYGa14TIvpJhJl4wzGmsVymkxJs2FopEvrFSc1rTSiGmTjACoDDoWylbJcePyfCZuGeW8NPRXoZnFmNHa8O7dW3KuXLuFvbgFN5x3uMnTDhMhOIwqlLRStg068mJLEYMWBzKlyevW+5OGSpVy3WhktJ5wztKUIs+izXt+vmCdY9i77u6s0GrA2hGFRekm5jRVdG+aDbjhIDKdNHJV6CpfilKO2hYqFqMhdc3a2gT+5rymNUPcIt4ZpnHCG9uVBcUWw+8MNQqIo5SNtvWeu2hKjPz8qy9RzaF84M3DW8bjgbwsosRXMxjHmjWH9z9kNzjiOrO0J0Yth6CcZ66nBTcO7NxeLC2cw44B5wzzx42PX33NscJ6uWAGoflhPE0H/DjSbKOSRffVCwmy1oU1vjAc33No71BBoV/OfG7fUWOipEhRhdSgaisC4daimkErQ06FGDXZDviww+3fooNjK1UmwJeZbV4xysr6bN5QuWBbIwSP2Y+YYeCyJD4+XdAKnPPU0ri8XKhJkFKtFVl5tNY1hEXeRl0jTVva4FnXwiUq7O4dj5/9iOnxM8wwiRvcOIprwSfAdhn6vJattzL2057zewfnYCeoGXQhBJl6jT6gsyLVRlo2thK5Phce3+8wdYfaGSqQt0KurWuAIg7HaFo21BncSyOXzOWUSTGhfaN42JabyW4R0qsRDKfRmtYiKW/s/IgbAm4cYJzwwdLKSi1atFpqQxkjtvZVMI3Ge1pnEFil0Q3KlmmmkuYVbTVVweX5wunjC+sa2R1liUyDlCJ522ipW5knkcQU5oFBoaWkdoZaN3KN6NqzB4rWFDUrrPUMXjNvGzSH0k4U6BBHKizkrYoA8tGSvOHNZ48MnUEyPA7dAr0Rtw2Pp6yFtiYMMF+v/Os/+Jf8loEH94U4cVNJOaM9zLEw+MDw+EhKC3iNKhU3Fh7twHg8Mj4OKN/AANZQrePl/ETuIt0lLtRoKTky7h/ITfSSWr2tujLBe/IaifGKcyNLnGnBoXcToxaCc15X1uvMuiyUVinVY+0Ob7rRbFVoqxj8gfBgMNbRrCWWIjIll431vBHnVQD7VYn1o1JUJTMkO2jsaAhaMcbAOm/sxh1UWLOUuFqJaIDWCqMNkGhVZhghDLiwY6mN61wodsfDw2cMD2/RwyhlfvCEIeCdvUuP3Pw37zB3pb7VY5ZS7n/uewXn84cPGAPjYeTl+UXAzLL2Q0VB1rSSUboxP81i8DNqmi53hQKtLUobSlNsqZBLxDrL6SJiSHR5C7ZC3GZhA1iLdYZ5jaT8hNIGF0StzQwGN3qatzSjRTgKJbovzQC267pq7BDQaIzWmKqwCmHJFJGbKCmhV7CDpSaRn6ipoJqIXAvTIlK1+C0ulxPn00emksEYjAtdGe5G0pUFc0pSHjrjUBSM0lilUcqjqyNvlfkpUqvFBoECxpxpqotO9
+      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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# View single image\n",
+    "plt.imshow(image.permute(1, 2, 0)) # adjust for matplotlib\n",
+    "plt.title(class_names[label])\n",
+    "plt.axis(False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7ebc6e5-c601-49fa-a185-05a50fdc81cc",
+   "metadata": {},
+   "source": [
+    "We want to turn this image into patches of itself inline with Figure 1 of the ViT paper.\n",
+    "\n",
+    "How about we start by just visualizing the top row of patched pixels?\n",
+    "\n",
+    "We can do this by indexing on the different image dimensions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "bcd2e784-7989-40e5-b8f5-64de18f1fe3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x1152 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Change image shape to be compatible with matplotlib (color_channels, height, width) -> (height, width, color_channels) \n",
+    "image_permuted = image.permute(1, 2, 0)\n",
+    "\n",
+    "# Index to plot the top row of patched pixels\n",
+    "patch_size = 16\n",
+    "plt.figure(figsize=(patch_size, patch_size))\n",
+    "plt.imshow(image_permuted[:patch_size, :, :]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad0f2977-7c7b-45e5-91a9-a8626e8e73c7",
+   "metadata": {},
+   "source": [
+    "Now we've got the top row, let's turn it into patches.\n",
+    "\n",
+    "We can do this by iterating through the number of patches there'd be in the top row. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "93210158-3dcb-4d1f-b728-c7c9c3df99dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of patches per row: 14.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x1008 with 14 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Setup hyperparameters and make sure img_size and patch_size are compatible\n",
+    "img_size = 224\n",
+    "patch_size = 16\n",
+    "num_patches = img_size/patch_size \n",
+    "assert img_size % patch_size == 0, \"Image size must be divisible by patch size\" \n",
+    "print(f\"Number of patches per row: {num_patches}\")\n",
+    "\n",
+    "# Create a series of subplots\n",
+    "fig, axs = plt.subplots(nrows=1, \n",
+    "                        ncols=img_size // patch_size, # one column for each patch\n",
+    "                        figsize=(num_patches, num_patches),\n",
+    "                        sharex=True,\n",
+    "                        sharey=True)\n",
+    "\n",
+    "# Iterate through number of patches in the top row\n",
+    "for i, patch in enumerate(range(0, img_size, patch_size)):\n",
+    "    axs[i].imshow(image_permuted[:patch_size, patch:patch+patch_size, :]); # keep height index constant, alter the width index\n",
+    "    axs[i].set_xlabel(i+1) # set the label\n",
+    "    axs[i].set_xticks([])\n",
+    "    axs[i].set_yticks([])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc30f0a2-7344-4a90-b5b7-7a98127c59fd",
+   "metadata": {},
+   "source": [
+    "Those are some nice looking patches!\n",
+    "\n",
+    "How about we do it for the whole image?\n",
+    "\n",
+    "This time we'll iterate through the indexs for height and width and plot each patch as it's own subplot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "7d45e15a-eb50-4c46-8055-2acaaacb881c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of patches per row: 14.0\n",
+      "Number of patches per column: 14.0\n",
+      "Total patches: 196.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x1008 with 196 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Setup hyperparameters and make sure img_size and patch_size are compatible\n",
+    "img_size = 224\n",
+    "patch_size = 16\n",
+    "num_patches = img_size/patch_size \n",
+    "assert img_size % patch_size == 0, \"Image size must be divisible by patch size\" \n",
+    "print(f\"Number of patches per row: {num_patches}\\nNumber of patches per column: {num_patches}\\nTotal patches: {num_patches*num_patches}\")\n",
+    "\n",
+    "# Create a series of subplots\n",
+    "fig, axs = plt.subplots(nrows=img_size // patch_size, # need int not float\n",
+    "                        ncols=img_size // patch_size, \n",
+    "                        figsize=(num_patches, num_patches),\n",
+    "                        sharex=True,\n",
+    "                        sharey=True)\n",
+    "\n",
+    "# Loop through height and width of image\n",
+    "for i, patch_height in enumerate(range(0, img_size, patch_size)): # iterate through height\n",
+    "    for j, patch_width in enumerate(range(0, img_size, patch_size)): # iterate through width\n",
+    "        \n",
+    "        # Plot the permuted image patch (image_permuted -> (Height, Width, Color Channels))\n",
+    "        axs[i, j].imshow(image_permuted[patch_height:patch_height+patch_size, # iterate through height \n",
+    "                                        patch_width:patch_width+patch_size, # iterate through width\n",
+    "                                        :]) # get all color channels\n",
+    "        \n",
+    "        # Set up label information, remove the ticks for clarity and set labels to outside\n",
+    "        axs[i, j].set_ylabel(i+1, \n",
+    "                             rotation=\"horizontal\", \n",
+    "                             horizontalalignment=\"right\", \n",
+    "                             verticalalignment=\"center\") \n",
+    "        axs[i, j].set_xlabel(j+1) \n",
+    "        axs[i, j].set_xticks([])\n",
+    "        axs[i, j].set_yticks([])\n",
+    "        axs[i, j].label_outer()\n",
+    "\n",
+    "# Set a super title\n",
+    "fig.suptitle(f\"{class_names[label]} -> Patchified\", fontsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32a983e8-6d76-4ef0-b0f2-c97a42bdbb08",
+   "metadata": {},
+   "source": [
+    "Image patchified!\n",
+    "\n",
+    "Woah, that looks cool.\n",
+    "\n",
+    "Now how do we turn each of these patches into an embedding and convert them into a sequence?\n",
+    "\n",
+    "Hint: we can use PyTorch layers. Can you guess which?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f774b58d-7095-4272-aba3-fd9a2db4f28f",
+   "metadata": {},
+   "source": [
+    "### TK - 4.3 Creating image patches with `torch.nn.Conv2d()`\n",
+    "\n",
+    "It's time to start moving towards replicating the patch embedding layers with PyTorch.\n",
+    "\n",
+    "To visualize our single image we wrote code to loop through the different height and width dimensions of a single image and plot individual patches.\n",
+    "\n",
+    "This operation is very similar to the convolutional operation we saw in [03. PyTorch Computer Vision section 7.1: Stepping through `nn.Conv2d()`](https://www.learnpytorch.io/03_pytorch_computer_vision/#71-stepping-through-nnconv2d).\n",
+    "\n",
+    "In fact, the authors of the ViT paper mention in section 3.1 that the patch embedding is achievable with a convolutional neural network (CNN):  \n",
+    "\n",
+    "> **Hybrid Architecture.** As an alternative to raw image patches, the input sequence can be formed from feature maps of a CNN (LeCun et al., 1989). In this hybrid model, the patch embedding projection $\\mathbf{E}$ (Eq. 1) is applied to patches extracted from a **CNN feature map**. As a special case, the patches can have spatial size $1 \\times 1$, which means that the **input sequence is obtained by simply flattening the spatial dimensions of the feature map and projecting to the Transformer dimension**. The classification input embedding and position embeddings are added as described above.\n",
+    "\n",
+    "The \"**feature map**\" they're refering to are the weights/activations produced by a convolutional layer passing over a given image.\n",
+    "\n",
+    "<img src=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/images/08-vit-paper-patch-embedding-animation.gif\" width=900 alt=\"example of creating a patch embedding by passing a convolutional layer over a single image\"/>\n",
+    "\n",
+    "*By setting the `kernel_size` and `stride` parameters of a [`torch.nn.Conv2d()`](https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html) layer equal to the `patch_size`, we can effectively get a layer that splits our image into patches and creates a learnable embedding (referred to as a \"Linear Projection\" in the ViT paper) of each patch.* \n",
+    "\n",
+    "Remember our ideal input and output shapes for the patch embedding layer?\n",
+    "\n",
+    "* **Input:** The image starts as 2D with size ${H \\times W \\times C}$.\n",
+    "* **Output:** The image gets converted to a sequence of flattened 2D patches with size ${N \\times\\left(P^{2} \\cdot C\\right)}$.\n",
+    "\n",
+    "Or for an image size of 224 and patch size of 16:\n",
+    "\n",
+    "* **Input (2D image):** (224, 224, 3) \n",
+    "* **Output (flattened 2D patches):** (196, 768)\n",
+    "\n",
+    "We can recreate these with:\n",
+    "* [`torch.nn.Conv2d()`](https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html) for turning our image into patches of CNN feature maps.\n",
+    "* [`torch.nn.Flatten()`](https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html) for flattening the spatial dimensions of the feature map.\n",
+    "\n",
+    "Let's start with the `torch.nn.Conv2d()` layer.\n",
+    "\n",
+    "We can replicate the creation of patches by setting the `kernel_size` and `stride` equal to `patch_size`.\n",
+    "\n",
+    "This means each convolutional kernel will be of size `(patch_size x patch_size)` or if `patch_size=16`, `(16 x 16)` (the equivalent of one whole patch) \n",
+    "\n",
+    "And each step or `stride` of the convolutional kernel will be `patch_size` pixels long or `16` pixels long (equivalent of stepping to the next patch).\n",
+    "\n",
+    "We'll set `in_channels=3` for the number of color channels in our image and we'll set `out_channels=768`, the same as the $D$ value in Table 1 for ViT-Base (this is the embedding dimension, each image will be embedded into a vector of size 768)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "3d4fd046-6b51-4ac0-8d39-e67fb333a18a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch import nn\n",
+    "\n",
+    "# Set the patch size\n",
+    "patch_size=16\n",
+    "\n",
+    "# Create the Conv2d layer with hyperparameters from the ViT paper\n",
+    "conv2d = nn.Conv2d(in_channels=3, # number of color channels\n",
+    "                   out_channels=768, # from Table 1: Hidden size D, this is the embedding size\n",
+    "                   kernel_size=patch_size, # could also use (patch_size, patch_size)\n",
+    "                   stride=patch_size,\n",
+    "                   padding=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03dec513-eea5-4d13-b7ab-41c9e997ef48",
+   "metadata": {},
+   "source": [
+    "Now we've got a convoluational layer, let's see what happens when we pass a single image through it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "1d3424d2-2cfa-431c-9fd0-e2afdf15fc9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# View single image\n",
+    "plt.imshow(image.permute(1, 2, 0)) # adjust for matplotlib\n",
+    "plt.title(class_names[label])\n",
+    "plt.axis(False);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a72a5614-f1cb-4c01-9696-24f07bf2a219",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 768, 14, 14])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Pass the image through the convolutional layer \n",
+    "image_out_of_conv = conv2d(image.unsqueeze(0)) # add a single batch dimension (height, width, color_channels) -> (batch, height, width, color_channels)\n",
+    "print(image_out_of_conv.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87afeb8f-86cf-4cad-bf88-84dfaccdbe2b",
+   "metadata": {},
+   "source": [
+    "Passing our image through the convolutional layer turns it into a series of 768 (this is the embedding size or $D$) feature/activation maps.\n",
+    "\n",
+    "So its output shape can be read as:\n",
+    "    \n",
+    "```python\n",
+    "torch.Size([1, 768, 14, 14]) -> [batch_size, embedding_dim, feature_map_height, feature_map_width]\n",
+    "```\n",
+    "\n",
+    "Let's visualize five random feature maps and see what they look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "af5b58ca-0d73-4c62-b4af-4e8867b764e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Showing random convolutional feature maps from indexes: [180, 39, 286, 72, 105]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot random 5 convolutional feature maps\n",
+    "import random\n",
+    "random_indexes = random.sample(range(0, 768), k=5) # pick 5 numbers between 0 and the embedding size\n",
+    "print(f\"Showing random convolutional feature maps from indexes: {random_indexes}\")\n",
+    "\n",
+    "# Create plot\n",
+    "fig, axs = plt.subplots(nrows=1, ncols=5, figsize=(12, 12))\n",
+    "\n",
+    "# Plot random image feature maps\n",
+    "for i, idx in enumerate(random_indexes):\n",
+    "    image_conv_feature_map = image_out_of_conv[:, idx, :, :] # index on the output tensor of the convolutional layer\n",
+    "    axs[i].imshow(image_conv_feature_map.squeeze().detach().numpy())\n",
+    "    axs[i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b847f2e1-1700-4040-a9aa-df9ab1139cce",
+   "metadata": {},
+   "source": [
+    "Notice how the feature maps all kind of represent the original image, visualizing a few you can see the different major outlines and some major features.\n",
+    "\n",
+    "The important thing to note is that these features may change over time as the neural network learns.\n",
+    "\n",
+    "And because of these, these feature maps can be considered a **learnable embedding** of our image.\n",
+    "\n",
+    "Let's check one out in numerical form."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "94f6f5b9-a1c7-4aa1-9780-7cd06457b2b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([[[0.2642, 1.1367, 1.0221, 0.9712, 1.0950, 1.2478, 1.2884, 1.1481,\n",
+       "           0.9640, 0.6204, 0.5996, 0.5855, 0.5560, 0.4992],\n",
+       "          [1.1578, 1.0633, 0.9593, 1.1931, 1.3194, 1.1306, 0.7317, 0.4322,\n",
+       "           0.6025, 0.7246, 0.7891, 0.5383, 0.4786, 0.7098],\n",
+       "          [1.0163, 0.9368, 1.1675, 1.3373, 1.0429, 0.7497, 0.7445, 0.4270,\n",
+       "           0.7430, 0.8750, 0.6105, 0.5147, 1.0207, 0.7852],\n",
+       "          [1.0669, 1.0157, 1.3291, 1.0117, 0.4848, 0.6802, 0.8365, 0.7736,\n",
+       "           0.8618, 0.9144, 0.8926, 0.9795, 0.7475, 0.7585],\n",
+       "          [0.9371, 1.1937, 1.0068, 0.6377, 0.7283, 0.9625, 1.0372, 0.8920,\n",
+       "           0.9372, 0.9034, 0.9683, 0.9405, 0.5958, 0.8740],\n",
+       "          [0.9419, 0.8599, 0.5429, 0.6954, 1.0202, 0.9093, 1.0003, 0.7619,\n",
+       "           0.8472, 0.8062, 0.6418, 0.7741, 0.5791, 0.9816],\n",
+       "          [0.7965, 0.7202, 0.6424, 0.9137, 0.8264, 1.0243, 1.0920, 0.9548,\n",
+       "           0.9166, 0.7937, 0.4675, 0.5346, 0.7774, 1.1001],\n",
+       "          [0.3298, 0.4832, 0.5324, 0.7486, 0.8303, 0.8101, 0.9969, 0.9931,\n",
+       "           1.0058, 0.6002, 0.6643, 0.7254, 0.8453, 1.1323],\n",
+       "          [0.5384, 0.4798, 0.6725, 0.8014, 0.7044, 0.7988, 0.8185, 0.8911,\n",
+       "           0.9720, 0.8939, 0.6234, 0.5674, 0.5775, 1.0011],\n",
+       "          [0.6199, 0.6465, 0.6503, 0.6215, 0.8154, 0.7950, 0.8647, 0.9872,\n",
+       "           0.8513, 0.8833, 0.5799, 0.5914, 0.6936, 1.0554],\n",
+       "          [0.5140, 0.6462, 0.6982, 0.7445, 0.7394, 0.8124, 0.7462, 0.9183,\n",
+       "           0.7471, 0.9436, 0.7147, 0.6396, 0.5795, 1.0201],\n",
+       "          [0.5467, 0.7408, 0.6854, 0.6624, 0.7465, 0.5077, 0.7633, 0.8709,\n",
+       "           1.0026, 0.7276, 0.7847, 0.5811, 0.5521, 1.0318],\n",
+       "          [0.8041, 0.8868, 0.5559, 0.5889, 0.7236, 0.6976, 0.7940, 0.9365,\n",
+       "           0.9110, 0.8182, 0.7013, 0.4890, 0.8364, 1.0031],\n",
+       "          [0.1976, 1.0262, 1.1979, 0.9982, 0.9644, 0.8868, 0.9556, 1.0204,\n",
+       "           1.0060, 0.9586, 0.9351, 0.8819, 0.9290, 0.9289]]],\n",
+       "        grad_fn=<SliceBackward0>),\n",
+       " True)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get a single feature map in tensor form\n",
+    "single_feature_map = image_out_of_conv[:, 0, :, :]\n",
+    "single_feature_map, single_feature_map.requires_grad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc7c08ca-a4ef-4350-b471-088b2f12b80e",
+   "metadata": {},
+   "source": [
+    "The `grad_fn` output of the `single_feature_map` and the `required_grad=True` attribute means PyTorch is tracking the gradients of this feature map and it will be updated by gradient descent during training. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "572ae1c5-9488-4882-bdc1-409eef95424e",
+   "metadata": {},
+   "source": [
+    "### TK - 4.4 Flattening the patch embedding with `torch.nn.Flatten()`\n",
+    "\n",
+    "We've turned our image into patch embeddings but they're still in 2D format.\n",
+    "\n",
+    "How do we get them into the desired output shape of the patch embedding layer of the ViT model?\n",
+    "\n",
+    "* **Desried output (flattened 2D patches):** (196, 768) -> ${N \\times\\left(P^{2} \\cdot C\\right)}$\n",
+    "\n",
+    "Let's check the current shape."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "c8219029-6162-4046-8702-0c2cb42f2378",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current tensor shape: torch.Size([1, 768, 14, 14]) -> [batch, embedding_dim, feature_map_height, feature_map_width]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Current tensor shape\n",
+    "print(f\"Current tensor shape: {image_out_of_conv.shape} -> [batch, embedding_dim, feature_map_height, feature_map_width]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0160c70b-0fe8-42f9-b6e9-5cac23e06836",
+   "metadata": {},
+   "source": [
+    "Well we've got the 768 part ( $(P^{2} \\cdot C)$ ) but we still need the number of patches ($N$).\n",
+    "\n",
+    "Reading back through section 3.1 of the ViT paper it says (bold mine):\n",
+    "\n",
+    "> As a special case, the patches can have spatial size $1 \\times 1$, which means that the **input sequence is obtained by simply flattening the spatial dimensions of the feature map and projecting to the Transformer dimension**.\n",
+    "\n",
+    "Flattening the spatial dimensions of the feature map hey?\n",
+    "\n",
+    "What layer do we have in PyTorch that can flatten?\n",
+    "\n",
+    "How about [`torch.nn.Flatten()`](https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html )?\n",
+    "\n",
+    "But we don't want to flatten the whole tensor, we only want to flatten the \"spatial dimensions of the feature map\".\n",
+    "\n",
+    "Which in our case is the `feature_map_height` and `feature_map_width` dimensions of `image_out_of_conv`.\n",
+    "\n",
+    "So how about we create a `torch.nn.Flatten()` layer to only flatten those dimensions, we can use the `start_dim` and `end_dim` parameters to set that up?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "ed82899d-7bbc-49f9-a423-8fa6344b8e99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create flatten layer\n",
+    "flatten = nn.Flatten(start_dim=2, # flatten feature_map_height (dimension 2)\n",
+    "                     end_dim=3) # flatten feature_map_width (dimension 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adcf7cfc-2635-4081-9ade-3542f77c47e2",
+   "metadata": {},
+   "source": [
+    "Nice! Now let's put it all together!\n",
+    "\n",
+    "We'll:\n",
+    "1. Take a single image.\n",
+    "2. Put in through the convolutional layer (`conv2d`) to turn the image into 2D feature maps (patch embeddings).\n",
+    "3. Flatten the 2D feature map into a single sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e3fa363b-1923-4e27-a0b5-980d885fcda2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original image shape: torch.Size([3, 224, 224])\n",
+      "Image feature map shape: torch.Size([1, 768, 14, 14])\n",
+      "Flattened image feature map shape: torch.Size([1, 768, 196])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1. View single image\n",
+    "plt.imshow(image.permute(1, 2, 0)) # adjust for matplotlib\n",
+    "plt.title(class_names[label])\n",
+    "plt.axis(False);\n",
+    "print(f\"Original image shape: {image.shape}\")\n",
+    "\n",
+    "# 2. Turn image into feature maps\n",
+    "image_out_of_conv = conv2d(image.unsqueeze(0)) # add batch dimension to avoid shape errors\n",
+    "print(f\"Image feature map shape: {image_out_of_conv.shape}\")\n",
+    "\n",
+    "# 3. Flatten the feature maps\n",
+    "image_out_of_conv_flattened = flatten(image_out_of_conv)\n",
+    "print(f\"Flattened image feature map shape: {image_out_of_conv_flattened.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe802095-e944-4607-b3a7-891ba452372b",
+   "metadata": {},
+   "source": [
+    "Woohoo! It looks like our `image_out_of_conv_flattened` shape is very close to our desired output shape: \n",
+    "    \n",
+    "* **Desried output (flattened 2D patches):** (196, 768) -> ${N \\times\\left(P^{2} \\cdot C\\right)}$\n",
+    "* **Current shape:** (1, 768, 196)\n",
+    "\n",
+    "The only difference is our current shape has a batch size and the dimensions are in a different order to the desired output.\n",
+    "\n",
+    "How could we fix this?\n",
+    "\n",
+    "Well, how about we rearrange the dimensions?\n",
+    "\n",
+    "We can do so with `torch.Tensor.permute()` just like we do when rearranging image tensors to plot them with matplotlib.\n",
+    "\n",
+    "Let's try."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "47f571a1-2303-4981-85f5-33936b39cf14",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Patch embedding sequence shape: torch.Size([1, 196, 768]) -> [batch_size, num_patches, embedding_size]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get flattened image patch embeddings in right shape \n",
+    "image_out_of_conv_flattened_reshaped = image_out_of_conv_flattened.permute(0, 2, 1) # [batch_size, P^2•C, N] -> [batch_size, N, P^2•C]\n",
+    "print(f\"Patch embedding sequence shape: {image_out_of_conv_flattened_reshaped.shape} -> [batch_size, num_patches, embedding_size]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "224d751a-11a0-4645-a225-cd36e507ebf8",
+   "metadata": {},
+   "source": [
+    "Yes!!!\n",
+    "\n",
+    "We've now matched the desired input and output shapes for the patch embedding layer of the ViT architecture using a couple of PyTorch layers.\n",
+    "\n",
+    "How about we visualize one of the flattened feature maps?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "e4204163-8689-4b9e-8828-4e1e20d9316e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1584x1584 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get a single flattened feature map\n",
+    "single_flattened_feature_map = image_out_of_conv_flattened_reshaped[:, :, 0]\n",
+    "\n",
+    "# Plot the flattened feature map visually\n",
+    "plt.figure(figsize=(22, 22))\n",
+    "plt.imshow(single_flattened_feature_map.detach().numpy())\n",
+    "plt.title(f\"Flattened feature map shape: {single_flattened_feature_map.shape}\")\n",
+    "plt.axis(False);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fe6c0a6-687c-473c-a30f-6b01b1df533f",
+   "metadata": {},
+   "source": [
+    "Hmm, the flattened feature map doesn't look like much visually, but that's not what we're concerned about, this is what will be the output of the patching embedding layer and the input to the rest of the ViT architecture.\n",
+    "\n",
+    "TK image - single image -> conv2d -> flatten -> get the output above (show the workflow and transformation, this could be the gif we've but using but extended to work with the flatten section)\n",
+    "\n",
+    "> **Note:** The [original Transformer architecture](https://arxiv.org/abs/1706.03762) was designed to work with text. The Vision Transformer architecture (ViT) had the goal of using the original Transformer for images. This is why the input to the ViT architecture is processed in the way it is. We're essentially taking a 2D image and formatting it so it appears as a 1D sequence of text. \n",
+    "\n",
+    "How about we view the flattened feature map in tensor form?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "0cdeb08e-948d-4810-ae8d-c607b3b9feec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([[0.2642, 1.1367, 1.0221, 0.9712, 1.0950, 1.2478, 1.2884, 1.1481, 0.9640,\n",
+       "          0.6204, 0.5996, 0.5855, 0.5560, 0.4992, 1.1578, 1.0633, 0.9593, 1.1931,\n",
+       "          1.3194, 1.1306, 0.7317, 0.4322, 0.6025, 0.7246, 0.7891, 0.5383, 0.4786,\n",
+       "          0.7098, 1.0163, 0.9368, 1.1675, 1.3373, 1.0429, 0.7497, 0.7445, 0.4270,\n",
+       "          0.7430, 0.8750, 0.6105, 0.5147, 1.0207, 0.7852, 1.0669, 1.0157, 1.3291,\n",
+       "          1.0117, 0.4848, 0.6802, 0.8365, 0.7736, 0.8618, 0.9144, 0.8926, 0.9795,\n",
+       "          0.7475, 0.7585, 0.9371, 1.1937, 1.0068, 0.6377, 0.7283, 0.9625, 1.0372,\n",
+       "          0.8920, 0.9372, 0.9034, 0.9683, 0.9405, 0.5958, 0.8740, 0.9419, 0.8599,\n",
+       "          0.5429, 0.6954, 1.0202, 0.9093, 1.0003, 0.7619, 0.8472, 0.8062, 0.6418,\n",
+       "          0.7741, 0.5791, 0.9816, 0.7965, 0.7202, 0.6424, 0.9137, 0.8264, 1.0243,\n",
+       "          1.0920, 0.9548, 0.9166, 0.7937, 0.4675, 0.5346, 0.7774, 1.1001, 0.3298,\n",
+       "          0.4832, 0.5324, 0.7486, 0.8303, 0.8101, 0.9969, 0.9931, 1.0058, 0.6002,\n",
+       "          0.6643, 0.7254, 0.8453, 1.1323, 0.5384, 0.4798, 0.6725, 0.8014, 0.7044,\n",
+       "          0.7988, 0.8185, 0.8911, 0.9720, 0.8939, 0.6234, 0.5674, 0.5775, 1.0011,\n",
+       "          0.6199, 0.6465, 0.6503, 0.6215, 0.8154, 0.7950, 0.8647, 0.9872, 0.8513,\n",
+       "          0.8833, 0.5799, 0.5914, 0.6936, 1.0554, 0.5140, 0.6462, 0.6982, 0.7445,\n",
+       "          0.7394, 0.8124, 0.7462, 0.9183, 0.7471, 0.9436, 0.7147, 0.6396, 0.5795,\n",
+       "          1.0201, 0.5467, 0.7408, 0.6854, 0.6624, 0.7465, 0.5077, 0.7633, 0.8709,\n",
+       "          1.0026, 0.7276, 0.7847, 0.5811, 0.5521, 1.0318, 0.8041, 0.8868, 0.5559,\n",
+       "          0.5889, 0.7236, 0.6976, 0.7940, 0.9365, 0.9110, 0.8182, 0.7013, 0.4890,\n",
+       "          0.8364, 1.0031, 0.1976, 1.0262, 1.1979, 0.9982, 0.9644, 0.8868, 0.9556,\n",
+       "          1.0204, 1.0060, 0.9586, 0.9351, 0.8819, 0.9290, 0.9289]],\n",
+       "        grad_fn=<SelectBackward0>),\n",
+       " True,\n",
+       " torch.Size([1, 196]))"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# See the flattened feature map as a tensor\n",
+    "single_flattened_feature_map, single_flattened_feature_map.requires_grad, single_flattened_feature_map.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6cc9dae5-5bf2-45b4-8266-cf733868441d",
+   "metadata": {},
+   "source": [
+    "Beautiful!\n",
+    "\n",
+    "We've turned our single 2D image into a single 1D learnable embedding vector (or \"Linear Projection of Flattned Patches\" in Figure 1 of the ViT paper)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b165987a-8370-471a-a663-711e0c6e60db",
+   "metadata": {},
+   "source": [
+    "### TK - 4.5 Turning the ViT patch embedding layer into a PyTorch module\n",
+    "\n",
+    "Time to put everything we've done for creating the patch embedding into a single PyTorch layer.\n",
+    "\n",
+    "We can do so by subclassing `nn.Module` and creating a small PyTorch \"model\" to do all of the steps above.\n",
+    "\n",
+    "Specifically we'll:\n",
+    "1. Create a class called `PatchEmbedding` which subclasses `nn.Module` (so it can be used a PyTorch layer).\n",
+    "2. Initialize the class with the parameters `in_channels=3`, `patch_size=16` (for ViT-Base) and `embedding_dim=768` (this is $D$ for ViT-Base from Table 1).\n",
+    "3. Create a layer to turn an image into patches using `nn.Conv2d()` (just like in 4.3 above).\n",
+    "4. Create a layer to flatten the patch feature maps into a single dimension (just like in 4.4 above). \n",
+    "5. Define a `forward()` method to take an input and pass it through the layers created in 3 and 4.\n",
+    "6. Make sure the output shape reflects the required output shape of the ViT architecture (${N \\times\\left(P^{2} \\cdot C\\right)}$).\n",
+    "\n",
+    "Let's do it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "3ef75c7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1. Create a class which subclasses nn.Module\n",
+    "class PatchEmbedding(nn.Module):\n",
+    "    \"\"\"Turns a 2D input image into a 1D sequence learnable embedding vector.\n",
+    "    \n",
+    "    Args:\n",
+    "        in_channels (int): Number of color channels for the input images. Defaults to 3.\n",
+    "        patch_size (int): Size of patches to convert input image into. Defaults to 16.\n",
+    "        embedding_dim (int): Size of embedding to turn image into. Defaults to 768.\n",
+    "    \"\"\" \n",
+    "    # 2. Initialize the class with appropriate variables\n",
+    "    def __init__(self, \n",
+    "                 in_channels:int=3,\n",
+    "                 patch_size:int=16,\n",
+    "                 embedding_dim:int=768):\n",
+    "        super().__init__()\n",
+    "        \n",
+    "        # 3. Create a layer to turn an image into patches\n",
+    "        self.patcher = nn.Conv2d(in_channels=in_channels,\n",
+    "                                 out_channels=embedding_dim,\n",
+    "                                 kernel_size=patch_size,\n",
+    "                                 stride=patch_size,\n",
+    "                                 padding=0)\n",
+    "\n",
+    "        # 4. Create a layer to flatten the patch feature maps into a single dimension\n",
+    "        self.flatten = nn.Flatten(start_dim=2, # only flatten the feature map dimensions into a single vector\n",
+    "                                  end_dim=3)\n",
+    "\n",
+    "    # 5. Define the forward method \n",
+    "    def forward(self, x):\n",
+    "        # Create assertion to check that inputs are the correct shape\n",
+    "        image_resolution = x.shape[-1]\n",
+    "        assert image_resolution % patch_size == 0, f\"Input image size must be divisble by patch size, image shape: {image_resolution}, patch size: {patch_size}\"\n",
+    "        \n",
+    "        # Perform the forward pass\n",
+    "        x_patched = self.patcher(x)\n",
+    "        x_flattened = self.flatten(x_patched) \n",
+    "        # 6. Make sure the output shape has the right order \n",
+    "        return x_flattened.permute(0, 2, 1) # adjust so the embedding is on the final dimension [batch_size, P^2•C, N] -> [batch_size, N, P^2•C]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5270aa24-85b7-4b5a-a799-8e5eeca47f8f",
+   "metadata": {},
+   "source": [
+    "`PatchEmbedding` layer created!\n",
+    "\n",
+    "Let's try it out on a single image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a5599575-44cc-46c9-95a4-e65eb1379a59",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input image shape: torch.Size([1, 3, 224, 224])\n",
+      "Output patch embedding shape: torch.Size([1, 196, 768])\n"
+     ]
+    }
+   ],
+   "source": [
+    "set_seeds()\n",
+    "\n",
+    "# Create an instance of patch embedding layer\n",
+    "patchify = PatchEmbedding(in_channels=3,\n",
+    "                          patch_size=16,\n",
+    "                          embedding_dim=768)\n",
+    "\n",
+    "# Pass a single image through\n",
+    "print(f\"Input image shape: {image.unsqueeze(0).shape}\")\n",
+    "patch_embedded_image = patchify(image.unsqueeze(0)) # add an extra batch dimension on the 0th index, otherwise will error\n",
+    "print(f\"Output patch embedding shape: {patch_embedded_image.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4d59a81-0cef-4251-832b-5f69da199996",
+   "metadata": {},
+   "source": [
+    "Beautiful!\n",
+    "\n",
+    "The output shape matches the ideal input and output shapes we'd like to see from the patch embedding layer:\n",
+    "\n",
+    "* **Input:** The image starts as 2D with size ${H \\times W \\times C}$.\n",
+    "* **Output:** The image gets converted to a sequence of flattened 2D patches with size ${N \\times\\left(P^{2} \\cdot C\\right)}$.\n",
+    "\n",
+    "Where:\n",
+    "* $(H, W)$ is the resolution of the original image.\n",
+    "* $C$ is the number of channels.\n",
+    "* $(P, P)$ is the resolution of each image patch (**patch size**).\n",
+    "* $N=H W / P^{2}$ is the resulting number of patches, which also serves as the effective input sequence length for the Transformer.\n",
+    "    \n",
+    "We've now replicated the patch embedding for equation 1 but not the class token/position embedding.\n",
+    "\n",
+    "We'll get to these later on.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-replicating-the-patch-embedding-layer.png\" width=900 alt=\"replicating the vision transformer architecture patch embedding layer\"/>\n",
+    "\n",
+    "*Our `PatchEmbedding` class (right) replicates the patch embedding of the ViT architecture from Figure 1 and Equation 1 from the ViT paper (left). However, the learnable class embedding and position embeddings haven't been created yet. These will come soon.*\n",
+    "\n",
+    "Let's now get a summary of our `PatchEmbedding` layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "e440be53-d72c-42b8-87c8-1c31c4262c16",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "========================================================================================================================\n",
+       "Layer (type (var_name))                  Input Shape          Output Shape         Param #              Trainable\n",
+       "========================================================================================================================\n",
+       "PatchEmbedding (PatchEmbedding)          [1, 3, 224, 224]     [1, 196, 768]        --                   True\n",
+       "├─Conv2d (patcher)                       [1, 3, 224, 224]     [1, 768, 14, 14]     590,592              True\n",
+       "├─Flatten (flatten)                      [1, 768, 14, 14]     [1, 768, 196]        --                   --\n",
+       "========================================================================================================================\n",
+       "Total params: 590,592\n",
+       "Trainable params: 590,592\n",
+       "Non-trainable params: 0\n",
+       "Total mult-adds (M): 115.76\n",
+       "========================================================================================================================\n",
+       "Input size (MB): 0.60\n",
+       "Forward/backward pass size (MB): 1.20\n",
+       "Params size (MB): 2.36\n",
+       "Estimated Total Size (MB): 4.17\n",
+       "========================================================================================================================"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create random input sizes\n",
+    "random_input_image = (1, 3, 224, 224)\n",
+    "random_input_image_error = (1, 3, 250, 250) # will error because image size is incompatible with patch_size\n",
+    "\n",
+    "# Get a summary of the input and outputs of PatchEmbedding\n",
+    "summary(PatchEmbedding(), \n",
+    "        input_size=random_input_image, # try swapping this for \"random_input_image_error\" \n",
+    "        col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
+    "        col_width=20,\n",
+    "        row_settings=[\"var_names\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8576f2f1-a2ad-4874-8f76-08156371f444",
+   "metadata": {},
+   "source": [
+    "### TK 4.6 Creating the class token embedding\n",
+    "\n",
+    "Okay we've made the image patch embedding, time to get to work on the class token embedding.\n",
+    "\n",
+    "Or $\\mathbf{x}_\\text {class }$ from equation 1.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-the-class-token.png\" width=900 alt=\"class token embedding highlight from the vision transformer paper figure 1 and section 3.1\"/>\n",
+    "\n",
+    "*Left: Figure 1 from the ViT paper with the \"classification token\" or `[class]` embedding token we're going to recreate highlighted. Right: Equation 1 and section 3.1 of the ViT paper that relate to the learnable class embedding token.*\n",
+    "\n",
+    "Reading the second paragraph of section 3.1 from the ViT paper, we see the following description: \n",
+    "\n",
+    "> Similar to BERT's `[ class ]` token, we prepend a learnable embedding to the sequence of embedded patches $\\left(\\mathbf{z}_{0}^{0}=\\mathbf{x}_{\\text {class }}\\right)$, whose state at the output of the Transformer encoder $\\left(\\mathbf{z}_{L}^{0}\\right)$ serves as the image representation $\\mathbf{y}$ (Eq. 4). \n",
+    "\n",
+    "> **Note:** [BERT](https://arxiv.org/abs/1810.04805) (Bidirectional Encoder Representations from Transformers) is one of the original machine learning research papers to use the Transformer architecture to achieve outstanding results on natural language processing (NLP) tasks and is where the idea of having a `[ class ]` token at the start of a sequence originated, class being a description for the \"classification\" class the sequence belonged to.\n",
+    "\n",
+    "So we need to \"preprend a learnable embedding to the sequence of embedded patches\".\n",
+    "\n",
+    "Let's start by viewing our sequence of embedded patches tensor (created in 4.5) and its shape."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "50381789-d73d-4648-9144-4d48da87318f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[[-0.4923,  0.0265, -0.0909,  ...,  0.1478, -0.0986,  0.2243],\n",
+      "         [-0.9849,  0.3805, -0.3638,  ...,  0.6115, -0.0805,  0.2097],\n",
+      "         [-0.6015,  0.1235, -0.2506,  ...,  0.6307, -0.4673,  0.2756],\n",
+      "         ...,\n",
+      "         [-0.6668,  0.1713, -0.1711,  ...,  0.4699, -0.2881,  0.2599],\n",
+      "         [-0.6983,  0.1949, -0.1884,  ...,  0.5152, -0.3126,  0.2151],\n",
+      "         [-0.6889,  0.1862, -0.1444,  ...,  0.5019, -0.3564,  0.2378]]],\n",
+      "       grad_fn=<PermuteBackward0>)\n",
+      "Patch embedding shape: torch.Size([1, 196, 768]) -> [batch_size, number_of_patches, embedding_dimension]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# View the patch embedding and patch embedding shape\n",
+    "print(patch_embedded_image) \n",
+    "print(f\"Patch embedding shape: {patch_embedded_image.shape} -> [batch_size, number_of_patches, embedding_dimension]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5e417fc-70c9-43d4-a294-e7728a24bf42",
+   "metadata": {},
+   "source": [
+    "To \"prepend a learnable embedding to the sequence of embedded patches\" we need to create a learnable embedding in the shape of the `embedding_dimension` ($D$) and then add it to the `number_of_patches` dimension. \n",
+    "\n",
+    "Or in pseudocode:\n",
+    "\n",
+    "```python\n",
+    "patch_embedding = [image_patch_1, image_patch_2, image_patch_3...]\n",
+    "class_token = learnable_embedding\n",
+    "patch_embedding_with_class_token = torch.cat((class_token, patch_embedding), dim=1)\n",
+    "```\n",
+    "\n",
+    "Notice the concatenation (`torch.cat()`) happens on `dim=1` (the `number_of_patches` dimension).\n",
+    "\n",
+    "Let's create a learnable embedding for the class token.\n",
+    "\n",
+    "To do so, we'll get the batch size and embedding dimension shape and then we'll create a `torch.ones()` tensor in the shape `[batch_size, 1, embedding_dimension]`.\n",
+    "\n",
+    "And we'll make the tensor learnable by passing it to `nn.Parameter()` with `requires_grad=True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "cc0bb859-e62e-41a8-9a47-339e4272a152",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]], grad_fn=<SliceBackward0>)\n",
+      "Class token shape: torch.Size([1, 1, 768]) -> [batch_size, number_of_tokens, embedding_dimension]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the batch size and embedding dimension\n",
+    "batch_size = patch_embedded_image.shape[0]\n",
+    "embedding_dimension = patch_embedded_image.shape[-1]\n",
+    "\n",
+    "# Create the class token embedding as a learnable parameter that shares the same size as the embedding dimension (D)\n",
+    "class_token = nn.Parameter(torch.ones(batch_size, 1, embedding_dimension), # [batch_size, number_of_patches, embedding_dimension]\n",
+    "                           requires_grad=True) # make sure the embedding is learnable\n",
+    "\n",
+    "# Show the first 10 examples of the class_token\n",
+    "print(class_token[:, :, :10])\n",
+    "\n",
+    "# Print the class_token shape\n",
+    "print(f\"Class token shape: {class_token.shape} -> [batch_size, number_of_tokens, embedding_dimension]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1ce6046-f018-4099-96d1-31dad6fc423b",
+   "metadata": {},
+   "source": [
+    "> **Note:** Here we're only creating the class token embedding as [`torch.ones()`](https://pytorch.org/docs/stable/generated/torch.ones.html) for demonstration purposes, in reality, you'd likely create the class token embedding with [`torch.randn()`](https://pytorch.org/docs/stable/generated/torch.randn.html) (start with a random number). \n",
+    "\n",
+    "See how the `number_of_patches` dimension of `class_token` is `1` since we only want to prepend one class token value to the start of the patch embedding sequence.\n",
+    "\n",
+    "Now we've got the class token embedding, let's prepend it to our sequence of image patches, `patch_embedded_image`.\n",
+    "\n",
+    "We can so using [`torch.cat()`](https://pytorch.org/docs/stable/generated/torch.cat.html) and set `dim=1` (so `class_token`'s `number_of_patches` dimension is preprended to `patch_embedded_image`'s `number_of_patches` dimension)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "a7287b01-76cb-4371-ab07-981f7bbf2be5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[[ 1.0000,  1.0000,  1.0000,  ...,  1.0000,  1.0000,  1.0000],\n",
+      "         [-0.4923,  0.0265, -0.0909,  ...,  0.1478, -0.0986,  0.2243],\n",
+      "         [-0.9849,  0.3805, -0.3638,  ...,  0.6115, -0.0805,  0.2097],\n",
+      "         ...,\n",
+      "         [-0.6668,  0.1713, -0.1711,  ...,  0.4699, -0.2881,  0.2599],\n",
+      "         [-0.6983,  0.1949, -0.1884,  ...,  0.5152, -0.3126,  0.2151],\n",
+      "         [-0.6889,  0.1862, -0.1444,  ...,  0.5019, -0.3564,  0.2378]]],\n",
+      "       grad_fn=<CatBackward0>)\n",
+      "Sequence of patch embeddings with class token prepended shape: torch.Size([1, 197, 768]) -> [batch_size, number_of_patches, embedding_dimension]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add the class token embedding to the front of the patch embedding\n",
+    "patch_embedded_image_with_class_embedding = torch.cat((class_token, patch_embedded_image), \n",
+    "                                                      dim=1) # concat on first dimension\n",
+    "\n",
+    "# Print the sequence of patch embeddings with the prepended class token embedding\n",
+    "print(patch_embedded_image_with_class_embedding)\n",
+    "print(f\"Sequence of patch embeddings with class token prepended shape: {patch_embedded_image_with_class_embedding.shape} -> [batch_size, number_of_patches, embedding_dimension]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79fd9252-c15e-40a6-965a-a872e0e09cab",
+   "metadata": {},
+   "source": [
+    "Nice! Learnable class token prepended!\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-prepending-the-learnable-class-token.png\" width=900 alt=\"going from a sequence of patch embeddings, creating a learnable class token and then prepending it to the patch embeddings\"/>\n",
+    "\n",
+    "*Reviewing what we've done to create the learnable class token, we start with a sequence of image patch embeddings created by `PatchEmbedding()` on single image, we then created a learnable class token with one value for each of the embedding dimensions and then prepended it to the original sequence of patch embeddings. Note: Using `torch.ones()` to create the learnable class token is mostly for demonstration purposes only, in practice, you'd like create it with `torch.randn()`.* "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48502c61-16b0-4659-b95f-0e830ae93077",
+   "metadata": {},
+   "source": [
+    "### TK 4.7 Creating the position embedding \n",
+    "\n",
+    "Well, we've got the class token embedding and the patch embedding, now how might we create the position embedding?\n",
+    "\n",
+    "Or $\\mathbf{E}_{\\text {pos }}$ from equation 1 where $E$ stands for \"embedding\".\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-the-position-embeddings.png\" width=900 alt=\"extracting the position embeddings from the vision transformer architecture and comparing them to other sections of the vision transformer paper\"/>\n",
+    "\n",
+    "*Left: Figure 1 from the ViT paper with the position embedding we're going to recreate highlighted. Right: Equation 1 and section 3.1 of the ViT paper that relate to the position embedding.*\n",
+    "\n",
+    "Let's find out more by reading section 3.1 of the ViT paper (bold mine):\n",
+    "\n",
+    "> Position embeddings are added to the patch embeddings to retain positional information. We use **standard learnable 1D position embeddings**, since we have not observed significant performance gains from using more advanced 2D-aware position embeddings (Appendix D.4). The resulting sequence of embedding vectors serves as input to the encoder.\n",
+    "\n",
+    "To start creating the position embeddings, let's view our current embeddings. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "33e5e5bf-e744-4249-ac9b-08986be5ef81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(tensor([[[ 1.0000,  1.0000,  1.0000,  ...,  1.0000,  1.0000,  1.0000],\n",
+       "          [-0.4923,  0.0265, -0.0909,  ...,  0.1478, -0.0986,  0.2243],\n",
+       "          [-0.9849,  0.3805, -0.3638,  ...,  0.6115, -0.0805,  0.2097],\n",
+       "          ...,\n",
+       "          [-0.6668,  0.1713, -0.1711,  ...,  0.4699, -0.2881,  0.2599],\n",
+       "          [-0.6983,  0.1949, -0.1884,  ...,  0.5152, -0.3126,  0.2151],\n",
+       "          [-0.6889,  0.1862, -0.1444,  ...,  0.5019, -0.3564,  0.2378]]],\n",
+       "        grad_fn=<CatBackward0>),\n",
+       " torch.Size([1, 197, 768]))"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# View the sequence of patch embeddings with the prepended class embedding\n",
+    "patch_embedded_image_with_class_embedding, patch_embedded_image_with_class_embedding.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecd1d068-7cac-46b7-aa43-ea8f01ffed4a",
+   "metadata": {},
+   "source": [
+    "Equation 1 states that the position embeddings should have the shape $(N + 1) \\times D$ where: \n",
+    "* $N=H W / P^{2}$ is the resulting number of patches, which also serves as the effective input sequence length for the Transformer.\n",
+    "* $D$ is the size of the **patch embeddings**, different values for $D$ can be found in Table 1.\n",
+    "\n",
+    "Luckily we've got both of these values already.\n",
+    "\n",
+    "So let's make a learnable 1D embedding with `torch.ones()` to create $\\mathbf{E}_{\\text {pos }}$. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "5bb7f6d1-0824-47eb-a059-6854da5c7433",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+      "         [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]], grad_fn=<SliceBackward0>)\n",
+      "Position embeddding shape: torch.Size([1, 197, 768]) -> [batch_size, number_of_patches, embedding_dimension]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate N (number of patches)\n",
+    "number_of_patches = int((height * width) / patch_size**2)\n",
+    "\n",
+    "# Get embedding dimension\n",
+    "embedding_dimension = patch_embedded_image_with_class_embedding.shape[2]\n",
+    "\n",
+    "# Create the learnable 1D position embedding\n",
+    "position_embedding = nn.Parameter(torch.ones(1,\n",
+    "                                             number_of_patches+1, \n",
+    "                                             embedding_dimension),\n",
+    "                                  requires_grad=True) # make sure it's learnable\n",
+    "\n",
+    "# Show the first 10 sequences and 10 position embedding values and check the shape of the position embedding\n",
+    "print(position_embedding[:, :10, :10])\n",
+    "print(f\"Position embeddding shape: {position_embedding.shape} -> [batch_size, number_of_patches, embedding_dimension]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "332facb6-b478-4910-b620-c06d1462d8b8",
+   "metadata": {},
+   "source": [
+    "> **Note:** Only creating the position embedding as `torch.ones()` for demonstration purposes, in reality, you'd likely create the position embedding with `torch.randn()` (start with a random number and improve via gradient descent). \n",
+    "\n",
+    "Position embeddings created!\n",
+    "\n",
+    "Let's add them to our sequence of patch embeddings with a prepended class token."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "03370370-e2c2-4e46-bc20-302b97fba9d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[[2.0000, 2.0000, 2.0000,  ..., 2.0000, 2.0000, 2.0000],\n",
+      "         [0.5077, 1.0265, 0.9091,  ..., 1.1478, 0.9014, 1.2243],\n",
+      "         [0.0151, 1.3805, 0.6362,  ..., 1.6115, 0.9195, 1.2097],\n",
+      "         ...,\n",
+      "         [0.3332, 1.1713, 0.8289,  ..., 1.4699, 0.7119, 1.2599],\n",
+      "         [0.3017, 1.1949, 0.8116,  ..., 1.5152, 0.6874, 1.2151],\n",
+      "         [0.3111, 1.1862, 0.8556,  ..., 1.5019, 0.6436, 1.2378]]],\n",
+      "       grad_fn=<AddBackward0>)\n",
+      "Patch embeddings, class token prepended and positional embeddings added shape: torch.Size([1, 197, 768]) -> [batch_size, number_of_patches, embedding_dimension]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add the position embedding to the patch and class token embedding\n",
+    "patch_and_position_embedding = patch_embedded_image_with_class_embedding + position_embedding\n",
+    "print(patch_and_position_embedding)\n",
+    "print(f\"Patch embeddings, class token prepended and positional embeddings added shape: {patch_and_position_embedding.shape} -> [batch_size, number_of_patches, embedding_dimension]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80a39e97-4504-4931-9cec-2569389f3faf",
+   "metadata": {},
+   "source": [
+    "Notice how the values of each of the elements in the embedding tensor increases by 1 (this is because of the position embeddings being created with `torch.ones()`). \n",
+    "\n",
+    "> **Note:** We could put both the class token embedding and position embedding into their own layer if we wanted to. But we'll see later on how they can be incorporated into the overall ViT architecture's `forward()` method.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-patch-embeddings-with-learnable-class-token-and-position-embeddings.png\" width=900 alt=\"patch embeddings with learnable class token and position embeddings\"/>\n",
+    "\n",
+    "*The workflow we've used for adding the position embeddings to the sequence of patch embeddings and class token. Note: `torch.ones()` only used to create embeddings for illustration purposes, in practice, you'd likely use `torch.randn()` to start with a random number.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6654c7ed-eb94-408b-b435-9b352c84328b",
+   "metadata": {},
+   "source": [
+    "### TK 4.8 Putting it all together: from image to embedding\n",
+    "\n",
+    "Alright, we've come a long way in terms of turning our input images into an embedding and replicating equation 1 from section 3.1 of the ViT paper:\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\mathbf{z}_{0} &=\\left[\\mathbf{x}_{\\text {class }} ; \\mathbf{x}_{p}^{1} \\mathbf{E} ; \\mathbf{x}_{p}^{2} \\mathbf{E} ; \\cdots ; \\mathbf{x}_{p}^{N} \\mathbf{E}\\right]+\\mathbf{E}_{\\text {pos }}, & & \\mathbf{E} \\in \\mathbb{R}^{\\left(P^{2} \\cdot C\\right) \\times D}, \\mathbf{E}_{\\text {pos }} \\in \\mathbb{R}^{(N+1) \\times D}\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "Let's now put everything together in a single code cell and go from input image ($x$) to output embedding ${z}_0$.\n",
+    "\n",
+    "We can do so by:\n",
+    "1. Setting the patch size (we'll use `16` as it's widely used throughout the paper and for ViT-Base).\n",
+    "2. Getting a single image, printing it's shape and storing its height and width.\n",
+    "3. Adding a batch dimension to the single image so it's compatible with our `PatchEmbedding` layer.\n",
+    "4. Creating a `PatchEmbedding` layer with a `patch_size=16` and `embedding_dim=768` (from Table 1 for ViT-Base).\n",
+    "5. Passing the single image through the `PatchEmbedding` layer in 4 to create a sequence of patch embeddings.\n",
+    "6. Creating a class token embedding like in section 4.6. \n",
+    "7. Prepending the class token emebdding to the patch embeddings created in step 5.\n",
+    "8. Creating a position embedding like in section 4.7.\n",
+    "9. Adding the position embedding to the class token and patch embeddings created in step 7.\n",
+    "\n",
+    "We'll also make sure to set the random seeds with `set_seeds()` and print out the shapes of different tensors along the way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "8de90548-e6b0-4123-90ca-a23b0fab52a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Image tensor shape: torch.Size([3, 224, 224])\n",
+      "Input image with batch dimension shape: torch.Size([1, 3, 224, 224])\n",
+      "Patching embedding shape: torch.Size([1, 196, 768])\n",
+      "Class token embedding shape: torch.Size([1, 1, 768])\n",
+      "Patch embedding with class token shape: torch.Size([1, 197, 768])\n",
+      "Patch and position embedding shape: torch.Size([1, 197, 768])\n"
+     ]
+    }
+   ],
+   "source": [
+    "set_seeds()\n",
+    "\n",
+    "# 1. Set patch size\n",
+    "patch_size = 16\n",
+    "\n",
+    "# 2. Print shape of original image tensor and get the image dimensions\n",
+    "print(f\"Image tensor shape: {image.shape}\")\n",
+    "height, width = image.shape[1], image.shape[2]\n",
+    "\n",
+    "# 3. Get image tensor and add batch dimension\n",
+    "x = image.unsqueeze(0)\n",
+    "print(f\"Input image with batch dimension shape: {x.shape}\")\n",
+    "\n",
+    "# 4. Create patch embedding layer\n",
+    "patch_embedding_layer = PatchEmbedding(in_channels=3,\n",
+    "                                       patch_size=patch_size,\n",
+    "                                       embedding_dim=768)\n",
+    "\n",
+    "# 5. Pass image through patch embedding layer\n",
+    "patch_embedding = patch_embedding_layer(x)\n",
+    "print(f\"Patching embedding shape: {patch_embedding.shape}\")\n",
+    "\n",
+    "# 6. Create class token embedding\n",
+    "batch_size = patch_embedding.shape[0]\n",
+    "embedding_dimension = patch_embedding.shape[-1]\n",
+    "class_token = nn.Parameter(torch.ones(batch_size, 1, embedding_dimension),\n",
+    "                           requires_grad=True) # make sure it's learnable\n",
+    "print(f\"Class token embedding shape: {class_token.shape}\")\n",
+    "\n",
+    "# 7. Prepend class token embedding to patch embedding\n",
+    "patch_embedding_class_token = torch.cat((class_token, patch_embedding), dim=1)\n",
+    "print(f\"Patch embedding with class token shape: {patch_embedding_class_token.shape}\")\n",
+    "\n",
+    "# 8. Create position embedding\n",
+    "number_of_patches = int((height * width) / patch_size**2)\n",
+    "position_embedding = nn.Parameter(torch.ones(1, number_of_patches+1, embedding_dimension),\n",
+    "                                  requires_grad=True) # make sure it's learnable\n",
+    "\n",
+    "# 9. Add position embedding to patch embedding with class token\n",
+    "patch_and_position_embedding = patch_embedding_class_token + position_embedding\n",
+    "print(f\"Patch and position embedding shape: {patch_and_position_embedding.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "129b4a2b-0a24-461f-a437-ad49925576c4",
+   "metadata": {},
+   "source": [
+    "Woohoo!\n",
+    "\n",
+    "From a single image to patch and position embeddings in a single cell of code.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-1-putting-it-all-together.png\" width=900 alt=\"mapping equation 1 from the vision transformer paper to pytorch code\"/> \n",
+    "\n",
+    "*Mapping equation 1 from the ViT paper to our PyTorch code. This is the essence of paper replicating, taking a research paper and turning it into usable code.* \n",
+    "\n",
+    "Now we've got a way to encode our images and pass them to the Transformer Encoder in Figure 1 of the ViT paper.\n",
+    "\n",
+    "<img src=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/images/08-vit-paper-architecture-animation-full-architecture.gif\" alt=\"Vision transformer architecture animation, going from a single image and passing it through a patch embedidng layer and then passing it through the transformer encoder.\" width=900/>\n",
+    "\n",
+    "*Animating the entire ViT workflow: from patch embeddings to transformer encoder to MLP head.* \n",
+    "\n",
+    "From a code perspective, creating the patch embedding is probably the largest section of replicating the ViT paper.\n",
+    "\n",
+    "Many of the other parts of the ViT paper such as the Multi-Head Attention and Norm layers can be created using existing PyTorch layers.\n",
+    "\n",
+    "Onwards!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02f725de-64d1-41d2-a9d6-374cf6d4f589",
+   "metadata": {},
+   "source": [
+    "## TK. 5. Equation 2: Multi-Head Attention (MSA)\n",
+    "\n",
+    "We've got our input data patchified and embedded, now let's move onto the next part of the ViT architecture.\n",
+    "\n",
+    "To start, we'll break down the Transformer Encoder section into two parts (start small and increase when necessary).\n",
+    "\n",
+    "The first being equation 2 and the second being equation 3.\n",
+    "\n",
+    "Recall equation 2 states:\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\mathbf{z}_{\\ell}^{\\prime} &=\\operatorname{MSA}\\left(\\operatorname{LN}\\left(\\mathbf{z}_{\\ell-1}\\right)\\right)+\\mathbf{z}_{\\ell-1}, & & \\ell=1 \\ldots L\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "This indicates a Multi-Head Attention (MSA) layer wrapped in a LayerNorm (LN) layer with a residual connection (the input to the layer gets added to the output).\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-2-msa-block-mapped-to-equation.png\" alt=\"mapping equation 2 from the ViT paper to the ViT architecture diagram in figure 1\" width=900/> \n",
+    "\n",
+    "*Left: Figure 1 from the ViT paper with Multi-Head Attention and Norm layers as well as the residual connection (+) highlighted within the Transformer Encoder block. Right: Mapping the Multi-Head Self Attention (MSA) layer, Norm layer and residual connection to their respective parts of equation 2 in the ViT paper.*\n",
+    "\n",
+    "Many layers you find in research papers are already implemented in modern deep learning frameworks such as PyTorch.\n",
+    "\n",
+    "In saying this, to replicate these layers and residual connection with PyTorch code we can use:\n",
+    "* Multi-Head Self Attention (MSA) - [`torch.nn.MultiheadAttention()`](https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html). \n",
+    "* Norm (LN or LayerNorm) - [`torch.nn.LayerNorm()`](https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html).\n",
+    "* Residual connection - add the input to output (we'll see this later on when we create the full Transformer Encoder block). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97430d7a-a69b-423c-be2b-ac16e7f9f83f",
+   "metadata": {},
+   "source": [
+    "### 5.1 The LayerNorm (LN) layer\n",
+    "\n",
+    "[Layer Normalization](https://paperswithcode.com/method/layer-normalization) (`torch.nn.LayerNorm()` or Norm or LayerNorm or LN) normalizes an input over the last dimension.\n",
+    "\n",
+    "You can set `normalized_shape` to be equal to the dimension size you'd like to noramlize over (in our case it'll be $D$ or `768` for ViT-Base).\n",
+    "\n",
+    "You can find the formal definition of `torch.nn.LayerNorm()` in the [PyTorch documentation](https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html).\n",
+    "\n",
+    "What does it do?\n",
+    "\n",
+    "Layer Normalization helps improve training time and model generalization (ability to adapt to unseen data).\n",
+    "\n",
+    "I like to think of any kind of normalization as \"getting the data into a similar format\" or \"getting data samples into a similar distribution\".\n",
+    "\n",
+    "Imagine trying to walk up (or down) a set of stairs all with differing heights and lengths.\n",
+    "\n",
+    "It'd take some adjustment each step right?\n",
+    "\n",
+    "And what you learn for each step wouldn't necessary help with the next one since they all differ.\n",
+    "\n",
+    "Normalization (including Layer Normalization) is the equivalent of making all the stairs the same height and length except the stairs are your data samples.\n",
+    "\n",
+    "So just like you can walk up (or down) stairs with similar heights and lengths much easier than those with unequal heights and widths, neural networks can optimize over data samples with similar distributions (similar mean and standard-deviations) easier than those with varying distributions. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf09f6d0-2480-4577-a694-1171898e1777",
+   "metadata": {},
+   "source": [
+    "### 5.2 The Multi-Head Self Attention (MSA) layer \n",
+    "\n",
+    "The power of the self-attention and multi-head attention (self-attention applied multiple times) were revealed in the form of the original Transformer architecture introduced in the [*Attention is all you need*](https://arxiv.org/abs/1706.03762) research paper.\n",
+    "\n",
+    "There are many resources online to learn more about the Transformer architeture and attention mechanism online such as Jay Alammar's wonderful [Illustrated Transformer post](https://jalammar.github.io/illustrated-transformer/) and [Illustrated Attention post](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/).\n",
+    "\n",
+    "But we're going to focus more on coding an existing PyTorch MSA implementation than creating our own.\n",
+    "\n",
+    "However, you can find the formal defintion of the ViT paper's MSA implementation is defined in Appendix A:\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-2-appendix-A.png\" alt=\"vision transformer paper figure 1 highlighted with equation 2 and appendix A\" width=900/>\n",
+    "\n",
+    "*Left: Vision Transformer architecture overview from Figure 1 of the ViT paper. Right: Definitions of equation 2, section 3.1 and Appendix A of the ViT paper highlighted to reflect their respective parts in Figure 1.*\n",
+    "\n",
+    "The image above highlights the triple input to the MSA layer.\n",
+    "\n",
+    "This is known as **query, key, value** input or **qkv** for short which is fundamental to the self-attention mechanism.\n",
+    "\n",
+    "In our case, the triple input will be three versions of the output of the Norm layer.\n",
+    "\n",
+    "Or three versions of our layer-normalized image patch and position embeddings created in section 4.8.\n",
+    "\n",
+    "We can implement the MSA layer in PyTorch with `torch.nn.MultiheadAttention()` with the parameters:\n",
+    "* `embed_dim` - the embedding dimension from Table 1 (Hidden size $D$).\n",
+    "* `num_heads` - how many attention heads to use (this is where the term \"multihead\" comes from), this value is also in Table 1 (Heads).\n",
+    "* `dropout` - whether or not to apply dropout to the attention layer (according to Appendix B.1, dropout isn't used after the qkv-projections). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1a012fa-9bf6-4cf2-bbd0-30ed692f9d74",
+   "metadata": {},
+   "source": [
+    "### 5.3 Replicating Equation 2 with PyTorch layers\n",
+    "\n",
+    "Let's put everything we've discussed about the LayerNorm (LN) and Multi-Head Attention (MSA) layers in equation 2 into practice.\n",
+    "\n",
+    "To do so, we'll:  \n",
+    "\n",
+    "1. Create a class called `MultiheadSelfAttentionBlock()` that inherits from `torch.nn.Module`.\n",
+    "2. Initialize the class with hyperparameters from Table 1 of the ViT paper for the ViT-Base model.\n",
+    "3. Create a layer normalization (LN) layer with `torch.nn.LayerNorm()` with the `normalized_shape` parameter the same as our embedding dimension ($D$ from Table 1).\n",
+    "4. Create a multi-head attention (MSA) layer with the appropriate `embed_dim`, `num_heads`, `dropout` and `batch_first` parameters.\n",
+    "5. Create a `forward()` method for our class passing the in the inputs through the LN layer and MSA layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "b76ae98c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1. Create a class that inherits from nn.Module\n",
+    "class MultiheadSelfAttentionBlock(nn.Module):\n",
+    "    \"\"\"Creates a multi-head self-attention block (\"MSA block\" for short).\n",
+    "    \"\"\"\n",
+    "    # 2. Initialize the class with hyperparameters from Table 1\n",
+    "    def __init__(self,\n",
+    "                 embedding_dim:int=768, # from Table 1 for ViT-Base\n",
+    "                 num_heads:int=12, # from Table 1 for ViT-Base\n",
+    "                 attn_dropout:int=0): # doesn't look like the paper uses any dropout in MSABlocks\n",
+    "        super().__init__()\n",
+    "        \n",
+    "        # 3. Create the Norm layer (LN)\n",
+    "        self.layer_norm = nn.LayerNorm(normalized_shape=embedding_dim)\n",
+    "        \n",
+    "        # 4. Create the Multi-Head Attention (MSA) layer\n",
+    "        self.multihead_attn = nn.MultiheadAttention(embed_dim=embedding_dim,\n",
+    "                                                    num_heads=num_heads,\n",
+    "                                                    dropout=attn_dropout,\n",
+    "                                                    batch_first=True) # does our batch dimension come first?\n",
+    "        \n",
+    "    # 5. Create a forward() method to pass the data throguh the layers\n",
+    "    def forward(self, x):\n",
+    "        x = self.layer_norm(x)\n",
+    "        attn_output, _ = self.multihead_attn(query=x, # query embeddings \n",
+    "                                             key=x, # key embeddings\n",
+    "                                             value=x, # value embeddings\n",
+    "                                             need_weights=False) # do we need the weights or just the layer outputs?\n",
+    "        return attn_output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc1f0c30-a4ea-41e8-98b2-1a6d8de802d1",
+   "metadata": {},
+   "source": [
+    "> **Note:** Unlike Figure 1, our `MultiheadSelfAttentionBlock()` doesn't include a skip or residual connection (\"$+\\mathbf{z}_{\\ell-1}$\" in equation 2), we'll include this when we create the entire Transformer encoder later on. \n",
+    "\n",
+    "MSABlock created! \n",
+    "\n",
+    "Let's try it out by create an instance of our `MultiheadSelfAttentionBlock` and passing through the `patch_and_position_embedding` variable we created in section 4.8."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "ceb1dfc0-40ad-4cee-bc54-e9a5cec56895",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input shape of MSA block: torch.Size([1, 197, 768])\n",
+      "Output shape MSA block: torch.Size([1, 197, 768])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create an instance of MSABlock\n",
+    "multihead_self_attention_block = MultiheadSelfAttentionBlock(embedding_dim=768, # from Table 1 \n",
+    "                                                             num_heads=12) # from Table 1\n",
+    "\n",
+    "# Pass patch and position image embedding through MSABlock\n",
+    "patched_image_through_msa_block = multihead_self_attention_block(patch_and_position_embedding)\n",
+    "print(f\"Input shape of MSA block: {patch_and_position_embedding.shape}\")\n",
+    "print(f\"Output shape MSA block: {patched_image_through_msa_block.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c9f8384-6120-495a-b253-baff5de58097",
+   "metadata": {},
+   "source": [
+    "Notice how the input and output shape of our data stays the same when it goes through the MSA block.\n",
+    "\n",
+    "This doesn't mean the data doesn't change as it goes through.\n",
+    "\n",
+    "You could try printing the input and output tensor to see how it changes (though this change will be across `1 * 197 * 768` values). \n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/08-vit-paper-equation-2-in-code.png\" width=900 alt=\"vision transformer paper with equation 2 of figure 1 highlighted and equation 2 turned into code\"/>\n",
+    "\n",
+    "*Left: Vision Transformer architecture from Figure 1 with Multi-Head Attention and LayerNorm layers highlighted, these layers make up equation 2 from section 3.1 of the paper. Right: Replicating equation 2 (without the skip connection on the end) using PyTorch layers.* \n",
+    "\n",
+    "We've now officially replicated equation 2 (except for the residual connection on the end but we'll get to this in section 7)!\n",
+    "\n",
+    "Onto the next!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0168ef51-ab1f-4ee7-8e9d-2ee7ecca3c7b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## TK 6. Equation 3: Multilayer Perceptron (MLP)\n",
+    "\n",
+    "UPTOHERE:\n",
+    "* Replicate equation 3 like replicating equation 2\n",
+    "\n",
+    "* TK also called \"feedforward\" \n",
+    "\n",
+    "> Dropout, when used, is applied **after every dense layer except for the the qkv-projections and directly after adding positional- to patch embeddings.**\n",
+    "\n",
+    "> The MLP contains two layers with a GELU non-linearity\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\mathbf{z}_{\\ell} &=\\operatorname{MLP}\\left(\\operatorname{LN}\\left(\\mathbf{z}_{\\ell}^{\\prime}\\right)\\right)+\\mathbf{z}_{\\ell}^{\\prime}, & & \\ell=1 \\ldots L\n",
+    "\\end{aligned}\n",
+    "$$ \n",
+    "\n",
+    "* TK - GELU in PyTorch -- https://pytorch.org/docs/stable/generated/torch.nn.GELU.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "68d9dbfe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Could also call this \"FeedForward\"\n",
+    "class MLPBlock(nn.Module):\n",
+    "    \"\"\"Creates an MLPBlock of the Vision Transformer architecture.\"\"\"\n",
+    "    def __init__(self,\n",
+    "                 embedding_dim, # embedding dimension (Hidden Size D in Table 1)\n",
+    "                 mlp_size, # MLP size in Table 1\n",
+    "                 dropout=0): # \"Dropout... is applied to every dense layer... (Appendix B.1)\"\n",
+    "        super().__init__()\n",
+    "        \n",
+    "        self.layer_norm = nn.LayerNorm(normalized_shape=embedding_dim)\n",
+    "        \n",
+    "        self.mlp = nn.Sequential(\n",
+    "            nn.Linear(in_features=embedding_dim,\n",
+    "                      out_features=mlp_size),\n",
+    "            nn.GELU(), # \"The MLP contains two layers with a GELU non-linearity (section 3.1).\"\n",
+    "            nn.Dropout(p=dropout),\n",
+    "            nn.Linear(in_features=mlp_size, # needs to take same in_features as out_features of layer above\n",
+    "                      out_features=embedding_dim), # take back to embedding_dim\n",
+    "            nn.Dropout(p=dropout)\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.layer_norm(x)\n",
+    "        x = self.mlp(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "442fb987",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 197, 768])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mlp_block = MLPBlock(embedding_dim=768, # Table 1 \n",
+    "                     mlp_size=3072) # Table 1\n",
+    "patched_image_through_mlp_block = mlp_block(patched_image_through_msa_block)\n",
+    "patched_image_through_mlp_block.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "812ccd0a-554f-4722-9987-f03c079a269f",
+   "metadata": {},
+   "source": [
+    "## TK 7. Create the Transformer Encoder \n",
+    "\n",
+    "* Tk - what is an \"encoder\"?\n",
+    "* Tk - \"transformer block\" or \"transformer encoder\"? - line this up with the paper\n",
+    "\n",
+    "See here for pre-built transformer blocks/layers: https://pytorch.org/docs/stable/nn.html#transformer-layers \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "2c43855c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class TransformerEncoderBlock(nn.Module):\n",
+    "    \"\"\"Creates a Transformer Encoder block.\"\"\"\n",
+    "    def __init__(self,\n",
+    "                 embedding_dim=768, # From Table 1\n",
+    "                 num_heads=12, # From Table 1\n",
+    "                 mlp_size=3072, # From Table 1\n",
+    "                 mlp_dropout=0.1,\n",
+    "                 attn_dropout=0):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Create MSA Block (for equation 2)\n",
+    "        self.msa_block = MultiheadSelfAttentionBlock(embedding_dim=embedding_dim,\n",
+    "                                                     num_heads=num_heads,\n",
+    "                                                     attn_dropout=attn_dropout)\n",
+    "        # Create MLP Block (for equation 3)\n",
+    "        self.mlp_block =  MLPBlock(embedding_dim=embedding_dim,\n",
+    "                                   mlp_size=mlp_size,\n",
+    "                                   dropout=mlp_dropout)\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        x = self.msa_block(x) + x # Create skip connection\n",
+    "        x = self.mlp_block(x) + x # Create skip connection\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94f9041c-a0af-4fbe-8969-a2bde8637821",
+   "metadata": {},
+   "source": [
+    "## TK 8. Putting it all together to create ViT\n",
+    " \n",
+    "TK - replicate this with the TransformerEncoderLayer - https://pytorch.org/blog/a-better-transformer-for-fast-transformer-encoder-inference/\n",
+    "\n",
+    "Combine the transformer blocks and patched embedding into a ViT architecture."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "2df890d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ViT(nn.Module):\n",
+    "    \"\"\"Creates a Vision Transformer architecture.\"\"\"\n",
+    "    def __init__(self,\n",
+    "                 img_size=224, # From Table 3 in ViT paper\n",
+    "                 in_channels=3,\n",
+    "                 patch_size=16,\n",
+    "                 num_transformer_layers=12, # From Table 1 in ViT paper\n",
+    "                 embedding_dim=768,\n",
+    "                 mlp_size=3072,\n",
+    "                 num_heads=12,\n",
+    "                 attn_dropout=0,\n",
+    "                 mlp_dropout=0.1,\n",
+    "                 embedding_dropout=0.1,\n",
+    "                 num_classes=1000): # default for ImageNet\n",
+    "        super().__init__() # don't forget the super().__init__()!\n",
+    "    \n",
+    "        # Get image size\n",
+    "        self.img_height, self.img_width = img_size, img_size\n",
+    "        \n",
+    "        # Calculate number of patches (height * width/patch^2)\n",
+    "        self.num_patches = (self.img_height * self.img_width) // patch_size**2\n",
+    "        \n",
+    "                 \n",
+    "        # Create class embedding (needs to go at front of sequence embedding)\n",
+    "        self.class_embedding = nn.Parameter(data=torch.randn(1, 1, embedding_dim),\n",
+    "                                            requires_grad=True)\n",
+    "        \n",
+    "         # Create position embedding\n",
+    "        self.position_embedding = nn.Parameter(data=torch.randn(1, self.num_patches+1, embedding_dim),\n",
+    "                                               requires_grad=True)\n",
+    "                \n",
+    "        # Create embedding dropout\n",
+    "        self.embedding_dropout = nn.Dropout(p=embedding_dropout)\n",
+    "        \n",
+    "        # Create patch embedding layer\n",
+    "        self.patch_embedding = PatchEmbedding(in_channels=in_channels,\n",
+    "                                              patch_size=patch_size,\n",
+    "                                              embedding_dim=embedding_dim)\n",
+    "        \n",
+    "        # Create transformer encoder blocks\n",
+    "        self.transformer_enedoder = nn.Sequential(*[TransformerEncoderBlock(embedding_dim=embedding_dim,\n",
+    "                                                                            num_heads=num_heads,\n",
+    "                                                                            mlp_size=mlp_size,\n",
+    "                                                                            mlp_dropout=mlp_dropout) for _ in range(num_transformer_layers)])\n",
+    "       \n",
+    "        # Create classifier head (equation 4)\n",
+    "        self.classifier = nn.Sequential(\n",
+    "            nn.LayerNorm(normalized_shape=embedding_dim),\n",
+    "            nn.Linear(in_features=embedding_dim, \n",
+    "                      out_features=num_classes)\n",
+    "        )\n",
+    "    \n",
+    "    def forward(self, x):\n",
+    "        # Get batch size\n",
+    "        batch_size = x.shape[0]\n",
+    "        # Create class token embedding\n",
+    "        class_token = self.class_embedding.expand(batch_size, -1, -1)\n",
+    "\n",
+    "        # Create patch embedding\n",
+    "        x = self.patch_embedding(x)\n",
+    "\n",
+    "        # Concat class embedding and patch embedding (equation 1)\n",
+    "        x = torch.cat((class_token, x), dim=1)\n",
+    "\n",
+    "        # Add position embedding to patch embedding (equation 1) for every batch\n",
+    "        x = self.position_embedding + x\n",
+    "\n",
+    "        # Run embedding dropout\n",
+    "        x = self.embedding_dropout(x)\n",
+    "\n",
+    "        # Pass patch, position and class embedding through transformer encoder layers (equations 2 & 3)\n",
+    "        x = self.transformer_enedoder(x)\n",
+    "\n",
+    "        # Put 0 index logit through classifier (equation 4)\n",
+    "        x = self.classifier(x[:, 0]) # run on each sample in a batch at 0 index\n",
+    "\n",
+    "        return x\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "7dc9f8ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([32, 1, 768])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "batch_size = 32\n",
+    "class_tokens = nn.Parameter(data=torch.randn(1, 1, 768))\n",
+    "class_tokens.expand(batch_size, -1, -1).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "f86190a3-ed1f-4ab3-881c-4eecea996912",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-0.2377,  0.7360,  1.2137]], grad_fn=<AddmmBackward0>)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set_seeds()\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "rand_image = torch.randn(1, 3, 224, 224)\n",
+    "# vit = ViT(num_classes=len(class_names)) \n",
+    "vit = ViT(num_classes=3)\n",
+    "vit(rand_image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c0a0c9c-6d98-47fd-a152-8a06be99b5fa",
+   "metadata": {},
+   "source": [
+    "## TK 9. Inspect the model\n",
+    "\n",
+    "> **Note:** If you go too big, your hardware might not be able to handle it... (e.g. too high of a batch size...)\n",
+    "\n",
+    "TK - Number of parameters should be equivalent to: https://pytorch.org/vision/main/models/generated/torchvision.models.vit_b_16.html#torchvision.models.vit_b_16 (`num_params=86,567,656`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "494bde26-ed1e-45dc-b615-78ac268ca20e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "======================================================================================================================================================\n",
+       "Layer (type (var_name))                                                Input Shape          Output Shape         Param #              Trainable\n",
+       "======================================================================================================================================================\n",
+       "ViT (ViT)                                                              [128, 3, 224, 224]   [128, 3]             152,064              True\n",
+       "├─Dropout (embedding_dropout)                                          [128, 197, 768]      [128, 197, 768]      --                   --\n",
+       "├─PatchEmbedding (patch_embedding)                                     [128, 3, 224, 224]   [128, 196, 768]      --                   True\n",
+       "│    └─Conv2d (patcher)                                                [128, 3, 224, 224]   [128, 768, 14, 14]   590,592              True\n",
+       "│    └─Flatten (flatten)                                               [128, 768, 14, 14]   [128, 768, 196]      --                   --\n",
+       "├─Dropout (embedding_dropout)                                          [128, 197, 768]      [128, 197, 768]      --                   --\n",
+       "├─Sequential (transformer_enedoder)                                    [128, 197, 768]      [128, 197, 768]      --                   True\n",
+       "│    └─TransformerEncoderBlock (0)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
+       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
+       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
+       "│    └─TransformerEncoderBlock (1)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
+       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
+       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
+       "│    └─TransformerEncoderBlock (2)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
+       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
+       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
+       "│    └─TransformerEncoderBlock (3)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
+       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
+       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
+       "│    └─TransformerEncoderBlock (4)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
+       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
+       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
+       "│    └─TransformerEncoderBlock (5)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
+       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
+       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
+       "│    └─TransformerEncoderBlock (6)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
+       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
+       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
+       "│    └─TransformerEncoderBlock (7)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
+       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
+       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
+       "│    └─TransformerEncoderBlock (8)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
+       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
+       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
+       "│    └─TransformerEncoderBlock (9)                                     [128, 197, 768]      [128, 197, 768]      --                   True\n",
+       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
+       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
+       "│    └─TransformerEncoderBlock (10)                                    [128, 197, 768]      [128, 197, 768]      --                   True\n",
+       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
+       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
+       "│    └─TransformerEncoderBlock (11)                                    [128, 197, 768]      [128, 197, 768]      --                   True\n",
+       "│    │    └─MultiheadSelfAttentionBlock (msa_block)                    [128, 197, 768]      [128, 197, 768]      2,363,904            True\n",
+       "│    │    └─MLPBlock (mlp_block)                                       [128, 197, 768]      [128, 197, 768]      4,723,968            True\n",
+       "├─Sequential (classifier)                                              [128, 768]           [128, 3]             --                   True\n",
+       "│    └─LayerNorm (0)                                                   [128, 768]           [128, 768]           1,536                True\n",
+       "│    └─Linear (1)                                                      [128, 768]           [128, 3]             2,307                True\n",
+       "======================================================================================================================================================\n",
+       "Total params: 85,800,963\n",
+       "Trainable params: 85,800,963\n",
+       "Non-trainable params: 0\n",
+       "Total mult-adds (G): 22.08\n",
+       "======================================================================================================================================================\n",
+       "Input size (MB): 77.07\n",
+       "Forward/backward pass size (MB): 13168.81\n",
+       "Params size (MB): 257.55\n",
+       "Estimated Total Size (MB): 13503.43\n",
+       "======================================================================================================================================================"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from torchinfo import summary\n",
+    "\n",
+    "# TK - clean up the summary so it looks nice when it prints out \n",
+    "# Print a summary using torchinfo (uncomment for actual output)\n",
+    "summary(model=vit, \n",
+    "        input_size=(128, 3, 224, 224), # make sure this is \"input_size\", not \"input_shape\"\n",
+    "        # col_names=[\"input_size\"], # uncomment for smaller output\n",
+    "        col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
+    "        col_width=20,\n",
+    "        row_settings=[\"var_names\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0279251-5cc1-42a5-bebc-8391ef911343",
+   "metadata": {},
+   "source": [
+    "* TK - same number of parameters as: https://pytorch.org/vision/main/models/generated/torchvision.models.vit_b_16.html#torchvision.models.vit_b_16 -> 86567656"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "3791f963-7d51-418b-b8bd-4c77341560e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(torch.Size([1, 1, 768]), torch.Size([32, 1, 768]))"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "batch_size = 32\n",
+    "cls_embedding = nn.Parameter(torch.randn(1, 1, 768))\n",
+    "# See here: https://pytorch.org/docs/stable/generated/torch.Tensor.expand.html\n",
+    "cls_embedding.shape, cls_embedding.expand(batch_size, -1, -1).shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd92b752-22f0-4a13-95ac-e085d20013ac",
+   "metadata": {},
+   "source": [
+    "## TK 10. Train model\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "9107b068-f253-4026-ad21-83be41404043",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0632771598bb43e7a49ed5ffa70c4490",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 1 | train_loss: 4.8759 | train_acc: 0.2891 | test_loss: 1.0465 | test_acc: 0.5417\n",
+      "Epoch: 2 | train_loss: 1.5900 | train_acc: 0.2617 | test_loss: 1.5876 | test_acc: 0.1979\n",
+      "Epoch: 3 | train_loss: 1.4644 | train_acc: 0.2617 | test_loss: 1.2738 | test_acc: 0.1979\n",
+      "Epoch: 4 | train_loss: 1.3159 | train_acc: 0.2773 | test_loss: 1.7498 | test_acc: 0.1979\n",
+      "Epoch: 5 | train_loss: 1.3114 | train_acc: 0.3008 | test_loss: 1.7444 | test_acc: 0.2604\n",
+      "Epoch: 6 | train_loss: 1.2445 | train_acc: 0.3008 | test_loss: 1.9704 | test_acc: 0.1979\n",
+      "Epoch: 7 | train_loss: 1.2050 | train_acc: 0.3984 | test_loss: 3.5480 | test_acc: 0.1979\n",
+      "Epoch: 8 | train_loss: 1.4368 | train_acc: 0.4258 | test_loss: 1.8324 | test_acc: 0.2604\n",
+      "Epoch: 9 | train_loss: 1.5757 | train_acc: 0.2344 | test_loss: 1.2848 | test_acc: 0.5417\n",
+      "Epoch: 10 | train_loss: 1.4658 | train_acc: 0.4023 | test_loss: 1.2389 | test_acc: 0.2604\n"
+     ]
+    }
+   ],
+   "source": [
+    "from going_modular.going_modular import engine\n",
+    "\n",
+    "optimizer = torch.optim.Adam(params=vit.parameters(), \n",
+    "                             lr=1e-3,\n",
+    "                             betas=(0.9, 0.999), # default\n",
+    "                             weight_decay=0.1) # from the ViT paper section 4.1\n",
+    "loss_fn = torch.nn.CrossEntropyLoss()\n",
+    "\n",
+    "set_seeds()\n",
+    "results = engine.train(model=vit,\n",
+    "                       train_dataloader=train_dataloader,\n",
+    "                       test_dataloader=test_dataloader,\n",
+    "                       optimizer=optimizer,\n",
+    "                       loss_fn=loss_fn,\n",
+    "                       epochs=10,\n",
+    "                       device=device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32cb3ad2-6eea-4b69-9ce8-322270643919",
+   "metadata": {},
+   "source": [
+    "## TK 11. Evaluate model\n",
+    "\n",
+    "TK - plot the loss curves"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "fcca1148-6475-4012-bfc9-cbad2706c22d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from helper_functions import plot_loss_curves\n",
+    "\n",
+    "plot_loss_curves(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c370cae-9854-474c-be05-51dabe62c204",
+   "metadata": {},
+   "source": [
+    "TK - why do the loss curves look the way they do? (too big of a model, not enough data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9bc3d75-028d-4d48-bcfb-d0dd4f53d2ca",
+   "metadata": {},
+   "source": [
+    "## TK 12. Bring in pretrained ViT from `torchvision.models` on same dataset \n",
+    "\n",
+    "* Get a similar model from here - https://pytorch.org/vision/main/models/generated/torchvision.models.vit_b_16.html#torchvision.models.vit_b_16 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "30de8333-74b0-49ae-a81e-0266e6325f26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.12.0+cu102\n",
+      "0.13.0+cu102\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The following requires torch v0.12+ and torchvision v0.13+\n",
+    "import torch\n",
+    "import torchvision\n",
+    "print(torch.__version__) \n",
+    "print(torchvision.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "b0b87f68-98cc-49f8-89bd-ff220a757f76",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'cuda'"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "device"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "29887400-2f92-4c75-961e-0541bea6b73a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set seeds\n",
+    "def set_seeds(seed=42):\n",
+    "    torch.manual_seed(seed)\n",
+    "    torch.cuda.manual_seed(seed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "b8e2dda6-8af0-4255-815f-4d885fa4b477",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Requires torchvision >= 0.13\n",
+    "pretrained_vit_weights = torchvision.models.ViT_B_16_Weights.DEFAULT\n",
+    "pretrained_vit = torchvision.models.vit_b_16(weights=pretrained_vit_weights).to(device)\n",
+    "\n",
+    "# Freeze the base parameters\n",
+    "for parameter in pretrained_vit.parameters():\n",
+    "    parameter.requires_grad = False\n",
+    "    \n",
+    "# Change the classifier head\n",
+    "set_seeds()\n",
+    "pretrained_vit.heads = nn.Linear(in_features=768, out_features=len(class_names)).to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "8fbd83a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "======================================================================================================================================================\n",
+       "Layer (type (var_name))                                                Input Shape          Output Shape         Param #              Trainable\n",
+       "======================================================================================================================================================\n",
+       "VisionTransformer (VisionTransformer)                                  [128, 3, 224, 224]   [128, 3]             768                  Partial\n",
+       "├─Conv2d (conv_proj)                                                   [128, 3, 224, 224]   [128, 768, 14, 14]   (590,592)            False\n",
+       "├─Encoder (encoder)                                                    [128, 197, 768]      [128, 197, 768]      151,296              False\n",
+       "│    └─Dropout (dropout)                                               [128, 197, 768]      [128, 197, 768]      --                   --\n",
+       "│    └─Sequential (layers)                                             [128, 197, 768]      [128, 197, 768]      --                   False\n",
+       "│    │    └─EncoderBlock (encoder_layer_0)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
+       "│    │    └─EncoderBlock (encoder_layer_1)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
+       "│    │    └─EncoderBlock (encoder_layer_2)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
+       "│    │    └─EncoderBlock (encoder_layer_3)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
+       "│    │    └─EncoderBlock (encoder_layer_4)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
+       "│    │    └─EncoderBlock (encoder_layer_5)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
+       "│    │    └─EncoderBlock (encoder_layer_6)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
+       "│    │    └─EncoderBlock (encoder_layer_7)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
+       "│    │    └─EncoderBlock (encoder_layer_8)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
+       "│    │    └─EncoderBlock (encoder_layer_9)                             [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
+       "│    │    └─EncoderBlock (encoder_layer_10)                            [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
+       "│    │    └─EncoderBlock (encoder_layer_11)                            [128, 197, 768]      [128, 197, 768]      (7,087,872)          False\n",
+       "│    └─LayerNorm (ln)                                                  [128, 197, 768]      [128, 197, 768]      (1,536)              False\n",
+       "├─Linear (heads)                                                       [128, 768]           [128, 3]             2,307                True\n",
+       "======================================================================================================================================================\n",
+       "Total params: 85,800,963\n",
+       "Trainable params: 2,307\n",
+       "Non-trainable params: 85,798,656\n",
+       "Total mult-adds (G): 22.08\n",
+       "======================================================================================================================================================\n",
+       "Input size (MB): 77.07\n",
+       "Forward/backward pass size (MB): 13322.95\n",
+       "Params size (MB): 257.55\n",
+       "Estimated Total Size (MB): 13657.57\n",
+       "======================================================================================================================================================"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Print a summary using torchinfo (uncomment for actual output)\n",
+    "summary(model=pretrained_vit, \n",
+    "        input_size=(128, 3, 224, 224), # make sure this is \"input_size\", not \"input_shape\"\n",
+    "        # col_names=[\"input_size\"], # uncomment for smaller output\n",
+    "        col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
+    "        col_width=20,\n",
+    "        row_settings=[\"var_names\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "2e4ed730-de72-415f-99d1-5dffd57e9dec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TK - the above output has the same number of parameters as our own created model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "94cb3900",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[INFO] data/pizza_steak_sushi directory exists, skipping download.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('data/pizza_steak_sushi')"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Download pizza, steak, sushi images from GitHub\n",
+    "image_path = download_data(source=\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\",\n",
+    "                           destination=\"pizza_steak_sushi\")\n",
+    "image_path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "2e6ae0fe-73c0-4930-988a-e4df903084b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(PosixPath('data/pizza_steak_sushi/train'),\n",
+       " PosixPath('data/pizza_steak_sushi/test'))"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_dir = image_path / \"train\"\n",
+    "test_dir = image_path / \"test\" \n",
+    "train_dir, test_dir"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "dd2f58ff-6182-453a-a802-70ff98c09557",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ImageClassification(\n",
+      "    crop_size=[224]\n",
+      "    resize_size=[256]\n",
+      "    mean=[0.485, 0.456, 0.406]\n",
+      "    std=[0.229, 0.224, 0.225]\n",
+      "    interpolation=InterpolationMode.BILINEAR\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create dataset for pretrained ViT\n",
+    "pretrained_vit_transforms = pretrained_vit_weights.transforms()\n",
+    "print(pretrained_vit_transforms)\n",
+    "\n",
+    "train_dataloader_pretrained, test_dataloader_pretrained, class_names = data_setup.create_dataloaders(train_dir=train_dir,\n",
+    "                                                                                                     test_dir=test_dir,\n",
+    "                                                                                                     transform=pretrained_vit_transforms,\n",
+    "                                                                                                     batch_size=1024) # From here: https://arxiv.org/abs/2205.01580 (there are other improvements there too...)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "a49408b4-24d9-4bb1-90a2-dd61c08f78a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7904829b8c1646d38bb6033a5b4367c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 1 | train_loss: 1.1490 | train_acc: 0.2356 | test_loss: 1.0584 | test_acc: 0.4667\n",
+      "Epoch: 2 | train_loss: 1.0017 | train_acc: 0.5289 | test_loss: 0.9194 | test_acc: 0.6400\n",
+      "Epoch: 3 | train_loss: 0.8716 | train_acc: 0.7244 | test_loss: 0.7983 | test_acc: 0.6667\n",
+      "Epoch: 4 | train_loss: 0.7583 | train_acc: 0.8089 | test_loss: 0.6942 | test_acc: 0.7733\n",
+      "Epoch: 5 | train_loss: 0.6608 | train_acc: 0.8622 | test_loss: 0.6060 | test_acc: 0.8800\n",
+      "Epoch: 6 | train_loss: 0.5777 | train_acc: 0.8889 | test_loss: 0.5318 | test_acc: 0.8933\n",
+      "Epoch: 7 | train_loss: 0.5076 | train_acc: 0.9156 | test_loss: 0.4700 | test_acc: 0.9067\n",
+      "Epoch: 8 | train_loss: 0.4487 | train_acc: 0.9244 | test_loss: 0.4188 | test_acc: 0.9333\n",
+      "Epoch: 9 | train_loss: 0.3993 | train_acc: 0.9378 | test_loss: 0.3765 | test_acc: 0.9333\n",
+      "Epoch: 10 | train_loss: 0.3580 | train_acc: 0.9378 | test_loss: 0.3417 | test_acc: 0.9467\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train pretrained feature extractor ViT for 5 epochs on Pizza, Steak, Sushi\n",
+    "# TK - can probably increase the batch_size here because we're using feature extraction and not \n",
+    "# training the whole model\n",
+    "from going_modular.going_modular import engine\n",
+    "\n",
+    "optimizer = torch.optim.Adam(params=pretrained_vit.parameters(), \n",
+    "                             lr=1e-3)\n",
+    "loss_fn = torch.nn.CrossEntropyLoss()\n",
+    "\n",
+    "set_seeds()\n",
+    "pretrained_vit_results = engine.train(model=pretrained_vit,\n",
+    "                                      train_dataloader=train_dataloader_pretrained,\n",
+    "                                      test_dataloader=test_dataloader_pretrained,\n",
+    "                                      optimizer=optimizer,\n",
+    "                                      loss_fn=loss_fn,\n",
+    "                                      epochs=10,\n",
+    "                                      device=device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "3c0af18e-6419-4dd6-b8ea-f5830bbd63d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the loss curves\n",
+    "from helper_functions import plot_loss_curves\n",
+    "\n",
+    "plot_loss_curves(pretrained_vit_results) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "0fd00943-01aa-4ef4-b366-3cb859a25b6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[INFO] Saving model to: models/08_pretrained_vit_feature_extractor_pizza_steak_sushi.pth\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save the model\n",
+    "from going_modular.going_modular import utils\n",
+    "\n",
+    "utils.save_model(model=pretrained_vit,\n",
+    "                 target_dir=\"models\",\n",
+    "                 model_name=\"08_pretrained_vit_feature_extractor_pizza_steak_sushi.pth\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "f52ef12c-b88e-4796-84eb-981491a84334",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pretrained ViT feature extractor model size: 327 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "# Get the model size in bytes then convert to megabytes\n",
+    "pretrained_vit_model_size = Path(\"models/08_pretrained_vit_feature_extractor_pizza_steak_sushi.pth\").stat().st_size // (1024*1024)\n",
+    "print(f\"Pretrained ViT feature extractor model size: {pretrained_vit_model_size} MB\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "277a08e0-bc0e-453d-bf0f-b56164ae71ab",
+   "metadata": {},
+   "source": [
+    "## TK - Things this replication misses out on\n",
+    "\n",
+    "TK Put down the difference in the paper vs this replication\n",
+    "* Many of these things are in Table 3:\n",
+    "    * training data (ImageNet from scratch vs FoodVision Mini data) \n",
+    "    * LR warmup\n",
+    "    * LR decay\n",
+    "    * Weight decay\n",
+    "    * Number of epochs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04b1569b-117e-43fd-9e0b-324157cb82a4",
+   "metadata": {},
+   "source": [
+    "## TK - Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd69be46-cb68-4391-9834-8f87d8814722",
+   "metadata": {},
+   "source": [
+    "## TK - Extra-curriculum\n",
+    "\n",
+    "* layernorm\n",
+    "* See the illustrated transformer for an overview of the Transformer model:  https://jalammar.github.io/illustrated-transformer/ + https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/\n",
+    "* Attention is all you need paper - Yannic video\n",
+    "* Vision transformer - yannic video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ecc8ddc-fd70-4e4b-b888-430f01de73c9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.8.13"
+  },
+  "vscode": {
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+}