Full Code of MagNet-DL/magnet for AI

Repository: MagNet-DL/magnet
Branch: master
Commit: bff6748803ac
Files: 69
Total size: 502.4 KB

Directory structure:
gitextract_kpew33sp/

├── .gitignore
├── .travis.yml
├── LICENSE
├── README.md
├── Tutorials/
│   └── MNIST-Quickstart/
│       ├── MNIST-Quickstart.ipynb
│       └── mnist_quickstart.py
├── arghandle/
│   ├── README.md
│   ├── __init__.py
│   ├── core.py
│   └── handlers.py
├── docs/
│   ├── Makefile
│   ├── make.bat
│   ├── requirements.txt
│   └── source/
│       ├── _static/
│       │   └── css/
│       │       └── magnet-theme.css
│       ├── _templates/
│       │   └── footer.html
│       ├── conf.py
│       ├── data.rst
│       ├── debug.rst
│       ├── index.rst
│       ├── magnet.rst
│       ├── nodes.rst
│       ├── training.rst
│       └── utils.rst
├── environment.yml
├── magnet/
│   ├── __init__.py
│   ├── _autograd.py
│   ├── data/
│   │   ├── __init__.py
│   │   ├── core.py
│   │   ├── data.py
│   │   ├── dataloader.py
│   │   ├── sampler.py
│   │   └── transforms.py
│   ├── debug.py
│   ├── nodes/
│   │   ├── __init__.py
│   │   ├── core.py
│   │   ├── functional/
│   │   │   ├── __init__.py
│   │   │   ├── activations.py
│   │   │   ├── functional.py
│   │   │   ├── losses.py
│   │   │   └── metrics.py
│   │   └── nodes.py
│   ├── training/
│   │   ├── __init__.py
│   │   ├── callbacks.py
│   │   ├── history.py
│   │   ├── train.py
│   │   └── utils.py
│   └── utils/
│       ├── __arghandle__/
│       │   ├── __init__.py
│       │   └── images.py
│       ├── __init__.py
│       ├── _node.py
│       ├── images.py
│       ├── misc.py
│       ├── plot.py
│       ├── statistical.py
│       └── varseq.py
├── readthedocs.yml
├── setup.py
└── tests/
    ├── data/
    │   └── test_dataloader.py
    ├── nodes/
    │   ├── test_core.py
    │   └── test_nodes.py
    ├── test_debug.py
    ├── training/
    │   ├── test_callbacks.py
    │   ├── test_history.py
    │   └── test_train.py
    └── utils/
        ├── test__node.py
        ├── test_images.py
        ├── test_plot.py
        ├── test_statistical.py
        └── test_varseq.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================

# Created by https://www.gitignore.io/api/python,pycharm+all,jupyternotebook

**/checkpoints/

### JupyterNotebook ###
.ipynb_checkpoints
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# Remove previous ipynb_checkpoints
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# Sensitive or high-churn files:
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.idea/**/dataSources.ids
.idea/**/dataSources.xml
.idea/**/dataSources.local.xml
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.idea/**/dynamic.xml
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# Gradle:
.idea/**/gradle.xml
.idea/**/libraries

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cmake-build-debug/

# Mongo Explorer plugin:
.idea/**/mongoSettings.xml

## File-based project format:
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# IntelliJ
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.idea_modules/

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atlassian-ide-plugin.xml

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/.rakeTasks

# Crashlytics plugin (for Android Studio and IntelliJ)
com_crashlytics_export_strings.xml
crashlytics.properties
crashlytics-build.properties
fabric.properties

### PyCharm+all Patch ###
# Ignores the whole idea folder
# See https://github.com/joeblau/gitignore.io/issues/186 and https://github.com/joeblau/gitignore.io/issues/360

.idea/

### Python ###
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
/__init__.py

# C extensions
*.so

# Distribution / packaging
.Python
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*.egg-info/
.installed.cfg
*.egg

# PyInstaller
#  Usually these files are written by a python script from a template
#  before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/

test_todo.py

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
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# Sphinx documentation
docs/_build/

# PyBuilder
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# Jupyter Notebook
test*.ipynb

# pyenv
.python-version

# celery beat schedule file
celerybeat-schedule.*

# SageMath parsed files
*.sage.py

# Environments
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env.bak/
venv.bak/

# Spyder project settings
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# Rope project settings
.ropeproject

# mkdocs documentation
/site

# mypy
.mypy_cache/


# End of https://www.gitignore.io/api/python,pycharm+all,jupyternotebook


================================================
FILE: .travis.yml
================================================
language: python
dist: xenial
python:
  - "3.6"

install:
  - python setup.py install
  - pip install codecov

before_script:
  - export PYTHONPATH=$PYTHONPATH:$(pwd)

script:
  - pytest tests/ --cov=magnet

after_success:
- codecov

================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2018 Vaisakh

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: README.md
================================================
# MagNet: Deep Learning Projects that Build Themselves

<div align="center">
  <img src="docs/source/_static/img/logo-full.png" alt="MagNet Logo"/>
</div>

<br>

[![GitHub](https://img.shields.io/github/license/mashape/apistatus.svg)](https://github.com/svaisakh/magnet/blob/master/LICENSE)
[![Build Status](https://travis-ci.org/MagNet-DL/magnet.svg?branch=master)](https://travis-ci.org/MagNet-DL/magnet)
[![Documentation Status](https://readthedocs.org/projects/magnet-dl/badge/?version=latest)](https://magnet-dl.readthedocs.io/en/latest/?badge=latest)
![Python Version](https://img.shields.io/badge/python-3.6-blue.svg)
[![GitHub Release](https://img.shields.io/badge/release-v0.1.0-ff69b4.svg)](https://github.com/svaisakh/magnet/releases)

[![Join the chat at https://gitter.im/MagNet-DL/Lobby](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/MagNet-DL/Lobby/?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat)](https://github.com/svaisakh/magnet/pulls)
[![codecov](https://codecov.io/gh/MagNet-DL/magnet/branch/master/graph/badge.svg)](https://codecov.io/gh/MagNet-DL/magnet)
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/16de4d80b50f40988f6886008a78593b)](https://www.codacy.com/app/svaisakh/magnet?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=MagNet-DL/magnet&amp;utm_campaign=Badge_Grade)
[![Project Stats](https://www.openhub.net/p/magnet/widgets/project_thin_badge.gif)](https://www.openhub.net/p/magnet)

MagNet is a high-level Deep Learning API, wrapped around PyTorch.

It was developed with the aim of reducing boilerplate code and writing Deep Learning architectures with more grace.

You should take a look at it if you need something that is:

- **Intelligent.** MagNet's ``Node``s are _self-aware_. Intelligent.
  They attach to each other like magnets (hence the name).
  This enables you to build complex architectures easily.
  
- **Simple.** MagNet's API enables a simplistic workflow.
  Get the data. Define the model. Train. Debug. Test. Deploy.
  
- **Extensible.** Written on top of the awesome PyTorch library,
  MagNet can also mix with lower-level details.
  
- **Ready.** The ``Trainer`` can accommodate **any training logic**, however complex.
  This means that you can do almost any experiment / research with
  all the features that MagNet has to offer.

<hr>

# Getting started: 30 seconds to MagNetize
The core idea of MagNet is the ``Node``.
Nodes are PyTorch modules that can change their properties dynamically based on the computational graph.

This way, they attach to each other... like magnets!

Here, we define a simple 3-layer CNN.
Note that you only need to specify the bare essentials.
No need to specify the strides, paddings, input sizes, or even flatten the output before feeding it to the final layer.

```python
model = nn.Sequential(mn.Conv(32), *mn.Conv() * 2, mn.Linear(10, act=None))

summarize(model, x=torch.randn(1, 1, 28, 28))

"""
+----------+------------+----------------------+
|   Node   |   Shape    | Trainable Parameters |
+----------+------------+----------------------+
|  input   | 1, 28, 28  |          0           |
+----------+------------+----------------------+
|   Conv   | 32, 14, 14 |         320          |
+----------+------------+----------------------+
|   Conv   |  64, 7, 7  |        18,496        |
+----------+------------+----------------------+
|   Conv   | 128, 4, 4  |        73,856        |
+----------+------------+----------------------+
|  Linear  |     10     |        20,490        |
+----------+------------+----------------------+
Total Trainable Parameters: 113,162
"""
```

Now, we'll get the dataset in a ``Data`` container.
``Data`` is a class that abstracts away the different sets (training, validation, test) and provides easy access to loaders.

```python
data = Data.get('mnist')
```

Next, we'll create a ``Trainer``.
MagNet's ``Trainer`` class abstracts away the training process and allows you to add features through ``callbacks``.

You can _implement your own training logic_.
Here, we'll just use the built-in ``SupervisedTrainer``.

```python
trainer = SupervisedTrainer(model)

# Tracks the loss, metrics and adds a nice progress-bar
monitor_callback = callbacks.Monitor()

# Train the model for one epoch
trainer.train(data(batch_size=64, shuffle=True), callbacks=[monitor_callback])
```

See the progress

```python
monitor_callback
```

<div align="center">
  <img src="assets/monitor-plot.png" alt="Monitor Plot" width="600"/>
</div>

For a more thorough introduction, check out:

[Quickstart - Training an AI to Recognize Handwritten Digits](Tutorials/MNIST-Quickstart/MNIST-Quickstart.ipynb)

# Installation

Clone the MagNet repository by running

``git clone https://github.com/svaisakh/magnet.git``

Create the conda environment that has all the needed packages and dependencies.

``
cd magnet && conda env update
``

Add the directory to the ```$PYTHONPATH``` variable by adding the following line to your ```.bashrc```

``export PYTHONPATH=$PYTHONPATH:~/magnet``

Point to a directory to store datasets by default.

``export MAGNET_DATAPATH="<PATH_TO_DIRECTORY>"``

Update by running

``source ~/.bashrc``

Anytime you use MagNet, activate the conda environment

``conda activate magnet``

<hr>

# Nodes - Models that build themselves!

Nodes are PyTorch modules that are _self-aware_!

Nodes can react to what's going on and dynamically change based on the input.

You don't have to worry about specifying the dimensionality of the input,
reshaping or having to work out whether to use Conv1D, Conv2D or Conv3D.

Here's a simple Deep Neural Network with two hidden layers:

```python
dnn = nn.Sequential(mn.Linear(50), mn.Linear(10, act=None))
```

You don't need to specify anything except the bare essentials.

```python
# Intelligent reshaping between nodes
conv = mn.Conv(32)
linear = mn.Linear(10)

x = torch.randn(1, 1, 28, 28)
y = conv(x)
print(y.shape) # Output: torch.Size([1, 32, 14, 14])

z = linear(y)
print(z.shape) # Output: torch.Size([1, 10])

# Din't need to flatten input to Linear layer
```

MagNet's Nodes are built just in time, according to the computational graph.

## Witty Features

Create multiple copies of the node

```python
mn.Linear(10) * 4
```
Create multiple copies with different hidden sizes

```python
mn.Linear() * (50, 30, 10)
mn.Conv() * (50, 30, 10)
```

Built-in activation functions and batch normalization

```python
model = nn.Sequential(mn.Conv(32), mn.Conv(64, act='tanh', bn=True), mn.Conv(128, act='lrelu'),
                      mn.Conv(10, act=None))
```

## Examples

DCGAN:

```python
conv = lambda *args, **kwargs: mn.Conv(*args, k=5, p='same', act='lrelu', **kwargs)

discriminator = nn.Sequential(conv(32), *conv(bn=True) * (64, 128, 256), mn.Linear(act='sigmoid'))

conv = lambda *args, p='same', bn=True, **kwargs: mn.Conv(*args, k=5, p=p, bn=bn, **kwargs)

generator = nn.Sequential(mn.Linear((256, 7, 7)), *conv(p='double') * 2, conv(32),
                          conv(1, bn=False, act='tanh'))
```

State of the art, 34-layer ResNet:

```python
class ResBlock(mn.Node):
    def __init__(self, c=None, p='same'):
        super().__init__(c)
    
    def build(self, x):
        # Tell the ResBlock how it should build itself   
        c = self._args['c']
        p = 'half' if c is None and self._args['p'] != 'same' else 'same'
        self.convs = nn.Sequential(mn.Conv(c, p=p), mn.Conv(c, p='same', act=None))
        
        super().build(x)
        
    def forward(self, x):
        res = self.convs(x)
        # If downsampling, pad using zeros
        if x.shape[-1] != res.shape[-1]:
            x = F.avg_pool2d(x, 2, 2, x.shape[2] % 2)
            x = torch.cat([x, torch.zeros(x.shape[0], res.shape[1] - x.shape[1],
                                          x.shape[2], x.shape[3])], dim=1)
        
        return F.relu(res + x)
        
res_layer = lambda n, c=None: nn.Sequential(ResBlock(c, p='half'),
                                            *[ResBlock(c) for _ in range(n - 1)])

resnet34 = nn.Sequential(mn.Conv(64, k=7), nn.MaxPool2d(3, 2, 1), 
                         res_layer(3, 64), res_layer(4), res_layer(6), res_layer(3),
                         nn.AvgPool2d(7), mn.Linear(1000, act=None)).eval()
```

<hr>

# Painless Training

MagNet's helps you train your models simply, effortlessly.

```python
# Train a classifier
trainer = SupervisedTrainer(model)
trainer.train(data(batch_size=16, shuffle=True))
```

##### Feature Filled Callbacks

Additional bells and whistles can be added using _callbacks_.

```python
# This callback monitors the training and logs the losses and metrics.
# Also displays a nice progress bar
monitor = callbacks.Monitor()

# This callback tracks the metrics on a validation set
validate = callbacks.Validate(data(batch_size=16, mode='val'),
                              SupervisedTrainer.validate)

# This callback saves and loads all models, optimizers, schedulers etc. periodically
checkpoint = callbacks.Checkpoint(save_path)

# This is some goofy callback defined by you. I don't know what it does exactly
i_am_kewl = MyGeniusCallback()

# Train again
trainer.train(data(batch_size=16, shuffle=True),
              callbacks=[validate, monitor, checkpoint, i_am_kewl])
```

You don't need to write training loops, logging or serialization code.

## Bring your own logic

MagNet is a general purpose framework.

Which means you can **use any training logic** and plug it right into the trainer.

```python
class MyTrainer(Trainer):
    def optimize(self):
        # Override this method with my own crazy logic
        generator, discriminator = self.models
        optimizer_generator, optimizer_discriminator = self.optimizers
        
        x = next(self.dataloader)
        z = torch.randn(x.shape[0], 32)
        
        x_fake = generator(z)
        
        p_x = discriminator(x)
        
        loss_x = some_loss_fn(p_x)
        loss_x.backward()
        optimizer_discriminator(loss_x)
        
        p_x_fake = discriminator(x_fake)
        
        # Some wierd stuff here
        # ...
        p_x_delta_2_nice_place = discriminator(generator(x_really_real))
        # ...
        # Some out this world shit here
        # ...
        and_that_is_how_you_invest_in_bitcoin = F.cross_entropy(z_strawberry_hat)
        # ...
        
        return massive_blood_loss
        """
```

And then, you can use the trainer like always in all it's might and glory.

```python
trainer = MyTrainer(models=[generator, discriminator],
                    optimizers=[nn.Adam(generator.parameters()),
                                nn.Adam(discriminator.parameters())])

trainer.train(data(batch_size=64, shuffle=True), epochs=13.8e9, callbacks=callbacks)
```

<hr>

# Debugging Tools

#### Catch breaks in computational graph

```python
# Suppose this is the loss function
def reconstruction_loss(x_gen, y):
  return torch.tensor([F.cross_entropy(input_, target)
                       for input_, target in zip(x_gen, y)]).mean()
    
# The check_flow() method can trace any breaks in gradient flow.
mdb.check_flow(trainer, data)
# Raises RuntimeError with a list of broken parameteres

# Aha! The loss function had created a new Tensor
# which did not have the gradient history
def reconstruction_loss(x_gen, y):
  return torch.stack([F.cross_entropy(input_, target)
                      for input_, target in zip(x_gen, y)]).mean()

mdb.check_flow(trainer, data)
# No breaks. No errors. Safe to move on.
```

#### Overfit a small sample

If you can't overfit on a small sample, the model probably cannot model the complexity of the data.

```python
model = mn.Linear(10)
# ...
# The overfit() function trains the model on various sample sizes and batch sizes.
mdb.overfit(trainer, data, batch_size=64)
```

<div align="center">
  <img src="docs/source/_static/img/overfit-fail.png" alt="Failed Overfitting" width="600"/>
  Failed Overfitting
</div>

```python
# Oops! Looks like there was something wrong.
# Loss does not considerable decrease for samples sizes >= 4.
# Of course, the activation was 'relu'.
model = mn.Linear(10, act=None)
# ...
mdb.overfit(trainer, data, batch_size=64)
```
<div align="center">
  <img src="docs/source/_static/img/overfit-pass.png" alt="Good Overfitting" width="600"/>
  Good Overfitting
</div>

#### Watch the model evolve

Sometimes, it is helpful to understand exactly how the model evolves over time.

When does the model learn to attend, to decode, to summarize, to track, to colorize?

The ``Babysitter`` callback constantly monitors the model and logs the mean relative gradients for each parameter. This will tell you how the model is learning as training progresses.

```python
# Just add the callback into the mix
trainer.train(dataloader, epochs, callbacks=[*my_existing_callbacks, Babysitter()])
```

<hr>

# Automatic Acceleration

**MagNet's codebase is device-agnostic.**

All data, models, tensors created by MagNet are automatically run on the faster hardware.

No need to shuttle between ``.to(device)`` and ``.cpu()`` calls.

```python
# When run on CPU
import magnet as mag
# Prints: Running your code on a CPU

"""
Your code here
"""
```

```python
# When run on GPU
import magnet as mag
# Prints: Accelerating your code on an NVIDIA 1080Ti GPU.

"""
Same code here
"""
```

<hr>

## Contributors Needed

I'm actively looking for contributors who can help take this forward.

Lots of Node building, Data Loading, Trainer Watching and Bug Squashing to be done.

Join us on Gitter. [![Join the chat at https://gitter.im/MagNet-DL/Lobby](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/MagNet-DL/Lobby/?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)


<div align="center">
  Handcrafted with ❤ by Vaisakh
 </div>

 <br>
 <hr>
 <sub>Created my free logo at LogoMakr.com</sub>



================================================
FILE: Tutorials/MNIST-Quickstart/MNIST-Quickstart.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training an AI to Recognize Handwritten Digits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accelerating your code on a shiney new Tesla P100-PCIE-16GB .\n"
     ]
    }
   ],
   "source": [
    "import magnet.nodes as mn\n",
    "\n",
    "from magnet.data import Data\n",
    "from magnet.training import SupervisedTrainer, callbacks\n",
    "from magnet.utils import summarize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mnist_quickstart import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the MNIST Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MagNet's Data class is an abstraction over the training sets and uses MagNet's DataLoaders, Sampler and collate functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the MNIST Dataset using the simple get() method\n",
    "data = Data.get('mnist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is what the digits look like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = next(data(batch_size=64))\n",
    "show_images(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Linear Node"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a simple Linear classifier using a Linear ``Node``.\n",
    "\n",
    "Nodes are at MagNet's heart.\n",
    "<br><br>\n",
    "They are _self-aware_ modules.\n",
    "<br><br>\n",
    "They react to the computational graph and change their properties accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that the activation has to be explicitly disabled.\n",
    "# The default is ReLU.\n",
    "model = mn.Linear(10, act=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model\n",
    "# The model is not yet built.\n",
    "# Makes sense since we don't know the input dimensionality yet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MagNet automatically builds Nodes just in time.\n",
    "\n",
    "Let's summarize the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+-----------+----------------------+\n",
      "|  Node  |   Shape   | Trainable Parameters |\n",
      "+--------+-----------+----------------------+\n",
      "| input  | 1, 28, 28 |          0           |\n",
      "+--------+-----------+----------------------+\n",
      "| Linear |    10     |        7,850         |\n",
      "+--------+-----------+----------------------+\n",
      "Total Trainable Parameters: 7,850\n"
     ]
    }
   ],
   "source": [
    "summarize(model, x) # x is the input to the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will ask the model to sort digits into respective buckets.\n",
    "\n",
    "The first column represents $0$, the second $1$ and so on...\n",
    "\n",
    "All the digits in the first column are what the model thinks is $0$.\n",
    "\n",
    "The correct results are in black and the wrong ones are in white."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1fOzYMcu6IWisa35a8/TaDFxv199p3dh3/vz5YVmPVt10E+GnnnrKcnKqYrHy448/Wq5Zs6ZlrXXr5DrdgFPfk/R5p3Ws7NmzW/78888tz5gxw3LdunUt6xTPWE71CoVWmjS/+eabIR1H33+0AhUM/Z3W6nrFihUtR7Pmlxy6qbg+B0L9niB0o0aNstykSRPL+llEn2sPPfRQwOM8+uijAfPevXst62dUnd4ay0nQXJkCAAAAAB84mQIAAAAAH+Km5nfjjTdabtOmjWXdGHX06NERXYNO89OpMIkyWUWnd+kGiQcOHLCsdRKd2DVz5kzLRYsWDeu6dAM6r/qFbtoWz4Kp42idTzeuHTlypOXNmzcHPKZu4qgbHGtd0Evbtm2TvE+k6WV8L1ot0/t7VbJ++OEHy1ofWLlyZZKPpdOAdLPP1ECn83377beWy5cvH9Jx5syZE/B2ndJ0yy23hLi6+KEVWq+NyVevXm15+PDhAe+jU8h0AqLWCJVOsNKKbrimsUXCpk2bLDdv3tyyVvu05qnvK8WKFQt4TK3/adaaqk7w0+qu1v86depkOZhJqCmJfv9D9euvv1rWmp9ueP7aa69Z1p9LvNFNjW+44QbLWjeNBf2TBqWfexOxiqibteuk0kqVKlnWGq9+5gxV7ty5LeukxnjBlSkAAAAA8IGTKQAAAADwIW5qfsprupnWpLxoDWH79u2WtXql09CUTrRLRFpN/PLLLwPeRyt3WlfRKTixoJs+xjOt2+kEJa1ZPPHEE0keRydWNmjQwHL37t0t62XteKYTNHv16hXwPjlz5rSs1bs777wzyeNrZUCnd2n1xMtVV12V5H1Sg1CrfVq30kl9Sjf3Tul0umGfPn2SvL/+TuikuwULFgTM+voRzzU/rd0vXbo04H20budV7QtGpkyZLGtN6o033rD8zjvvWB4wYIBlfQ0uU6aM7zWkBrqxuf7e64Tlyy+/3HIw9ep4EMlqn04f3rNnT8D76O+KPmd1up1+1tXPZvFMq336e6Z/rqC/uxdffHHA4+jE7AceeMByq1atLCfKVE7nuDIFAAAAAL5wMgUAAAAAPsRlzS85dBNTzSNGjLCs9al9+/ZZ1vpGokzw081f33333STvX79+fcs1atSIyJr+l07/0SlXXveJZ1obK1mypOVQpynpz01/Jokobdr//jcZnQCldPO96tWrR3xN52h9AP9u3Lhxlr0q1Tohq2XLlpFeUsK68MILLWvlxcvQoUMta9U3UWglzGsiYrjUqlXLstb8dENvrSBS8/t3WqNW+p6WnGmBiWbZsmWWJ06caPnTTz+1vHPnTss6RTkYOs32uuuu87PEqNNJ1zpRV6t9efLksXzq1CnL+nqgn7e/+eYby/r7+vPPP1vWz0m6iXc84soUAAAAAPjAyRQAAAAA+JDian5KJwA9++yzljNmzGh5xowZlnViUzzTy8q6KeyJEycC3r948eKWg5lEFW6jRo2yrJfQVaNGjaK1nGQZOHCgZd1MNlSzZ8+2nOg1P60xeW1oGi5LliyJ6PHjjdZidSKU/h7pdFKddhjMNCut9jVu3NiyvpZonfWjjz6yrBMpkTy6cWoi0klkkZ5KduzYMcte73laz2rYsGFE15PovKbGapVNc0px/Phxy7rhrL7GnT592rL+2YhOPdSpnN99912Sj6tVt6pVqwb8Wv2MGg/0M6T+yYzWFHVD3ixZsljWKqDSCl+7du0sT5o0yfJDDz1k+bHHHgt12VHFlSkAAAAA8IGTKQAAAADwIcX1NIYNG2ZZNw/U6SKvvvqq5bvuuis6CwsjvSSqmxF7bZo2ZMgQy9HaEHLevHmWdRM2r0u+wUy8ihX9/r300kuWtQJw5ZVXWtZL0/3797d85MgRy1OmTAn7OhOdXvafM2eOZa3i6vcwpdLNxvX1ad26dQHvr9OPdBPIatWqWf7Pf/4T8GubN29uWSdqaqVF69Ja5UDozp49a/nMmTMBb49nWu3UKZ5r1qyx3K9fP8tez7vk0A1Tvaxfvz7sj5tSedUyv/766+guJErO/ZlE7dq17bYvv/wy4H2bNm1qWafS6XN88eLFAb/27rvvtlynTh3L+t4/depUy0WKFLGsr+kFCxYMePxo0j+BCdefw+ifduj0U63lav1Sp07qJtLxgitTAAAAAOADJ1MAAAAA4EOKqPnp5B6d2qfVCd2gVqdiJSKtQHnRySd33HFHJJdjdBO222+/3bJXtS9btmyWW7RoEbmF+bBjxw7LOsFP66KlS5e2rM8prfnptEWtbGmtYMCAAZZT6kaoWl0bPHiw5TFjxljetm1bSMfUmpRWjpTWKy655BLLM2fOtFyiRAnLkZ5EFgytKq9du9ayTni65ZZbLOtz6eDBg5anTZsWMAdDf0ZU+8JHXwv1Oev1GhlvdPqY1uW7dOliuVevXpYbNGhguUCBAmFZQ+/evZO8j76u4J/0d1qngarkTKuNZ+c239WJul6/f6tWrbI8evRoy1qJzpcvn2WtqOlnAq2xab3wxRdftKw1988++8yyTllNqerVq2d5woQJlrUG2bdvX8v6mSxecGUKAAAAAHzgZAoAAAAAfEjYmt/KlSst64Q1vVyr01QS/bK/Vnk2btwY8D758+e3rBWMSDg3EUc35A11cpNW4HQaXjxo3769Za+qlW5Qq5Nm1OHDhy3r1J9ErPiESiuR7733nuW333474P1D/T6E+j3UGmGZMmUsd+zY0bL+3sRqU9pzNRTnzv936WQpnSa1a9cuy1oP0fsrrT97fd90Yqg+h2vWrGk5ffr0gf8BHnRCldZkKleuHNJxEB9at25tWX+eWvN78MEHLXtNPVNbtmyxXLRoUcv6fr969erQF4vzaC1TN1jVDWhT6sRZr42eA1m0aJFlfT/QCt/TTz9tOZiN0rXy9+6771oeO3asZZ3Kmhpqfqp79+6WtY6v1XNqfgAAAACQQnAyBQAAAAA+JFTN79dff7WsdRPduFYnpXz88cfRWVgUaCVON4tVOjlJN90Mlx9//NHyk08+6Zw7f7PGYHTr1s2yTl5MFFod9ar2Ka28HD161LLWBXXyYaLTal/Xrl0tv/nmm2E5fq5cuSznzJnT8m+//eb7mLq594oVKyxrjTNam107d36NSWt4jzzySMD779271/Lnn3+e5PF1E8g9e/ZY1umIS5cuDfi4pUqVsuxVg/SqEeqmw+XLl7esU0BTq4cffjjWSwiZ/i5qNadDhw6WtbKqm7fr7+uyZcssV6hQwfL48eMt6+RU3UyWiuj/08qt/lzKlStn+aKLLrKs035V5syZLeuG81506q1uaqufRZYvX57kcaIpqeeMVsCff/75gLdXrFgxLGvRKbden+tSG92Q97rrrrOslUuttodrSmhycWUKAAAAAHzgZAoAAAAAfEiomp9Op9Jqn2rUqFG0lhNV3377rWWvCVy6eVyoNm/ebFmnnun3c/369UEfT6s+PXv2tKyb1MWbffv2WZ4/f77lYsWKWdaJMsHQDViV1jKuuuqqkI4Zz3TqVriqfVrxGTFihGWtXXz44YeW9fmm1eBgTJo0ybJOdCxbtmxIx4kErYRoNVGfYzt37rSslR2dtKmTqPR5PnnyZMtaz9LHDWaSmlfNT6t9Q4YMSfI4iUxfT7du3WpZvzdaq7z55pujsq5o0M3Yn3rqqYD30e/DyZMnLWfIkCGkx0qpk1C9VKtWzbLWkPPkyWNZ67dZs2YN6fhaX9PPE5r1fVKnDOt74/79+0N63Gg6936in3O+++47yzVq1LCcKVOmsD++VuF1al8oUwZTC50qqzVS3czXa2pttHFlCgAAAAB84GQKAAAAAHyI+5pfs2bNLGvVLXv27Ja1mpOSJqMprTN4VRt0U8w///zTslZ/ZsyYYVmrkjr5UKeDBbPB5zk6XU03873zzjv/9evihW48qZtHXnvttZZ1EpoX/X57XbqP57pjqI4dO2b5oYceCssxdULP3LlzLefOnTvg/Rs2bGhZp8+NHj3ask5mCoZOWFu1apXlSFQ/VKFChSzrtCyvCVz6O1qlShXLurGhTklTt9xyS8Dct29fy/q6G+rURH3cq6++OqSvTWQTJ060rBNP9TX03ERU55wrUqRIVNYVL/T7EGq1LzUbMGCA5eLFiwe8j9ZHN27caFmnpOlrhj5XX3nlFcu7d++2XLJkScta9z9w4EDQa48X52qQ+jqr71t//fWXZX1v0ymJodJK4euvv25ZPyeptm3b+n6slEQrlxdffLFlnRhat25dy3peEG1cmQIAAAAAHziZAgAAAAAf4rLmpxNORo4caVmrAVpLS6nVPnXTTTdZ1tqNGjduXMAcSlUvWOcukevGu82bN7fsVcdKRFpl0E1OdcKR1rG0HqaTj+rXr2+5Y8eOYV9nrOh0Iq8pm8HQyZE6fS7U55JOsHriiScsa01DK3/6M1Ja9dR/Y6TplK6xY8da1smEl156qeVatWpZ1olHoU7y8nLjjTcGzDifTjDTiqUX3Vhdp3sCXr766ivLugm8bg7/008/WdZ6nteG3vpnEloLVHrMlEino3bp0sWybnSsEzq96M/nl19+sTxo0CDLXlXpu+66y7K+b+GfdOpkLKt9iitTAAAAAOADJ1MAAAAA4EPc1Py+//57y926dbOs09Cee+45y9WrV4/OwuKEbkqqk2eWLl0a0cdt3LixZd0k9dwkKt2gMdHlzZvXstalpk2bZlmfg+PHj7es1awff/wx4PHvuOOOsKwzJdFqn06Q0w1nk0OrbvXq1bN8/Phxy/ocjwf33HNPwIz4NmbMGMvB1F11IhUQjFatWiV5n7Rp//vfyN99992A99m0aZPlKVOmJH9hCS5dunSWT58+bVmn8Gl9Xycaam1P31d0qqLSinabNm0s63TPjBkzBrv0FE0//2vVXn8W8YIrUwAAAADgAydTAAAAAOBD3NT8tLqmm6c2bdrUcv/+/aO6pnhSuHBhy4sWLbKslUidJKO++eabgLdr7UwrfHoJtWrVqqEvNkHpRC3dwFRrfjoR6amnnrKslT+lU3m8Nl1NdPp9u+qqqyzr9Llbb73VslYbdEpluKp9wciSJYvl/PnzW9aNKoFQ6NRUzUCk6WTZZs2aWfaqlut0Od2YNrV64YUXLOtnKv0sOnXqVMteE5I1X3/99Za1Sq6fqUqUKJGcZad4+j3Xicn6nh0vuDIFAAAAAD5wMgUAAAAAPsS05qcT+Xbu3Gm5aNGilps0aRLVNSUCrVW9/vrrATOSp0WLFpbnzZtn+bPPPrOs07uUVt10A0CdspSSZMqUyfLChQsta30kR44cAe8fK7qpKtU++LV69WrL+rvutTn6a6+9FvE1pXQFCxa0nDNnTsu6aXJqqK5ptW/dunWW9bVWnTx50vKnn34auYUlIJ1KPGrUKMsTJkywfPDgQcv6HFT33nuvZd1MPTXTyZHFihWz7PV5SCuU+tlLXzt1g+N4kTI/3QEAAABAhHEyBQAAAAA+RL3mN2zYMMs6fU5rEToZrXz58tFZGCAuuOC/vxr9+vWzrLWSsWPHWr7yyistz54927JXHSClyp49e8CciJ5//nnLGTJkiOFKEK/0/ezPP/+0rO9nZcuWtawTQOHPJZdcYjl37tyWteanE1h1YmhKonV/ral5efTRRy0Hs6l0aqX1PKp6yTd06FDLX3/9tWWdoKjTpN977z3LuiFynz59LOtzP15wZQoAAAAAfOBkCgAAAAB8iErN76+//rLctm3bgPdp3769Zb3kB8SaTpccOXJkwIzEMn/+/FgvASmAbnA+YMAAyzrRc9KkSZb1tQSRo5uup1Tbt2+3rFNUtdaoVXTdABWIlpYtW1rW+m39+vUt58mTx7JOpX7ssccsZ8yYMVJLDAuuTAEAAACAD5xMAQAAAIAPUan56YZyhw4dsnzRRRdZfumllyxnzpw5GssCAMC3m2++2fLp06djuBKo48ePx3oJUZUaao1ITIULF7b866+/xnAlkcWVKQAAAADwgZMpAAAAAPAhKjW/66+/3jJVCAAAkFzr16+P9RIAgCtTAAAAAOAHJ1MAAAAA4ENUan4A4lunTp1ivYQUpWPHjrFeAoAoOHv2bKyXkKKetukuAAAgAElEQVSkSZMm1ksA/iFf/oKudoMmnv87V6YAAAAAwAdOpgAAAADAB2p+AABP27Zts3zJJZdYnjt3ruVbb701qmsCACBecGUKAAAAAHzgZAoAAAAAfKDmBwDwlDbtf/+bm07aYuoWAABcmQIAAAAAXziZAgAAAAAfOJkCAAAAAB84mQIAAAAAHziZAgAAAAAfmOaHfxg1apTldu3aWd65c6flM2fOOOeca9mypd3Wv3//KKwuZXvooYcsb9iwwXK/fv0s33bbbVFdE1KfY8eOWX777bctZ8+e3XLJkiWjuiYgtfvhhx8sjxw5MqSvnTdvnuU1a9ZY1qmcxYsXt9ypUyfLjz32WEiPlRIVK1bMctOmTS2/+uqrls+ePRvwaz/88EPL7777ruWFCxeGcYWIJa5MAQAAAIAPnEwBAAAAgA8pouZ36tSpgLfv27fP8pw5c8LyWBdffLHl66+/3nL69OnDcvxY+fLLLy2//vrrlnft2mVZ6wDnNvLcsWNHFFaXeuj3eNWqVZbffPNNy9T8EGkrV660PHDgQMv58+e3XKhQoaiuCUgtvvrqK8v6fvzTTz9Z9vrcEwzdiFudq+8759zff//t+/gpRenSpS3PnDnT8kUXXWRZq31eNT+tSep9qPmlHFyZAgAAAAAfOJkCAAAAAB/ivuanl0T1svOUKVMsN2vWLODXnj592vKRI0fCvrYiRYpY3rx5c9iPH2laHdPJPVo183Ku7qOTaRA5W7ZsifUSkMLpBL/OnTsHvE/jxo2jtZyQLFmyxPKCBQtC+lp9j/F67Ttw4IBl/d5oLcqrOuXF62u7du1q+eWXXw7pmEhcY8eOtdyoUSPLWue79NJLLd97772WixYtGvCY+rzSCXRe0qVLZzljxoxJ3j+l0991rfYlx+OPP275iSeeCMsxEXtcmQIAAAAAHziZAgAAAAAf4r7mp9PkKlWqZPnw4cOWDx48aDlXrlyWH374Yct79+61/Ndff1leu3at5TvuuMNypkyZLBcsWNBy1apVLWtNI1Fota9Lly6+j3Pue1i+fHm7rUSJEpbvuecey1mzZrXcpEkT348ZD7p37245d+7clmvVqmU5Z86cUV0T4Nevv/5q+fnnn7es08Ry5Mhh+dlnn43OwkJUvXp1y/p+EIxgan4q0FTTYL9WeX2tVq5LlSpluWbNmiEdP1GcPHnSsr4ff/LJJ5Z1uuTkyZMDHmfcuHGW69atG84lRsW0adMsa7VPJwgvXbrUsm6gjfDJkiWL5auvvjrgfXQ69EcffWT5s88+s/z0009b1s9ahw4dCss6E4W+htWuXdvyI488EvA+6sSJE5YnTJhg+ZVXXrH8xx9/hGWdycWVKQAAAADwgZMpAAAAAPAhLmt+OlVKL9dv27bNctmyZS3r1Jk+ffoE/NrURmsCb731lmWvKV1em80ldXz9mWieO3duwGNPnz7dsk6t0s3x4plWMRYtWmR5xowZlidNmhTSMbWyumnTpmSsDkja6tWrLWsdd+vWrZb1NfWll16yXLhw4Qivzh/9/dPXu5tvvtmyvvbcd999lvX1SSt2WjGJJq1L68bw8UAn6mo9T+nrX548eSx/++23Ae+v9Tx9D/HiNTVRj5+I7/1edaXBgwdbptoXeZkzZ7Z8+eWXW9aJulpX86oV9+jRw7JOAtS6Wkqi7xlt2rSx3KFDB8v6u6ubIH/++ecBj6l/5tO6dWvLq1atsvzGG29Y7tu3b4irDh+uTAEAAACAD5xMAQAAAIAPcVnz08129bK/XiJs1aqVZa1y5MuXL8KrSwxaddHsNXEqQ4YMll9//XXLWoHRS9XDhw//x/F04pJW15TWcbTOqVNw4mGzwKNHj1p+4IEHLP/0008B768TH0Ol0w4vu+wyy7/88ovvYwJq/PjxlnUjWK32KX0+v/baa5FbWJhUqVLFsr6WqLZt2wa8/dFHH7Wsm5ZGk7626nub1uTiwYsvvmh56NChMVxJ6qG1M0SXfr7RimuoE0M//vhjy/p54p133rF80003+VliTOnkw6+//tqy1iOnTp1quX///pa9ar9K/5xHa37ZsmWzHC+vkVyZAgAAAAAfOJkCAAAAAB/isuanG0nqdDO9nFejRg3L+fPnj87C4pxuyKtT+7yqfVdccYXlDz/80LJuxOvlySef/MdtXjU/ve+GDRssz5s3z7JuGBoP9RGtOuoGpurCCy+0rBPPIoHnOIKh9dQPPvjAsj4/vaawXXrppZZ1w9SUbuLEiZZD3Xg3GLqJt1dlsnHjxpa19htvtF4TaXnz5rWsdR+tP+/Zsydq64mE5cuXW9YNeVX9+vUtZ8qUKeB9tKb/1FNPWa5Tp45lrZLGQ5U+EeifOejr47XXXmtZf4b6pyhaHz73ZxHOnf+5QauDxYoVs7x582b/i46i5s2bW9bPjYMGDbKsn+2CoZ/thwwZYlk3r1b6+h1LXJkCAAAAAB84mQIAAAAAH+Ky5ue1gZdeEtUaWWquQOnkqi5duiR5f6326SSZYKp9SfHaeHfOnDmWq1evbnn9+vWW33//fcuxqvnpRsMjRoxI8v5aBSxevLjvx9VqltfGjVrXAJQ+f7QSNGXKlID3v//++y3feOONlhs0aGC5QIEC4VxiXNMpWt98801IX3vmzBnLWvEpUqSI5S+++MKybsibiDp27GhZ6/j6np0+fXrLhQoVCnicokWLWtbnnXrwwQcta8W/ZMmSlhO95jds2DDLXptE7969O6Rjtm/fPmCuWLGiZa1S6cbdep/USif1/fDDD5YrVapkuUWLFpb1M5h+jgpmCqq+dnvVr+NZ9+7dLevk7Xbt2iX5tdddd51lrQvqn4Vo7Vorl/onLboJfSxxZQoAAAAAfOBkCgAAAAB8iJuan16uHzNmTMD76CXo1Fzt00kvzzzzjOVTp05Z1kuiOulHp3pp3SeSChcubPmaa66xvG7duqg8/r/RqopWH7wuuWvd7j//+U+Sx9+/f79l/ZmoHj16WP7xxx8D3sdrs2CkTlu2bLHcrFkzy7NmzQp4/1tuucWybuCrU7327dtnecWKFZanT59uWWvC1apVs5zIm6VrTUd/p3WSrFaAlVb7tJLy+++/W9YqZd26dS0//fTTlnU6WDzTSWQfffSR5cmTJ1vW6YX33XdfWB730KFDlr3qcCmJ1hofe+yxkL52+/btlqdNm2ZZ31s0v/3225a1vqZ/BhCrzaxjQZ9fWl/Vmp/+7jZq1Miy13u80tdunfjnVfGPZzqBUqftaW1PK+P6PSxTpozlP//807LW/PS5qfVhnRYYL68HXJkCAAAAAB84mQIAAAAAH+Km5qeT6H777TfLmTNntqybf8XzxoaRppPvdu3aZdlrw0mdOqebQ8ZaJDbIDMaOHTssjx492rJXtU8vTderV89yr169At5fN5XUzU+TM61n8eLFvr8WKcOxY8cst2rVyrJW+7S2p5Wy/v37W9Z6be/evS3r1M1gKida69ApeLr5ZCLQ95J3333XslbLvCZGff3115Y7depkWasne/futTx48GDLutmkTl5MlIlq+n3zmsgXLlq32rp1a0QfK5q0wqfTzZ544gnLF1zg/2Pa6dOnLY8aNcryp59+almnTfbp08ey/k5369bN8m233eZ7PYlGJ0gnh/7e64RFnRyYiPRPRcaNG2e5Zs2aAe+v0xHbtGljWeuC+qcrBQsWtKwV1Hic4smVKQAAAADwgZMpAAAAAPAhpjU/ra3oxCgvOklIaytZsmQJeH+tquTOnduyXkbUCSHxTDd385ospapWrWq5adOmEVlTotIpVDqxKBg6eW/hwoVhWxOQlNmzZ1ueOnWqZa32vfHGG5Zbt25tWacrffDBB2FZj76+6kbbWglKZNmyZbNcuXLlgPfR20uVKmVZa+teEzp37txpuXbt2pYnTJhgOVEqf/BHJz1qDhedwqcT6DTrZymtm86fP99yrVq1LOtrj254nVLo77FuSutF/1xh48aNlm+//XbLKamaqvQ94Oabb/Z9HJ1e2aRJE8tal543b57v40cDV6YAAAAAwAdOpgAAAADAh5jW/HRSik7wU1oFvPvuu8PyuP369bOslxRffPFFy8mZoBMJWvH5/vvvk7z/jBkzLGtdJRZ0U0xdlypXrlxE13D8+HHLodbzdAJNMLR2pTWIQoUKWdYpOD179rTs9bPVaipSD33evvDCC5Z1I26t7em0Sa2w6n30tU3rz3fddZflHDlyBFyPTgL88ssvLSfihpPhphOsKlSoYFkrkG+99VbAr922bZtlfR/SiWr4dzpdEsHTKcn6GUunAGv1VKf56XNbq4OJRqt9kyZNshzMJrxKpySm1GpfJOj0ytKlS1vW6apr1qyJ6ppCxZUpAAAAAPCBkykAAAAA8CGmXbaVK1f6/lqtUuklWqWbrU6ePNmyTlzRy9TPPfec5Xir+emmhcFceo51tU/phDGdSqgbMnfs2DGia9CJWjNnzvR9nLx581q+5pprLGtdVDct9ZoCpvQStxedZPPKK68keX+kDF999ZXlLVu2WNZKnlb7lFZO9HnbsmVLyx06dEhyDfv27bP83nvvWdaa36pVq5I8TmqilV59/dM6s9cGqYsWLbJctGhRy/rzT210AquXp556KgorSdlKlChheeTIkZZ16rH+LHTj1TvvvNNy4cKFI7TCyFixYoXlYD5f6e9iom1SHi/0M7xuXq3ff93YPN5xZQoAAAAAfOBkCgAAAAB8iGmX7eqrr7YczOapJUuWtFy+fHnLwVxS1q/VzSx37NhhWacL6gbB8eD999+3rJvEqXvvvTday0nS8OHDLS9btsyyrl2nX2mOBN0QUeucvXr1Cnh/rXlqDa9Ro0aWr7/++nAu8V8lyubSiI6uXbsmeZ89e/ZY1qmAOvXs0KFDlr/44gvLOlVSN1TXaX76u1ynTp1glh11OsWwU6dOAe+jv9M6wSwSHnjgAcu6oeqSJUss6wRb3dhXK5aNGzeO1BLjxoIFCyx7bdip73k6mRLJlylTJsv6e6TTbTds2GB58ODBloN5fYq1Zs2aJXkfnbLZtGlTy1q/1Ylzjz/+uOVXX301uUtM0e655x7L+nl+7969lrXmHu+4MgUAAAAAPnAyBQAAAAA+xLTmlydPHss6FSY5Dh8+bHnKlCmWveobt99+u2WvjSoThU7iiQW93K0bTyr9mTdv3jziawqke/fulr2qPyoeKp+6yS+gk7MqVqwY8D5a89PqmFbEQq2LabVPp6W2bds2pONEi9ajvTYWbtiwYbSWcx6tNmvNRTcVP336tOXVq1dHZ2Fx4rvvvrOsU2BVwYIFLetG1olIN4vVXLt2bctXXnmlZf3ThUjTmrlOWNNNfvV2rdBdcsklEV5daM5V9LyqiL/88otlnRC5fPlyy/pnA0on1OHfeX0PX3vttSivJDy4MgUAAAAAPnAyBQAAAAA+xNfOtD7NnTvXsk48+vjjjwPeXycAdenSxbJOV0pEOkGvR48eUX9MrfadOHEi4P11auPNN98cuYX9C60qxarCt337dstnzpyxrBvW6dS1p59+OjoLQ1zR56rmv/76y/KsWbPC8ljZs2e3rJO8tIKmmyvWr18/LI8bScFUt3VaodaTovl+EMw6R48ebbl3796RXE5cGD9+fJL3iWbVLdIefvhhy8OGDQt4e9asWS1r/U+rUZHeRLZ06dKWW7RoEXANOiFTN/eOB5kzZ3bOnf87p6+tdevWtbx27dqAx9DnnddrNP5JN4/XevWpU6cs6wbmiYQrUwAAAADgAydTAAAAAOBDxGp+Wl3SSXpr1qyxPHDgQMs6lUfpRrq6cdynn35qWS8L6vQj9eabb1pu1aqVZb1sHs90g7MZM2YEvI9uwNmxY0fLL7/8suVw1drOTe7Ty/xJ3dc555588smwPH6imzlzpmXdUFVrAvfdd59l3UQYqUeNGjUs6+Q9rdeGSustuoG51om0ypPIOnToYNmrbvTCCy9YTpv2v/99MZrTRn/++eck75OoU64iSatuKYl+vmnZsqXlcePGWR4xYoTlsWPHWtb3jUcffdSybjJfuHDhsKzTa5Lojh07LOvnkmzZsoXlccNBK/W7du2yfK4G6JxzDz74oOUKFSpY1tq9HkenpuKf9LmZK1cuy7phfKJOLeXKFAAAAAD4wMkUAAAAAPgQse7QsmXLLOsmqXXq1LGsm6AtWLDA8uLFiwMe5+DBgwEfq3jx4pb1UqxOtLvooossJ+LUPp1spBt26saG6q233rL81VdfWdYJcVob8qo7rlu3zrLWC8/VELSWphvy6kScJk2aBDx2aqYbcwLBGDp0aMAMb4UKFbKsE8Z06qtW0rVSpfUkrUonx/z58y0vWbIk4GNpbSi12bZtm+W9e/fGcCWxpe/HWud76aWXLHfu3Nny559/btlr8998+fJZ1g23tcqmUzy1qudlypQpAW/ft2+f5Xit+akCBQpY1t/LUL399tvhWE6KVapUqYC3z5s3L8orCT+uTAEAAACAD5xMAQAAAIAPEav56YQk3ZDrww8/DJi96GVnnUxTq1Yty/fff7/lDBkyhL7YBKBT+HSj4fbt21v2qvx53T5x4kTLWi0JZeO5KlWqWO7atavlWG3ImyiCmaakm0sDCJ1uYDp48GDLZcuWtazVPn3t69SpU8CvfeCBByzfdNNNAR938+bNlnVq7YEDByxr/clr40993S9RokTAx0pJtPq/devWgPfRqZNa308NdMqmVv9PnDhhuU+fPpanTZtmWT8HaC1Qc3LoJMy2bdtajref0e7du51z50/QvPbaa30fT6eE6mbL+Kcbb7wx4O063ThRcWUKAAAAAHzgZAoAAAAAfIhYzU83Ig1GtWrVLOuGaLfddpvleLtcHCu33HKLZb3EPGfOHMt66f79998Py+OWK1fO8rkKmk650s3u8O90KlPPnj0ta/2oevXqUV0TkJLpFFfdkFfrdiNHjrS8fv16y3/88YdlrfwNGjTIcij16H9Ts2ZNy7r5ur7+plTBTFLTajubmf8//fMGfU/Wut2KFSss65S/c7W3/6VTBLVGqH960bBhQ8uPPfaYZa86Vzw4NyVSN0SvWrWqZf28pBuc79+/3/I777xjedSoUZZPnz4d3sWmAJUrVw6YN23aZFlr0YmKK1MAAAAA4AMnUwAAAADgQ8SukU+YMMGyTpHRy78FCxa0rBWMcNUlUgOd9qRTDTWzwWf80frA0aNHY7gSIHXTWtTjjz8eMOtkOa37eG0k7yVnzpyWs2TJYlkrfFoz0o3QU4NZs2YFvP2hhx6yXKZMmWgtJ+HpZ6lrrrkmYPaiddaUaNeuXZYnT54cMCP5Lr/8css6NVrPEQ4fPhzVNUUCV6YAAAAAwAdOpgAAAADAh4jV/HSzRM0AAMQj3Ux7/vz5Ae+jty9btiyk4+tm5uXLlw9xdSnf9ddfb/mHH36wrJMVdePj3LlzR2dhAHy59dZbA95+bqpiSsGVKQAAAADwgZMpAAAAAPCBHe8AAAiSbpquGcn3zDPPWB4/frzlUqVKWabaB8Q3neBXq1Ytyzq1Tzc7Tgm4MgUAAAAAPnAyBQAAAAA+UPMDAAAxV7JkSct//PFHDFcCwK8NGzZY1o3KUzKuTAEAAACAD5xMAQAAAIAP1PwAAAB8SJMmTayXAAR09uzZWC8hxdi6bbt7p++7nv87V6YAAAAAwAdOpgAAAADAB06mAAAAAMAHTqYAAAAAwAdOpgAAAADAB06mAAAAAMAHTqYAAAAAwAdOpgAAAADABzbtBQAAABAxBw8etPzRRx9ZnjRpkuU5c+ZYLlGihOUbb7zRcunSpS0/8cQTlnPnzh2+xYaIK1MAAAAA4AMnUwAAAADgAzU//MPXX39t+dZbb7WcNu0/z70HDx5s+ZlnnonougAAACLl2muvtbxo0SLLmTJlspwmTRrLZ8+eDen4b7/9tuXOnTtbPnbsWEjHSUQ//vij5RYtWljOkCGD5Xz58lnevHmz5XXr1lnW7/8PP/xgeezYsZYDfV6NJK5MAQAAAIAPnEwBAAAAgA/U/FIxnZrSs2dPywsWLLCsl0r10uo5s2fPtkzND4h/EyZMsPzZZ59ZXr58ueWLL77Ycvny5S137NjRcvr06SO1RACIiaJFi1rOmDGjZa3zHTp0yLJW1y655BLLl19+ecDjv/LKK5ZHjRplee3atT5XnDjGjRtnWat9Y8aMsVyrVi3L3377reUGDRpY3rp1q+Xx48dbfuCBByzXqVMnDCsOHlemAAAAAMAHTqYAAAAAwIe4qfnt2bPH8k8//RTwPtOnT7c8dOhQy2fOnLGstbThw4dbrlKliuWrr746eYtNYJ988onlRo0aWU4Nk2SA1GTlypWW27dvb/nLL7+0nDNnTsvlypWzvHHjRsuzZs2yPHXq1IDHKVSoUBhWHH5aV9SaslYXtVaidAPImjVrRmB1ABJRq1atLGt1rU+fPpZXrFhh+aGHHgp4nK5du1p+5JFHwrnEuJQ9e3bLhQsXtuz1Gqwb9er389lnn7Ws33+dlEjNDwAAAAASACdTAAAAAOBD3NT8mjRpYvnzzz9P8v5eG3Lp7TpdrmXLlpZ79+7tZ4kJRTfe1X+vTupLTrXv7rvvds4516FDB9/HABBeOhHq3O+oc84dPXrUsm4U2bBhQ8u6WeLp06ctDxgwwPJLL71keffu3ZbjteaXK1cuy7/++qtlfY/xer/RTTqLFCliOUeOHJY/+ugjy5dddlnyFotUTaeVVapUKcn7lylTxnLFihUt6/NWac11w4YNlrW6q68NBw8eTHINKdHMmTMt792717LWftevX2/577//tqyfh3755ZckH6tbt26+15mI3njjDcsvvPBCSF+bNWtWy9dcc41lrfmdOHHC/+KSiStTAAAAAOADJ1MAAAAA4EPUa3779++3rBP8dFKfV4UvOUaPHm1ZJ6vcdNNNYX+saNqxY4dl3YxTLzcfOXLE9/E7depk+a677rJctmxZ59z5G68lOq1G3nrrrUnev1q1apZvueUWy3opOzlr0OMDXrS+dscdd1jOmzevZZ3iWbVq1SSPmS5dOss6ueqtt97yvc5Y0E3Ft23bFvA+OoHrzz//tLx06VLL69atC/i1t99+u+X8+fNb/vDDDy17bd6ZiHRyrk7g0qqY1h0PHz5sWb8PixYtSvL4wXwOePTRRy1rrVXrq4miR48elvV3V2lVTzeRVV63K/0ZtW7d2rJWB9u1a2dZfxdSOq1YZsuWLeB9rrjiCsu6sewNN9xguUCBAgG/dteuXZa9XpNSKp3mpzk59Pmurx/RxpUpAAAAAPCBkykAAAAA8CEqNT+t9jVt2tTy5MmTo/HwzjnnDhw4YHn8+PGWE73mV69ePct6uTlUetl60KBBlm+77Tbfx0w0WrEL9f6atRqZHMHUNZA66STOunXrBrx95MiRlnVD3lDpa6dO+WvevLll3SA9njZF1ymDXhMHdSKfWrZsmeV9+/ZZ1ilnzZo1s6ybzVevXt3yiy++aFmnyiYi3cx52rRplrV+9ttvvwX8Wq316/2VVvu87qO02v7DDz9Y1vqlbg4az/R995VXXrGs1aWePXta1vcHnSj3448/Bjz+zTffbPn++++3rO/9WhPWyppX3S0l0s9Rhw4dspwnTx7LBQsWtJw+fXrLOu3U6/nbvXt3yzt37kzeYlMp3ZBev89ekyyjgStTAAAAAOADJ1MAAAAA4ENUan56eT851b777rvPsm7Iq5e7hw0bZlknBKqhQ4daHjhwoO/1xINNmzb5/trnn3/estZPLr300mStCeGhUwGTMyEQKU+jRo0s6wacX3zxheXkVPtOnjxpWaefauVv8eLFlnVj3yFDhvh+3HgSzPdPXyu13qbvT/369bN85513Wi5ZsmRylxiXtFYXau1G38uDqflt377d8pYtWyxrxfW1114LaQ2xohvm6mbT9957r+XkVMjnzZsX8DgdO3a0rFOAM2fO7PuxUgr9ExXdtFefUzodVauR+lzW4+j0UARPa9f6ZxVa3x4zZkw0l3QerkwBAAAAgA+cTAEAAACAD1Gp+emUI69Ntbxu180SixcvnuRjadVCJzAtWLAg4P3vueceyzNmzEjy+CnJkiVLLOsl1NRa89MqndYgvDbnVV4b7AYzIdCrujF//vwkvxaph04rmzRpkmXdVPfGG28My2NpFTqY57BOduvbt6/ljBkzhmU98erc5uX/m/W9Sqt9NWvWtKzvSV6TBuON/jxz5sxpWaeb6fPFa/PZcNEpkjpZcdWqVRF93EjT3yet42sVUCt5iIxbb73Vsn5G0sl++hlS6Ya8yalcpzZ6vtC1a1fLs2bNsnzq1CnLutl16dKlI7w6b1yZAgAAAAAfOJkCAAAAAB+iUvPT+pRuyueldu3alkOtCeilft2AzmsjyWDWE8+0NqKTDIOxcOFCy7rRX+PGjS3rpsa1atWyHMykpUQWrg1zvep/yqtSqNOXkDrpJrla6ylQoIDlNm3ahOWx1q9fb1mrg6po0aKW//zzT8s7duyw7FXZTk3KlCljuX379pZfeOEFyzp5qm3bttFZWDLly5fP8jXXXGNZf+aRrvapK6+8MmqPFU1alWzQoIHldu3aWZ46daplrUaFSt/LU/r7eqi0qrdmzRrLOrXPyx9//GF579694V1Ygtq1a5fliRMnWh43bpxl/SyqU2X1M5luIq3TZmMpsc8kAAAAACBGOJkCAAAAAB+iUvMLlV663717d8AcjCeffDJcS4pbWms8cuSI5Q8//DCk45w4ccLy4MGDLQ8aNMjyiy++aDl9+vTOufPrk/Xr1w/pMVMzrb4GMy0QqZNWHmbPnm1ZKxI6WSpUf//9t+XXX3/dsta2dPNOnTD5wAMPWP75558t6+sQG396++6772K9hN7nOuwAACAASURBVGQpX7685XBNkcQ/aQVKc7jcf//9lrVKpTXC1EonVubPnz+kr508eXK4l5PwtEr+3HPPBbxP5cqVLV911VWWR4wYYfnQoUOWly9fbrlIkSJhWacfXJkCAAAAAB+icmVK/wtmnz59krx/ly5dAmYv+l9RE32gRKj0D51HjRoVMP/++++WhwwZYvnzzz+3vHLlyoDH1+9t7969/3UtDRs2tKz/Ff222277168DEJj+VzcVriuYo0ePtjx+/HjLup/Q4sWLLesV7I0bNwY8pq5ZhwAh8Ydz6JXGnj17xnAl/2/RokUBb9f3vMOHD1vOmjVrxNeUCK677jrLl19+ecD7cGXq/M9IV1xxheVgBnXo1VrdCyk1K1y4sOWXX37Z8iWXXGL5mWeesZwuXTrLejVK91nUrFdZoy11nXkAAAAAQJhwMgUAAAAAPkSl5tekSRPLwdT8EF56CbVr166W9Q/OtQrRuXNnywMHDrQcyh4ULVq0sKyXyi+77LKgj5HS6NAJ3VuqY8eOAe8DaF1XJWcftG+//dZy9+7dLWuF6+OPP7as+wlphU9fM/SPs3VvOpw/aEJr6PyuJ1+WLFkC3r5z507LR48etUzN7//p7/2FF15oefXq1ZanTJkS8Gsvvvhiy7qXUiLT78E777xjuWLFipb1NVcrzvr5pmXLlpZLlixpWZ93+rqZ2hQrVsxyt27dQvraHDlyBLz94MGDyVlS2HBlCgAAAAB84GQKAAAAAHyISs1P6yN33XWX5Z9++snyrl27fB8/OROSEn26UnJkypQpYO7Xr59lnbiidZXPPvvMOXf+xJ8DBw5Y1v0EHn/8ccs6GQyAP6FUbp1z7uTJk5bHjBljWesqTZs2tXzfffcFPM6yZcsC3q7TBXUSYGqydOlSy1qP1sqk1pzz5csXnYWlYDNmzAh4u9azQt0fKDU7t3+kc+e/bzdv3tyy7r20adMmy4m2R6Luz/f8889b1n+rF93f84cffrBcu3Ztyzr9T/+8Rf/sBcHTPRfjEVemAAAAAMAHTqYAAAAAwIeo1Px0o65z9TDnnJs+fbrlBx980HJyNt4N9WtT2ya/obrooossP/zww//Iw4cPt9v00rf6+eefLU+bNs1yLDdYiyfVqlWL9RKQwunvqeYqVapY7t+/f5LHmThxYsDba9SokYzVJZZjx45Z1qml+n72119/WS5evLhlr43StUIdjOPHj1v+9NNPA+aUSif1/fbbbwHvo8/r1Ew35C1YsKBlrw1oS5QoYVmrqvo5Sb/nHTp0CN9io+TchsWDBg36x23/S/8U5Z577rGsv986UdJrUp/WThG8devWWV6xYoVlfc7qczmWOJMAAAAAAB84mQIAAAAAH6JS8/OiE6P0cl64PPnkk5ZDrVEkih07dljes2ePZd1oM5KPq4/v5cSJE5Z14l9qM3/+/IC3U/ODF91w+/vvv7f8+++/W86dO3fArx02bJjlV199NeAxx40bZzmYKXxer6N58+ZN8msTjU7nW7t2rWXd8PSXX34J+LW6wadWfxo0aGBZa35///13wOPoZqI6CVenrA4YMCDwPyCF0uesfm4oXbq05bp160Z1TbGmlXn9XFWnTh3LWkfT56dm/Qyh71dDhgyxrLX9/fv3J2fZUaM1vnOT9bw25J0zZ45l/dMFrfapsmXLWtZapSpTpkyIK44N/Tf++uuvlrUi6vVvDJfNmzdbbt++vWX9GWl1WqvWscSVKQAAAADwgZMpAAAAAPAhpjU/pZftwuWDDz6wfPXVVwe8j15S1MvXeuk2ns2ePduyVj8GDx5sWaedaBUiGNu3b7e8bds2y61bt3bOnV898qK1ourVq4f0+CnJ119/HeslIMG0bdvWsk5q0ypPrVq1LM+dO9eyVqB0KqdOsSpWrJjvtWltyOv1NdHohp2ffPKJ5X379oV0HJ02tXv37oBZqz+6sf3TTz8d8D5MqPt/WjXV77M+rwsUKBDVNcWCVvt0yuapU6csaw1Pf1+VVrtq1qxpecmSJWFZZzzQKtgNN9zgnDv/ubNq1SrLWqcNhk459tpMvVu3biEdM5oWLFhgecSIEZZ18qh+ngxXze/MmTOWly9fblmf1/pnJPq91Q2lixQpEpb1JBdXpgAAAADAB06mAAAAAMCHuKn5xYpOLPnmm28sJ0rNT+kkntq1a1vOlSuX5ZtuuimkY65fv96yfq9CoVUinQqTGrzxxhuWO3bsGPB2wEv58uUtt2nTxnKvXr0s9+3bN+DXXnrppZZnzJhhWTfmTI5MmTJZvuyyy8JyzFjbunWrZa9q3wUX/Pdt06vycsstt1i+8847A97n9ttvt5wtW7aQ1pna7Nq1y7JupJqa6bTJLl26WJ43b55lnch3+vTpgMfRemRKqvape++91/K5qXD6ZwsPPfRQksfImTOnZZ2SqBVCnTi3evVqy2+99VaIK46eZ555xrJWwy+++GLL6dOntzx16lTfj7Vw4ULLOs30iy++CHh//byqFdRnn33W9xoihStTAAAAAOADJ1MAAAAA4EOqqfnp5BCv2/Wy7NGjRy3rxonx5txkGuecK1mypGXdZFLrKtOmTQvp+HrZ2mtSzTm66adOIdPpVKkZm/MiVGnT/ve/d73zzjsB79OjRw/L9evXt9y5c2fLRYsW9b0GneipG3AHs8lvohk+fLhl3bxT6fvBI488EvE1wblRo0ZZ3rJli+Urr7wyYE4NtKbmVRuvV69eksdZsWJFuJaUUA4dOmR5586dlitXrmxZP1NptTpPnjyW9TPShAkTLL/22muWjx07FoYVR8Ydd9xhWWt+f/zxh2WdWBjKZ8L/pV+rdWn93dUKn2atGsYjrkwBAAAAgA+cTAEAAACAD6mm5qd1GS9a8XjppZcsR2JD4XDRtU2fPt2yTq/RiXzhphsB6sZ0uvllaqb1CzbtRbjo75o+xzJkyGA5Xbp0YXksnRp2+PBhy7o5qFaFdKPZRFOoUCHLDRo0iOFKoLym6+bNmzdgxv/LnTu3Zf0MpJv5pob3Jd10tkOHDs4556666iq7TaujWuPV11N15MgRy7q5rVb7NmzYkIwVR0/v3r0tay30wIEDlidNmmQ5X758lrU+rvfRirROj9WpiQ8++KDlSpUq+Vp7POHKFAAAAAD4wMkUAAAAAPiQamp+oWrSpInluXPnxnAlwdONM2fOnGlZp9AMGTIkyePoJpM6fWX27NmWX3nlFeecc61bt7bbdMJNaqa1K900UTfyZLIfkkMrO5kzZ47JGv7++2/LWpNJ5Jof4lNK2RQ62nRaseaBAwda9tqcOiXRSajnpsLdeOONdpu+ZnlV+z777DPLo0ePtjxx4sSwrTMWdEpe1apVA96nRo0aSR5HN/9NjbgyBQAAAAA+cDIFAAAAAD6k6Jpfzpw5Ld90002Wv/nmmyS/dseOHRFZU7QUK1bM8oABAwJmhI9ORNJqn96uNT8gUVSpUsVy9uzZLR88eDAWywGQTF4bUqdUumlu+/btY7gSpFRcmQIAAAAAHziZAgAAAAAfUnTNTzcX083Cgqn5AaHQOp/XJohM8EMi0s1QdbNPIFqGDh1qWTei7dy5cyyWAwDn4coUAAAAAPjAyRQAAAAA+JCia36qZcuWATMQSVrto+YHAKHbu3evZd2kukiRIrFYDgCchytTAAAAAOADJ1MAAAAA4EOqqfkBsTBv3rxYLwEAUgyd5nfxxRfHcCXxb8iQIQEzgPDiyhQAAAAA+MDJFAAAAAD4QM0PCIM33ngjYAYAJM8HH3wQ6yV4Onv2bKyXkKKkSZMm1ksAQsaVKQAAAADwgZMpAAAAAPCBkykAAAAA8IGTKQAAAADwgZMpAAAAAPCBaX74h99++83ynXfeaXn37t2W586d65xzrkKFCtFbGAAAABBHuDIFAAAAAD5wMgUAAAAAPlDzwz/Ur1/f8saNGwPe51z976+//orKmgAAAIB4w5UpAAAAAPCBkykAAAAA8IGaH5xzzm3bts3yzp07k7z/kSNHIrkcAABStP3791vesmWL5QULFlieNGmS5Ztvvtnyr7/+avns2bOW16xZY7lNmzaWn3jiiTCsOPW64447LH/55ZeW06RJY1l/Dnr7oUOHLI8dO9byyJEjLf/www9hW2uiadWqlWX9vi1fvtzy9ddfb7ldu3aWs2bNGuHVBYcrUwAAAADgAydTAAAAAOBDiq75bd++3fLs2bMtn9tw1jnnjh07Znnp0qWWCxUqZPmyyy6zfO+991q+/fbbLefJkycMK46devXqWdZNe+FNaxZvv/225TFjxiT5tfqcGj9+vGU2QQaA1KdLly6Wtdqnvvnmm4C3e9XLnnnmmYDH14m9HTp0CH2xqUTBggUtv/POO5b//PNPy40bN7ZcokQJy6VLl7Zct25dy/ozadSokeXnn3/e8tChQ5Oz7IQzYMAAy/r8VfrcX7lypWX9vJUjR44IrC44XJkCAAAAAB84mQIAAAAAH1JEzU8v82llauHChZYPHDhg2esyotq0aZPlRYsWWZ44caLl7777znKi1/yCqfalS5fOcsuWLSO5nLjVv39/y1qPOHz4sOVs2bJZ1jrfqVOnLK9evdpyrVq1LP/yyy8BjwMASHw//vij5bZt21r2qvCpiy66yHKWLFksnzlzxnLatP/9b+T6pw4bN260/MEHH1g+ePCg5RYtWlguVqxYkutJ6UqWLGm5VKlSlnPmzGn577//TvI4zZo1s1yzZk3Lw4cPtzxw4EDLOi1Zp/+lVD///LNlneCXPn16yw0bNrQ8Y8YMyzq9snLlypFaYpK4MgUAAAAAPnAyBQAAAAA+JFTNb9++fZY7d+5sedCgQZa1SqUyZsxoOVeuXJZvu+02y+PGjUtyDTr977333rPct2/fJL823nz11VeWdfNApZUB3VitR48ekVtYnHnttdcs679bn2vly5e3rJP9dOLjnj17LBctWtSybtZYtWpVy6+++qrlRx55xLI+l5E6aa108+bNlnWClFabJ0+ebFmnemn1R6srjz32mOXMmTNb1qqFVpv1NbVnz56WdYrq66+/bll/LyJp2LBhlv/666+A9/noo48sa/3Wi1el6uWXX7b80ksvWU70CjjCSyfBeVX7dHPehx9+2PJ9991nWd9DvGidr2nTppa3bt1quU+fPpb1vUU/YwXzpxEpkdbua9SoYTmYap/Sz40TJkywrH8G0LVrV8v6mSM11Pz0fUuzatKkiWX97DVkyBDL1PwAAAAAIMFwMgUAAAAAPsR9zU8vj1arVs3yihUrLOslaJ2UUq5cOcv333+/Za1kLV682HIwNT+lx0lEvXr1snz06NGA99FpQ3oZOjUZMWKEZb28XLt2bctaNfWq9eTLl8/yt99+a7lfv36WP/30U8sNGjSwrM/3bt26Bb12pBxaLXnqqacs6wafN910k2WtE+kG00pfO7Xqq9UJ1bt3b8s6WUw3t9TNz5X+vuh9wj01TKc7derUyfKuXbuS/Npg6kxa7dP7d+/e3fK8efMsT5kyxXKBAgWSPH68+emnnyzPnDkzLMf8/fffLWtdPlwyZMhgOdRKVqRpXUzfT3RSsNa6k+Ppp58OmG+99VbLWjXUirr+Tj/33HNhWU+i0T8t0d/pcKlQoULA27VyjX/n9ac90caVKQAAAADwgZMpAAAAAPAh7mt+77//vmWtOimtcugUFC/ff/+9Za0Fnj17NmBW99xzj+UnnngiyceKN7oBsU7aUjpp66233or4muKRbnaoOXv27Ja19hjqxC6tiI4aNcryl19+aVkrru+8845lan6pk04M1WqfWrBggeW6detaDqaiMnjw4IC36+vlF198YVl/L/744w/LWn3TrBteFy5cOMn1+KUVqWCqfV60Jr5s2bKAt+sGk0o3ZtX1tGvXzrK+l8SKvh9Ur17d8unTpy3r9EK9PVwiMSkuEafPhavaFwytnmq9UCf+6Xu/1t10s/pEVKlSJefc+b+7J06ciOhjFilSxLJOnNPPnyq1/knF/9Jps151vhIlSkRrOf+KK1MAAAAA4AMnUwAAAADgQ1zW/FauXGlZLzXrpfs6depY1s1kvcyaNcuyTknTCVbFixe3/OyzzwY8TiJW+06ePGlZN531qmzoZXydXJWa6IbG+rzTDUw1J4duZqoT2PRxtX6B1Gnq1KlJ3kcrJDolUidJeunYsWPA27UOozU/deGFF1rWTYF1U0rdcDSS3njjDcvjx4+33KxZM8vB1Azz5s1rWX8vvW5v0aKF5a+//tryd999Z1k387322mst62TEaLruuuss6/uBvmckh9ab9Ji6OXlyKp9ahdYNmrW2pVMWdTptapYjRw7L+nt59913W9bPTFq30p9j+vTpI7XEsNLn4bk/b9Cacq1atSyvWrXK9+OkS5fOstaBp02bZlm/96px48aWU8NGvcHInz+/Za/PojoZNJZS5ydlAAAAAEgmTqYAAAAAwIe4rPlpDW/Pnj2Wr776asu6SWrWrFkDHkcnoL366quWtT6lm1xOnz7dcrZs2UJddtzSyTBeVaEXXnjBcsWKFSO+pninU3a0zqfPx/nz51vWyWleDh8+bFnrJu+++65lfW4+88wzlvW5jJRt7969lrXmvGTJkoD318rzwIEDLefOnTss6wnmua21lAcffDAsj+uXbg6sOTm8pnXq7R9//LHlxx57zPLcuXMtr1+/3rL+3D755BPLhQoVSt5iQ6Cb22ol8tChQ5b136jvkVpT9JpEpzUwnZCrtdDkVMW0Zqk1PxXpSW2JTivAffr0saw1vy1btlg+evSoZa/KWrzZunWr5XOvTzq9VP+tujG5fj+86O+rfo5q06aN5ePHj1vW37NevXpZ1j9vSc30c5L+LHTzbf1MptOnY4krUwAAAADgAydTAAAAAOBD3NT8tMKiE/aUXjbNlStXwPtoHUovoao77rjD8oABAyynpGqfTqp57733kry/TpkK15S6RKbPL530o3Wm9u3bW77gggsC3l83O9QpZ0OHDg34uFrJbN68ueWU9NzEv9MaiG7Uq3RqX7iqfVqj0Ofe2rVrLWsNVTdI/7/27jtMqvps4/gYXimytAgIXiAoUoUsioKALCACAgYhEFpohhCagNKrSBNpFgKRokgTBKmR3hQQ6V1QCAFUqhRpEjrvH3l53tt4jjtzdmZ2Zvb7+Sf3NTkz+2OdnZlznXueX3JX+yKBTvn76KOPLFeoUMGy/i43bNhgeeXKlZa15h5OL774YqLHVKtWLQwr8Z9Op0XSZc6c2XKxYsUsa0Uz2t3dwDwhIcFu0/d1/QypE/maNWtmWadvLlq0yLL+zvbt22dZK716O/5Dq336njdw4EDH43XyrD819HDgyhQAAAAAeMDJFAAAAAB4EDE1P52IkjZtWst6eVmra5p1ktCmTZscH79y5cqW58+fbzlWK20nT560fOzYMcdjdIO+YE3+ikVDhgyxvHnzZsta2enRo4dlnTB15MgRyzqZRmsCkyZNsqwVVKRMuhGp1uq0ZqJVlGD97eqkz8mTJ1vWzRK1XtGpU6eg/NxYpBPSdONifc1A0unUQYTOunXrLK9du9ZyuDbiDgWdzFu1alXLusmzfv0hffr0lrXWrO/3AwYMsNy/f//gLTZGHDp0yLJOBtWJp1qbVDq1r3Xr1iFYXdJwZQoAAAAAPOBkCgAAAAA8iJian9aedJpR3759Lev0NK256CQQrcVoZSolVPtu375t2W0KiurSpYtl3UTR7TG1jrZr165fHKsTxnSzzKRsyhgJ9Lm5fPlyy/rcnDp1qmWdhKbPxzx58lhesmSJ5UKFCgVvsYh6HTp0sKyvhfq65bZReaC0tqqbWKry5ctb1kmVwVpDrPvzn/9sWf/u4c2pU6cs60bJSqupRYoUCfmaYoVuwhsfH2959+7dlufNm2c5mmt+bubOnWtZa35u1b4aNWpY3rp1a4hXF330KzkVK1a0fPToUcv6OVM/i+pnVJ0eG4mfKbkyBQAAAAAecDIFAAAAAB5ETM1PaZVEJ6DpBqgXLlywrFUq1aJFC8uxWu1TWtnRyVxKN5DU+o7SiSsjRoywPHbs2F/9+boBsm4+u3TpUsu5cuX61ceIdLlz57Y8YcIEywUKFLCsl6OVViKo9sGNbgCtU+GC5auvvrKs1Wmd2JU9e3bLujEqlanAaSUdSXfjxg3L+jlAFS1a1PIf/vCHkK8pVnz77beWdSNbpa8ZsaJs2bKWFy5c6HiMfv7UDbpjaUPjULh27ZplnTyrU6a1lqv/LaJpIiJXpgAAAADAA06mAAAAAMCDiKn56cSPF1980bJeWg2UXo6uV69eQPfVqptuuhjJdLqcm2effdZyqlSpLOsGnOPGjbOsG4gGYt++fZbnzJljuWPHjp4eLxLpRBmt/7l5//33LTdv3tzyE088EdR1Af9N/45189+dO3c6Hq914BIlSoRsXUCgxo8fn+gxjRo1CsNKwu/8+fOWdYKsTgDVypSqU6eO5U8++STR45VOW4sVOilVv06iXwnR2zdu3Gh51qxZlmvWrGl527ZtQV9ntHv44Yctr1mzxrJWcbXyF624MgUAAAAAHnAyBQAAAAAeJGvNT6t9Wj3Zv3+/4/G6eWqOHDksb9++3fF4PSZQ0VLtU1evXnW8XaeD6RS5f/7zn5YnT55s2a3ap1Nr3CYoOtGpd1q3zJkzp9+PEYm0vqjP36ZNm1pu3LixZa1Z6O9BN0N+5plngr1MwPfDDz9YvnjxomX9m37ggQcs6xRPJE2VKlUsb9myJRlXEr30s4K+nyjd7LNhw4YhX1NyOH36tOVXX33Vslb13N6bdTNaf45XerxW+PPly5fofSPN3XqZblKuz52BAwdafv311y2nTp3a8fG6d+/ueN+bN28mea2xJmPGjJZnzpxpWT/3fP7555b1uRbpk2S5MgUAAAAAHnAyBQAAAAAehL3md+DAAculSpWy7LYJr16uHzNmjOW0adNa7ty5s+X33nvPcjRegg6UbrS5a9cux2P08rRuAvr8889bdpuaqNVKva/bz3Kil2p12s0LL7zg92NEoh07dljW52y3bt0s66Vpndq3du1ay4cPH7ZMzQ+h8OGHH1rWSaX6vNXX42jfXDuSLF++3LL+vrNkyWK5YMGCYV1TtNHPDVpZVXnz5rUcq89frZaHU0JCguVQbCQeanFxcZbfeOMNn8/382rf7NmzLbttsq21Pf2b1hpvIF9/SOn071Xp5MhomiLJlSkAAAAA8ICTKQAAAADwIGQ1v2vXrlnWGlPdunUt61Qp1bZtW8u9evWynClTJsfjH3vsMcfbFy5c6N9io5hu4nf58mXHY65cuWK5YsWKAT2+TlHSHAi9nJ4hQwZPjxEptGKiU/h0oz+3qTOFCxe2rDU/IBQOHjxoWevPqn79+pY/+OCDkK8ppfjuu+8snz171vGYatWqWS5ZsmTI1xRtdJNUnYqqdFJt7969Q76mlEon0er7WLTQrxTUqFHD5/P9fDJiv379LPszzfjMmTPBXmJU0gmRBQoUsKwb8roZPHiw4+06VdbtM38k4soUAAAAAHjAyRQAAAAAeBCymp9Oj2rXrl2ix+sEtAEDBlh22yhNjR8/3vH2EiVKJHrfaFe8eHHLTz31lGWtVurlabfKiVdZs2Z1vL1Hjx6WtRZQvnz5oP78cDtx4oRjbtKkSaL3PXLkSCiWBBit+ur0U7dKtdbLtKqKpJkyZYplfS3WCX4vv/xyWNcUbd5++23L+lqr9CsBDRo0CPmaIom+r+vUM91g143b8Tqpb9myZZbj4+M9rzMS1KpV6xe39ezZ0/I333yT6GPkyZPHcqNGjSzr3/qtW7e8LjFqaD1SP/fo80Xpc23v3r2WP/74Y8fj9bmWO3duz+sMN65MAQAAAIAHnEwBAAAAgAchq/l99NFHlvVytBvdwEunxrlt7DV27FjLuqGf/iydYher9DLopk2bHI/RyYqjRo1yPEYnq7hVgvTS9t0NaDt16uT/YmPA+vXrLetzLUeOHI7H61Qv/e+j982ZM2cwl4gU7NKlS5Z1I8rr169b1o24dZofkkanx7755puOxzz66KOWmeD3S99++63lDRs2OB6jE/x0o+mUoGnTppb1vWjx4sWW/dk4Vqt9Wr2fMGGCZbcKfzRyqov99re/tZw+fXrLjz/+uGWtB2qlTatr7777ruPtsUr/jfrZ8qWXXrKsn//1uTlo0CDHx9T3IbfXzkjHlSkAAAAA8ICTKQAAAADwIGQ1P51a5M9lZ53Koxu76uVXdeHCBctXr161rNP/Bg4c6N9iY1yaNGksd+3a1fEYt9vxc7pJsj6vV61aZVmrfS1btnS8b+XKlS0/99xzQV8nUqYqVapYPnbsmOW0adNaXrBggWUqpklz6tQpy0OGDLGs70kqmqZTJQfdOFqfv0o3B01pE/y0ovvpp59a1s9PbtONldZN8+fPbzlfvnxJXWJE0te8p59+2ufz+XzDhg2z2zS70a+f1KxZ0/LOnTuDscSop1NLy5Qpk+jxDz/8sOWGDRtafuihh4K7sDDhyhQAAAAAeMDJFAAAAAB4ELKa34gRIyzrpq1q5cqVlnfs2GFZN57UrHQaWsaMGS1XrFjRcoUKFfxfMOCH5s2bW9Y6xfbt2y3rNCCt9ukEpV69eoVohUhp5s2bZ1k3RdQaqj7fqJoFj0772rJli+MxlSpVsqwbKeOXdu/e7Xi7TpabO3duuJYTNVq1amVZ32d06rHav39/yNcUSXTi3r333uvz+X7+mqg1aP0sqvXAiRMnWnaroKYE+re4bt06y3Xr1rWs9Wd9/evTp4/lIkWKWL7//vuDvs5w48oUAAAAAHjAyRQAAAAAeBCymp9O3Bk6dGiix69YscLynDlzLLtNRdKaX8+ePS0XKlQooHUCgdBpFhLISAAAFwVJREFUSkuXLrWsU/t0M0XdGLB3796WdaNEIFC6Ca9b7UmneJYrV85ytmzZQrewFEA3Rt63b5/jMenSpbOsk8L43XsTFxdnWafP4T/i4+Mtjx492jGnZLq57ODBg3/2vwhMqlSpLJcuXdpySq4++nxcmQIAAAAATziZAgAAAAAPQlbzC5RuYqoZiFRaKdWpNkCoffnll5anT5/ueEzr1q0tUysNHq1O/fTTT4keoxUs/Dqtpqpz585ZXrt2reWEhISQrwkAEsOVKQAAAADwgJMpAAAAAPAgYmp+AAD/7Nmzx/H2fPnyWR4wYEC4lgPfzzfrfuGFF5JxJdFLN/XUKZUXL160/Kc//cny999/H56FAcCv4MoUAAAAAHjAyRQAAAAAeEDNDwCiTPv27R0zQk83ideMpCtWrJhlnYioG5536dIlrGsCgMRwZQoAAAAAPOBkCgAAAAA8oOYHAIhY/fv3T+4lxJR+/fol9xL80qpVK8cMAJGGK1MAAAAA4AEnUwAAAADgASdTAAAAAOABJ1MAAAAA4AEnUwAAAADgASdTAAAAAOABJ1MAAAAA4AEnUwAAAADgQYrctHfBggWWBw0aZHnr1q2Wr1y5YjldunThWRgAAAiJZcuWWd6+fbvlnj17JsdyAMQIrkwBAAAAgAecTAEAAACABzFd87tx44blli1bWp42bZrl27dvW86YMaPle+65J8SrCy2tMGzatCnR41u0aGE5derUIVkTAAChNmnSJMsdO3a0fO3aNcv6+YCaHwLx+uuvO95evnx5x9vXrFlj+fPPP3fMKcGtW7cs/+tf/7K8cuXKRO+bO3duywkJCZYzZcoUpNUlDVemAAAAAMADTqYAAAAAwIOYq/mtWLHCct++fS1v3rzZ8Xit840ePdpy2rRpQ7C64Dhx4oRlnU709ttvWz59+rTlU6dOJfqYY8aMsayXsOvWret1mQAAhIy+FzZo0MCyTua9evVqWNeE2PTZZ59ZrlChQkD31ePdqoCxWvnTap/WaUeMGGG5evXqlvXrNh9//LHjY5YqVcryhx9+aLlQoUJJW2wScGUKAAAAADzgZAoAAAAAPIiJmt+SJUssd+nSxfLXX3+d6H3feecdy02aNAnuwkJkxowZlrt27Zro8RkyZLD8m984nz8fP37csv5OqPn5Rzd/7tevn2WdFqmXuOvVq2e5ePHiIV4dUrr9+/dbnjVrluX333/fslaeq1WrZnns2LGOx6hatWpZ7t27t+USJUp4XDHw//Q9/o033rD83XffWT569GhAj/nqq68mfWGIaXfu3En0mP79+wf0mPr5wG3KXyy5ePGiZa32tWrVyvLAgQMt61dsevXqZblNmzaW169fb7lTp06WFy9eHIQVe8OVKQAAAADwgJMpAAAAAPAgamt+OsWudu3alq9fv25ZK23FihWzPG/ePMu5cuUK1RJDRquMWrtJkyaN5VdeecVyt27dLGfOnDnEq4ttJ0+etNyoUSPLOi3SrUo5fPhwy1q1qlKliuVhw4ZZjouLS9pikaLpa57W/HS6kpvx48dbdns+qwULFljesGGDZZ22BgTi+++/t9ysWTPLZ8+e9fyYqVKlsvzkk096fpxwmjZtmuWvvvrK8oULFywfPHjQ8b558uSxrBPlatSoYTlLlizBWGbM0Kl9Smt4FStW9Pz4Oi050KmA0ShdunSWy5UrZ1m/TqKfXVXRokUtL1q0yLI+f3XD35kzZ1quX7++xxV7w5UpAAAAAPCAkykAAAAA8CCqan6rV6+2/Mc//tGyVvv0Mr5WA3RqVazSSSlt27ZNxpXELt1Ebt26dZ4f59tvv7U8YcIEy1ojbNq0qWWdlgYoff7oVEmd0KlTJUNNNwzPli2b5aVLl1pmyh8So9VRt2pfkSJFLHfo0MGyblSv09MKFixoWTf5TS4HDhywPGDAAMtaA9darj/T5dxMnDjRsv79TZkyxXLhwoU9P36scKve6eS9YInVCX5Kp/PpZ3LdTNut5qd0M1+dYq2fjfyZ4B0qXJkCAAAAAA84mQIAAAAADyK+5qeX93XT08uXL1vOnj27Zb2UXb169RCvLrJoHeD8+fOWk2OCn26gOHr0aMt79+4N6HE+/fTToK3JK61Ovfnmm4keX6hQIctac9I66qZNmxzvq/9enVLzt7/9zbJeKkfKlD9/fstHjhyxHM46nxutIp07d85ypUqVLA8ZMsSybsYI3KXvW/qa+thjj1keN26c5UuXLllu0aKF42PqRqGRYOTIkZanT59u+b777rN88+ZNyy1btrScIUMGy02aNLGsX3XQOvnQoUMta0W9VKlSlhcuXGg5ISHBz39FbNHqXSim7elj6uRAt81/dfpftNNqrdb8AlW6dGnH23WqcrhxZQoAAAAAPOBkCgAAAAA8iMian0596t69u+U9e/Y4Hq8bqLlV+3RqzowZMyzrhqlaJYjkjexefPFFy//4xz8s60a9OnVONzsLduVPL9XqNKK///3vlrV+4UbrF7rRYCTQaVBuG5jqc2f+/PmW8+XLZ1mrqTqpz63K+NNPP1nu2LGjZf1vqM8FxDbdsFOnlYWz2qdVvVWrVgV0X30dePnlly1T84OTxo0bO2Y39erVs6yTf/W9vHLlykFaXXDo5tj6dQWteOv7wNNPPx3Q4xcoUMCy/tv79OljWaf56fuwbqpatWrVgH5uNNOpfVrJK1++fECPo/fVzxBu1UF9/KRsChzJtL6qOViOHTsW9Mf0F1emAAAAAMADTqYAAAAAwIOIqfn9+OOPlnVDXr3ErfSSuE70OXz4sOV27dpZXr9+vWWtmwwfPtzytm3bLEdyzU+riVrzUxs3brQ8depUy7Vr17acK1euRH+WTkfRquTgwYN9Pp/Pd+LECbvNrc6nl691M2H9d+gl3//5n+R/Wn7xxReW3WpUxYsXt7x8+XLL999/v+PxcXFxlufOnWv5m2++sfz73//e8sGDBy3r73bXrl2WqfmlHG7VHzc1a9a0/NRTT1nW55LWU3Vaqv5t6jSpM2fOWNaJafHx8ZavXbtmefbs2YmuE/BKXzv3799vWevYOulON+2NBGPGjLGs0/mKFSsW0p+rf9OZMmWyrF+r0Pci/WpEnTp1Qrq25Ka/G7d6nltVz586n04L1EphLE3tS4m4MgUAAAAAHnAyBQAAAAAeJH+f6v8sWbLEsluFRSf6vPvuu47H66aqOhXQzY0bNyxv2bLFcqTVAVStWrUs6yQ43QhWN+3VKX+zZs2yrBP3tM43adIky4cOHbKslb577rnH5/P5fLlz57bbdBKQVgR0k8VIrk9qjap58+aWtTLy4IMPWtaqnlu1zx9atSpXrpxl/d27TRFEbNMa0+7duwO6r27SqVO93OjkSa0c5c2b1zHrNL+sWbNa1iogNT+E0sSJEy3rJC99TX377bfDuqZAaO09nLRKX6NGDcu6mbZ+hrhy5Up4FhYldLNdf+iGvNT5kk6rkioUmyz7i09oAAAAAOABJ1MAAAAA4EHE1PyGDRvmeHvZsmUtjx492nLGjBkt6yXUCRMmOD6OVs10cpxW+3Tqj06sSZcu3a+uPdxy5sxpefHixZa1BqnTd9SXX35pWafRBerupn/6M/Pnz+/58SKB1hR1U1RVv359y8HaXPjIkSOWdVIfoNM6J0+enOjxWtkJ9Pn5zDPPBHS8VvuAcNHa+qhRoxyPCXSD1ZSsSJEilnUzef3d6ucJ/ZwU67RO5k+FLFbrfPoVj6NHj1o+e/as5bVr11o+fvy4Zf36SUJCgmX97Khfb/CHfo5VZcqUCehxgokrUwAAAADgASdTAAAAAOBBstb8dJrc119/7XjMyJEjLWu1Ty+/6lS1Rx55xLJO9qtevbplnWTzxBNPWN63b5+/S49I06ZNs6yXUP3ZBNlNgwYNLE+fPj0Jq4tMWrG7evWq4zF3K40+3883Ng3Wz9XJhzq9TVWqVMlyx44dg7IGRD7duNlN586dLbdp08ZymjRpQrIm4NfMnz/f8s6dOy37M1Xvzp07lu9OjP1vOlnO7RjdiBb+089b+nlIJ93qBGF974oVWs8LdDqc25S5aHHz5k3LWlnUr9jcvn3bctWqVS27VfWef/55yzqFW6dG6/TN9u3bW9apytu3b7c8btw4y/oZvnbt2o5rCAeuTAEAAACAB5xMAQAAAIAHYa/53bp1y/Jrr71mWTfPbd26teUnn3zS8rp16yzr5bxHH33U8qJFiyznypXLcQ16mTJVqlR+rz2a6MQV/d261SLczJw50/KePXss9+3b1+fz+XwVK1a027JlyxbwOpObTkj74YcfHI/Ry92h+Llu1T6drqbHU2GJbYcPH7bsNsGvU6dOlgcNGmQ5derUoVtYEGkdG9FJ63xaqdeJpNevXw/oMf2p+fnjrbfesjx16lTL+reiFaKUQD97Xb582fEYnW58+vRpy1r/CvS/aTTQal+/fv08P47WAqOx8terVy/LI0aMsJw+fXrLmzdvtly4cOFEH1P/pvV3q7+fQ4cOWdavMWjtVD+3ax44cKDl5Hz/48oUAAAAAHjAyRQAAAAAeBD2mp9uqnvu3DnLGTJksHy3Qubz+Xy/+c3/n+/pxnEXLlywrBNl3Kp9qkePHpa1ula6dGnL0VKXUbqB2pgxYyxrzU9/P1rN0Mv7H3zwgWWtA+zdu9dyw4YNfT6fz/fss8/abTrlJ23atIH/A5KBXoLWS8ehoNMWBwwYkOjxa9asCeVyEKGGDBliWes1Sl/DIuG16tq1a5bfeOONRI9v1qxZKJeDENGa3ODBgy1r9Us3ztQ6qm4Oq+8rJUuWDGgNumm93vf8+fOW9bVTNxx1q7fFqk8++cSyTkBbvXq158fUSty2bdss6+cw/SwVqbSS51bt0yqafqXhs88+c3ycaN8sWic2a7Vv69atlgsWLBjQY2pdN3PmzJZr1arleLwe85e//MXxGJ1Uq89B/Twa7mm2XJkCAAAAAA84mQIAAAAAD8JS89MKwDvvvON4TNmyZS3rZXx18eJFx9vr1q3reLvWthYvXmxZNyDTDXx1als0TvnTKSi7d+92POa9996zrBsZ363t+Xw+X7du3Sy/8MILlnXTtLu0LqCPp5uH6qbBkUYnOi1ZssSyViaTQqt9r7zyimWtr6ru3bsH5ecier3//vuWkzLRLJx0gptuzKgeeughy/p6g8im9aYNGzZY1jqOvq/oBuMZM2a0/NVXX1nWDTv90aFDB8vt2rWzrJN8tcKnr+W68XxKsGnTJstNmjSx7DaFT/8b6YbIbhXjM2fOWNZq/7JlyyzrZzXdVFwnNSc3reopt2qf0tv1qwKBbvIbaY4dO2ZZv+4RaLXPH/r8Gj9+vOUuXbo4Ht+qVSvLv/vd7yxrlVU3Bm/RooVlff3ImjWr5WB+HYUrUwAAAADgASdTAAAAAOBBWGp+ejnvwIEDjsdotcxNoUKFHG/XOp9uTKcbXupUEJ1S8vHHH1t+7rnnEl1DtNOqhdbyVI4cOSyvWLHCstYo7l66nzNnjt2mE5S0aqDTvfSSrFY0kkvevHkt65qDRaeWuVX7tJ4ZyZVIhEewNi4NNa0N6WRQpc95nZiVL1++0C0MSaZTvdavX285S5YsjsfoFC19n9DXttmzZ1s+efKk48/Vir/WXf2Z0hUXF2e5aNGijsfEKv1bHDp0qOPtOvVTvxqh7/ejRo2yrF+B0MmNjRs3tuz2lQyt/eo04WigX/dICp16qDmSJSQkWNa/70aNGlkOtBp36tQpywsWLLCsk711Il+6dOks6/TQnj17Wtb3Rf1KyY4dOyzrpuL6dY57773XstaBk4orUwAAAADgASdTAAAAAOBB2DftdaP1ATe6KZxO5OvatatlncKnE/wefPBByytXrrTsVh2MRv78DnWz2HXr1lnWaUy6sa8+puaxY8f6fL6f19J0atLVq1ct62TBiRMnWtZLuG4VuGihG0bq5Bg3Wn/RipRWK5AyRXK17+jRo5Z1MpfWJfQ1WCd5adUCkU1rXVqd1wqOvgdr/vHHHy2vXbvW8fH19b5AgQKWtQaum/zi12mlSbMqV66cZa1eaZ1LzZw503Kg9fP4+PiAjo8kOs0vpalWrZrlXr16WS5evLhl/RyeIUMGx8c5fvy4ZX0eXbhwwbJO1Xvrrbcs6wTpQL8K8vjjjzvmcIjuT7AAAAAAkEw4mQIAAAAADyKmUzRv3jzLunGc0rqYTuTQzeJUiRIlLOtEIp1qE0uGDx9uWat6Ot1HL7NqDVIv7+plXOU0ZUzrfP7QtfTo0cOybhwYjXbu3Gl5xowZjsfoZW3dTJFqHyKZTuZ67bXXLLtV+zp16mR5yJAhIV4dQmHfvn2WtZL3/fffO2Y3+tqWP39+y3369LGc0jbVDQW3DXbVqlWrEj1Gq7u1atVK0pqilW7m67Zpb6zSqdr6tQ7dVHfkyJGJPk62bNksa11Qa8I1a9a0rBu6RyuuTAEAAACAB5xMAQAAAIAHYekXaSVPq3e6UZdOmdPsD70c2b59e8vdu3e3HKvVPqWbqem/XTdi0yqgbqCmtQ7NSjdHjvbpe8H20ksvWdbf0yOPPGJ54cKFlgsWLGj5iy++sPzMM8+EaomIEv5s2nvp0iXL999/f0CPr9XcM2fOOB4zdepUy+PGjbOs1S63ap/bBr6IHjq9bfny5QHdN3fu3JZ1o03d+DPU9P1JJ465bfgb7cqUKWNZXw/Onj3reLx+Hpo0aZLlOnXqWNbPbbFIK3xa7atQoYLlaNx4Nyn076ZVq1aWW7RoYdntPWPRokWWS5UqZTmlbKDNJ2IAAAAA8ICTKQAAAADwIOw1P50A1bZtW8uzZ892vG/27Nkt68S5ypUrW9bLtTlz5kzaYmNQ6dKlLeukuS1btlju0qWLZbcJff7UjxKjE4Luu+8+T48RKbQecfr0act6qfynn36y3KZNG8fHOXDggOUPPvjActWqVYOxTEQZnXKk1VClEzd1M06t202ZMsWyVjO0qqcTPf2hlSmd/EW1L7bMmjUruZeQJFqj1g3VY1XevHktr1mzxvL27dsdj9eaX7169UK2rkimm/Pq5xn9nNOvXz/HHOjjRzudypkjRw7HY7QKmBJxZQoAAAAAPOBkCgAAAAA8CPtuobpxabRXCaKRTjPSyXEbN25MjuVEHa2MaI3q2rVrjsefOnXKslYBy5cvb/mvf/2rZbdL6Eg5evfubXn16tWWdTNzt+mbOnkvUDqdTyu4OoXt1VdftaybsAKIDEWKFHHM8I9W/tym/Cmt82nFMpZqfkgcV6YAAAAAwANOpgAAAADAg7DX/IBoFhcXZ7l48eKW161bl+h94+PjLTdt2tQxAyVLlrS8atUqyzrB9PLly54fX6erasX06aefthzODVYBIBLppGjg13BlCgAAAAA84GQKAAAAADyg5gcEQDev03re9OnTLevGxLph8vPPP2/5gQceCNUSEUO08nfhwgXLI0eOtPzvf/87oMfUzRXZ5BwAgKThyhQAAAAAeMDJFAAAAAB4QM0P8Ein+Z08eTIZV4KUpnPnzsm9BAAA4OPKFAAAAAB4wskUAAAAAHjAyRQAAAAAeMDJFAAAAAB44DqA4vqNGz6fz+c7eep02BYDIHncuHUnuZcQU747ejy5lxAzeG4GF89NRLJs2XMk9xJiBn/rwXP3XOjuudF/u+fOnTuO71Sbt+3yTZ4xJ3QrAwAAAIAo0KxhHV/JEvG/uN31ZOripcu+bw78y/fbLJl9qVPfG/IFAgAAAEAkuX79hu/cj+d9hQrk82XMEPeL/9/1ZAoAAAAA4I4BFAAAAADgASdTAAAAAOABJ1MAAAAA4AEnUwAAAADgwf8Cv0XDHvirmYIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "order(model, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this is a [supervised learning](https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms) problem, we will use the simple ``SupervisedTrainer`` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Track the accuracy of classification as well\n",
    "trainer = SupervisedTrainer(model, metrics='accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Callbacks breathe life into MagNet's training process.\n",
    "\n",
    "Callbacks allow you to add additional features to the trainer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This callback tracks metrics (loss, accuracy) on a held-out validation set\n",
    "validate_callback = callbacks.Validate(data(batch_size=64, mode='val'),\n",
    "                                       SupervisedTrainer.validate)\n",
    "\n",
    "# This callback logs the metrics and adds a nice progress bar\n",
    "monitor_callback = callbacks.Monitor()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train for an epoch with batch size $64$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=750), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "trainer.train(data(batch_size=64, shuffle=True),\n",
    "              callbacks=[validate_callback, monitor_callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at the Monitor callback to see how training went"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vaisakh/magnet/magnet/utils/statistical.py:122: RuntimeWarning: window_fraction (0.3) too low for order (3) and length (9) of data\n",
      "Returning raw data\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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