Add CNN class for MNIST handling and import it to main.py

[sweep-nightly]

Description

This PR adds a new Convolutional Neural Network (CNN) class for handling the MNIST dataset. The CNN class is implemented in a new file named cnn.py and is imported into main.py for use. This change replaces the existing feed-forward neural network (Net class) with a CNN, potentially improving performance on image data.

Summary of Changes

• Created a new file src/cnn.py to contain the CNN class.
• Added import statements for necessary modules in cnn.py.
• Defined the CNN class in cnn.py, inheriting from nn.Module.
• Implemented the layers and forward pass in the CNN class.
• Added docstrings and comments to the CNN class and its methods.
• Formatted and indented the code in cnn.py according to the rules specified in sweep.yaml.
• Modified src/main.py to import the CNN class from cnn.py.
• Replaced the instantiation of the Net class with an instantiation of the CNN class in the training loop of main.py.
• Formatted and indented the code in main.py according to the rules specified in sweep.yaml.

Please review and merge this PR to incorporate the CNN class for improved handling of the MNIST dataset.

Fixes #9.

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Rollback Files For Sweep

• Rollback changes to src/main.py
• Rollback changes to src/cnn.py

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[sweep-nightly]

Apply Sweep Rules to your PR?

• Apply: All docstrings and comments should be up to date.
• Apply: Code should be properly formatted and indented.
• Apply: Variable and function names should be descriptive and follow a consistent naming convention.
• Apply: Imports should be organized and grouped together.
• Apply: There should be no unused imports or variables.
• Apply: Code should be properly commented and include docstrings for functions and classes.