Add CNN class for handling MNIST dataset

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Description

This PR adds a new CNN class in the src/cnn.py file to handle the MNIST dataset. The CNN class is designed to process 28x28 grayscale images and includes two convolutional layers, ReLU activations, max pooling, and two fully connected layers. Additionally, a train_cnn function is implemented to train the CNN using the provided DataLoader.

Summary of Changes

• Created a new file src/cnn.py to contain the CNN class and training function.
• Imported necessary PyTorch modules in src/cnn.py.
• Defined a new class CNN in src/cnn.py that inherits from torch.nn.Module.
• Implemented the __init__ method in CNN to define the layers of the CNN.
• Implemented the forward method in CNN to perform the forward pass.
• Defined a train_cnn function in src/cnn.py to train the CNN using the provided DataLoader.
• Added docstrings and comments to the CNN class and train_cnn function.
• Imported the CNN class and train_cnn function in src/main.py.
• Created an instance of the CNN class in src/main.py.
• Called the train_cnn function in src/main.py, passing the CNN instance, DataLoader, and number of epochs.

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

Fixes #9.

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• Rollback changes to src/main.py
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