Add CNN class for handling MNIST dataset

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Description

This PR adds a new CNN class in the cnn.py file to handle the MNIST dataset. The CNN class is implemented using PyTorch and includes convolutional layers, pooling layers, and fully connected layers. Additionally, a train_cnn function is defined to train the CNN on the MNIST data.

Summary of Changes

• Created a new file src/cnn.py to contain the CNN class and the train_cnn function.
• Imported necessary PyTorch modules in cnn.py.
• Defined a new class CNN in cnn.py that inherits from torch.nn.Module.
• Implemented the __init__ method in the CNN class to define the layers of the CNN.
• Implemented the forward method in the CNN class to perform the forward pass of the CNN.
• Defined the train_cnn function in cnn.py to train the CNN on the MNIST data.
• Added comments and docstrings to explain the functionality of the CNN class and the train_cnn function.
• Imported the CNN class and the train_cnn function in src/main.py.
• Created an instance of the CNN class in main.py after loading and preprocessing the MNIST data.
• Called the train_cnn function with the CNN instance, DataLoader instance, and number of epochs in main.py.

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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