Resolving issue#2083

[zabboud]

Fixes #2083

Description

Added model.eval() in test loop and comments to explain what model.eval() does. Added additional comments before torch.no_grad() to explain the differences between model.eval() and torch.no_grad() and their uses.

Changes:

def train_loop(dataloader, model, loss_fn, optimizer):
    ....
    # Set the model to training mode - important for batch normalization and dropout layers
    # Unnecessary in this situation but added for best practices
    model.train()
    .....


def test_loop(dataloader, model, loss_fn):
    # Set the model to evaluation mode - important for batch normalization and dropout layers
    # Unnecessary in this situation but added for best practices
    model.eval()
       .....

    # Evaluating the model with torch.no_grad() ensures that no gradients are computed during test mode
    # also serves to reduce unnecessary gradient computations and memory usage for tensors with requires_grad=True
    with torch.no_grad():
             .....

Checklist

• The issue that is being fixed is referred in the description (see above "Fixes model.eval() #2083 ")
• Only one issue is addressed in this pull request
• Labels from the issue that this PR is fixing are added to this pull request
• No unnecessary issues are included into this pull request.

cc @suraj813

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