Add error logs to the training loop

[sweep-nightly]

Description

This PR adds error logs to the training loop in the PyTorch model. It enhances the code in src/main.py to log any errors that occur during the training process. The logs are written to a file named training.log and provide valuable information for debugging and improving the model's performance.

Summary of Changes

• Imported the logging module at the beginning of src/main.py.
• Configured the logging module to output messages to a file named training.log with the logging level set to ERROR.
• Added a try-except block in the training loop to catch any exceptions that may occur.
• Logged the exception message along with relevant information such as the current epoch, batch, and loss value.
• Formatted the log messages in a clear and consistent manner.
• Updated docstrings and comments to reflect the changes made to the code.

Fixes #1.

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• Rollback changes to src/main.py

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Apply Sweep Rules to your PR?

• Apply: All docstrings and comments should be up to date.
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• 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.