Add error logs to the training loop

[sweep-nightly]

Description

This PR adds error logs to the training loop in the PyTorch model. Error logs are important for debugging and monitoring the training process. The code has been modified to handle potential errors and log them using the logging module. Additionally, logs have been added to track the progress of each epoch.

Summary of Changes

• Imported the logging module at the start of the file.
• Configured the logging module to write logs to a file, with a level of logging.ERROR.
• Added try-except blocks in the training loop to handle potential errors.
• Logged error messages using logging.error() in the except blocks, including the epoch number and iteration number.
• Added logging.info() at the start and end of each epoch to log the progress of the training, including the epoch number and model metrics (if available).

Fixes #1.

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