Improve LR scheduler documentation in transfer learning tutorial

[sammy-1904]

Description

This PR improves the documentation and comments related to the learning rate
scheduler (StepLR) in the transfer_learning_tutorial.py script.

Changes made:

• Added clarification about the recommended order: optimizer.step() followed by scheduler.step().
• Explained why StepLR is stepped once per epoch in this tutorial.
• Improved clarity of the backward/optimizer comment inside the training loop.
• Updated comments to align with current PyTorch best practices.

No functional changes were made. This is a documentation only improvement.

Checklist

• The issue being addressed is documentation clarity (no existing issue number)
• Only one change area is addressed in this PR
• No unnecessary changes are included
• PR follows PyTorch Tutorials contribution guidelines

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/tutorials/3681

• 📄 Preview Python docs built from this PR

Note: Links to docs will display an error until the docs builds have been completed.

This comment was automatically generated by Dr. CI and updates every 15 minutes.

[meta-cla]

Hi @sammy-1904!

Thank you for your pull request and welcome to our community.

Action Required

In order to merge any pull request (code, docs, etc.), we require contributors to sign our Contributor License Agreement, and we don't seem to have one on file for you.

Process

In order for us to review and merge your suggested changes, please sign at https://code.facebook.com/cla. If you are contributing on behalf of someone else (eg your employer), the individual CLA may not be sufficient and your employer may need to sign the corporate CLA.

Once the CLA is signed, our tooling will perform checks and validations. Afterwards, the pull request will be tagged with CLA signed. The tagging process may take up to 1 hour after signing. Please give it that time before contacting us about it.

If you have received this in error or have any questions, please contact us at cla@meta.com. Thanks!