docs: Fix incorrect usage description of ctx.save_for_backward

[harshaljanjani]

→ Fixes #2797

Description

→ This PR corrects the misleading explanation of ctx.save_for_backward in the two_layer_net_custom_function.py tutorial. The original text incorrectly stated that arbitrary objects can be cached using ctx.save_for_backward, whereas only tensors are supported.
→ The updated docstring now accurately reflects PyTorch’s autograd API.

Checklist

• The issue that is being fixed is referred in the description (see above "Fixes #ISSUE_NUMBER")
• 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 @albanD @jbschlosser

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/tutorials/3377

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[harshaljanjani]

I’ve signed the Facebook CLA. I believe re-running the tests should reflect that. Thank you very much!

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[sekyondaMeta]

@jbschlosser or @albanD Does this look good?

cc: @svekars

[svekars]

LGTM

[albanD]

Sounds ok, you can also link https://docs.pytorch.org/docs/stable/notes/extending.html#extending-torch-autograd for further details.

[harshaljanjani]

@albanD @svekars @sekyondaMeta
The changes should be done. If there are any remaining nits, I'll be quick to address them!
Additionally, I've left comments on other issues tagged with docathon-2025. I believe a few are outdated/not reproducible anymore, and for others, I've requested clarification.
Please review them when time permits, thanks!