Add OOV token handling to character-level RNN tutorial

[rice-e]

Improves char-rnn tutorial code quality

Description

This PR adds proper handling for Out-Of-Vocabulary (OOV) characters in the character-level RNN tutorial.

Problem:

• The current implementation doesn't properly handle characters not in the allowed set
• Using string.find() returns -1 for unknown characters, causing them to be treated as apostrophes (the last character in the allowed_characters string)
• This creates ambiguity between actual apostrophes in names (like O'Brien) and unknown characters

Solution:

• Added an underscore character "_" as a dedicated OOV token
• Modified letterToIndex() to explicitly handle unknown characters
• Added comments explaining the purpose of OOV handling
• Updated the comment about input nodes (57 → 58) to reflect the added character

This change follows best practices for NLP systems by explicitly handling unknown characters, improving both the model's accuracy and the tutorial's educational value.

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 @vmoens @nairbv @albanD @jbschlosser

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/tutorials/3284

• 📄 Preview Python docs built from this PR

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✅ No Failures

As of commit 3ae82b7 with merge base cb2e4ed ():
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