fix: serialize non-standard FunctionResponse dicts in AnthropicLlm

[giulio-leone]

Summary

Fixes #4779

part_to_message_block() only handled function_response dicts containing "content" or "result" keys. Any other dict structure (e.g. SkillToolset returning {"skill_name": ..., "instructions": ...}) fell through to an empty string, causing Claude to never see the tool output.

Changes

• anthropic_llm.py: Added json.dumps() fallback after the existing "content" / "result" checks, so non-standard response dicts are serialized to JSON text instead of being silently dropped.
• test_anthropic_llm.py: Added regression test verifying that a SkillToolset-style response dict is round-tripped correctly through part_to_message_block().

Testing

• All 18 test_anthropic_llm.py tests pass (including the new one)
• Full unit test suite: 4623 passed, 0 regressions

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[rohityan]

Hi @giulio-leone , Thank you for your contribution! It appears you haven't yet signed the Contributor License Agreement (CLA). Please visit https://cla.developers.google.com/ to complete the signing process. Once the CLA is signed, we'll be able to proceed with the review of your PR. Thank you!

[giulio-leone]

Rebased onto latest main to resolve merge conflicts.

The upstream branch introduced improved json.dumps() serialization for standard result values (dict/list). I kept that improvement and preserved the fallback path for non-standard response dicts (e.g. SkillToolset returns) that was the purpose of this PR.

Both the upstream PDF document tests and this PR's non-standard response test are included.

[giulio-leone]

@rohityan Thanks for the heads-up! I'll get the Google CLA signed. Will follow up once it's done.

[giulio-leone]

I have signed the Google CLA. Could you please re-run the CLA check? Thank you!

[giulio-leone]

Hi @rohityan — the CLA is now signed and passing ✅ (it was a Co-authored-by trailer issue that has been resolved). Ready for review!