fix: chat dataset

[akoumpa]

What does this PR do ?

Changes

nemo_automodel/components/datasets/llm/chat_dataset.py (_normalize_tool_calls):

• Missing/empty id → auto-filled with f"call_{idx}".
• Missing/empty type → auto-filled with "function".
• Wrong-type values (e.g. id: 123) still raise — catches real data bugs.
• Normalized id/type are written back into the returned dict so downstream chat templates see them populated.

tests/unit_tests/datasets/llm/test_chat_dataset.py:

• The two parametrize cases that previously required missing id/type to raise now pass a wrong-type value instead (still expects raise).
• Added test_normalize_tool_calls_autofills_missing_id_and_type covering both missing-key and empty-string forms.

Both OpenAI-style data (with id) and customizer-style data (without) now flow through the same validator.

• With id (OpenAI-style)

  {"messages":[{"role":"user","content":"Weather in Seattle?"},{"role":"assistant","content":"","tool_calls":[{"id":"call_abc","type":"function","function":{"name":"get_weather","arguments":{"city":"Seattle"}}}]
  },{"role":"tool","tool_call_id":"call_abc","content":"72F sunny"},{"role":"assistant","content":"It's 72F and sunny."}]}

• Without id (customizer-style)

  {"messages":[{"role":"user","content":"Weather in Seattle?"},{"role":"assistant","content":"","tool_calls":[{"type":"function","function":{"name":"get_weather","arguments":{"city":"Seattle"}}}]}]}

Differences:

• No id on the tool_call (auto-filled to "call_0" by the normalizer).
• No follow-up tool role message / final assistant reply — customizer data trains the model to emit the call, not to consume its response.

Changelog

• Add specific line by line info of high level changes in this PR.

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[akoumpa]

/ok to test d58fd8e

[akoumpa]

/claude review

[claude]

LGTM