[Model] Use merge_by_field_config for MM models (G)

[DarkLight1337]

Purpose

Part of #26149

Test Plan

Model and tensor schema tests should pass. I have also run the example script on all five models.

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Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

[gemini-code-assist]

Code Review

This pull request is a great refactoring that simplifies the multimodal input processing for several models by adopting the new merge_by_field_config mechanism. The changes consistently remove manual input processing logic, such as calls to flatten_bn and type checks, and delegate this responsibility to the MultiModalFieldConfig system. This makes the code cleaner and more maintainable.

The refactoring has been applied to gemma3_mm, gemma3n_mm, glm4_1v, glm4v, and granite_speech models. Key improvements include:

• Simplification of _parse_and_validate_*_input methods.
• Removal of redundant helper functions like _validate_and_reshape_mm_tensor.
• In gemma3n_mm.py, the input data classes were nicely refactored from TypedDict to TensorSchema, improving type safety and clarity.
• In glm4v.py, a subtle but important bug was fixed by explicitly overriding get_input_embeddings to use the implementation from SupportsMultiModal, ensuring correct multimodal embedding merging.
• In granite_speech.py, a small but effective optimization was introduced for calculating audio_embed_sizes.

The changes are well-executed and consistent across all affected models. The code is now more robust and easier to understand. I have not found any issues of high or critical severity. Great work!