Fix nested vocab_size for DistillationTrainer and GOLDTrainer

[Beichen-Ma]

Fixes #5585. The root cause was in DistillationTrainer.__init__ calls teacher_model.resize_token_embeddings(self.model.config.vocab_size), which raises AttributeError on configs where vocab_size is nested (e.g. Qwen3_5Config exposes it under config.text_config).

Fix

• Replaced with self.model.config.get_text_config().vocab_size, which transformers defines on PretrainedConfig to return the text sub-config on nested configs and self on flat ones.
• Applied the same fix to GOLDTrainer (identical duplicated pattern). Updated an existing GOLD unit test whose SimpleNamespace mock didn't implement get_text_config.

Tests

• Reproduced the original error on Qwen3.5-0.8B student / Qwen3.5-2B teacher before the fix.
• After the fix, trainer init and one training step complete cleanly.
• pytest tests/experimental/test_gold_trainer.py passes.
• DistillationTrainer has no existing unit test file. Given it's experimental and the fix is one line mirroring an existing pattern, I scoped this PR to the minimal fix rather than introducing a new test file.

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Note

Low Risk
Single-line logic change in trainer initialization plus a test mock update; low risk aside from potential edge cases if a model config lacks get_text_config().

Overview
Fixes teacher embedding resizing during initialization for DistillationTrainer and GOLDTrainer by reading vocabulary size via self.model.config.get_text_config().vocab_size instead of self.model.config.vocab_size, supporting models with nested text configs.

Updates the existing GOLD init unit test mock config to implement get_text_config() so the new code path is exercised.

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[cmpatino]

The changes look good to me! Let's wait for the review from another maintainer before merging