CohereASR training-loss double-shift bug (same pattern as Moonshine #46784)

### System Info - `transformers` version: 5.13.0.dev0 (main branch at commit dc5a497c18) - - Platform: Windows - - - Python version: 3.x - - - - Using GPU: N/A (code-level bug, not runtime) ### Who can help? @eustlb @ebezzam @vasqu ### Information - [x] The official example scripts - [ ] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [ ] My own task or dataset (give details below) ### Reproduction aIn `src/transformers/models/cohere_asr/modular_cohere_asr.py`, `CohereAsrForConditionalGeneration.forward()` has a double-shift bug: 1. Labels are shifted right via `shift_tokens_right()` (line 483) to create `decoder_input_ids` 2. 2. Then `self.loss_function()` is called (line 503), which maps to `ForCausalLMLoss` 3. 3. `ForCausalLMLoss` shifts labels again internally (`labels[..., 1:]` in `loss/loss_utils.py` line 63-64) This means the model trains against `labels[..., 1:]` instead of `labels`. This is the **exact same bug** that was fixed in Moonshine in PR #46784 (commit d8c235494e). The Moonshine fix replaced `self.loss_function()` with `CrossEntropyLoss()`, but `CohereAsr` — which inherits from Moonshine and overrides `forward` — was not updated. ```python # Buggy code (line 503 in modular_cohere_asr.py): loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size) # Should be: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.reshape(-1, self.config.vocab_size), labels.reshape(-1)) ``` ### Expected behavior Training loss should be computed directly against `labels` without any additional shifting, since `shift_tokens_right()` already handles the decoder input alignment. This matches the behavior of Whisper, Bart, and the now-fixed Moonshine model. The fix should use `CrossEntropyLoss()` instead of `self.loss_function()`, as done in Moonshine PR #46784.