Fixing DeformableDETR the easy but not the best way (IMO).
Author: Narsil
Pull request: yes

# What does this PR do?

This PR fixes the pipeline to accomodate  DeformableDETR.

The core of the issue is 
https://github.com/huggingface/transformers/blob/main/src/transformers/models/deformable_detr/modeling_deformable_detr.py#L178-L181

Both these tensor don't use `batch_size` in the first place, so the magic batchig/debatching of the pipeline is confused.

It's not entirely clear why the tensor has this weird shape though. @NielsRogge is there any reason ?
Regardless we can't really change that this it would be a breaking change.

The culprit lines are here:
https://github.com/huggingface/transformers/blob/585f9c6d9efa9f6e93888b6adf84912ba3f98dfc/src/transformers/models/deformable_detr/modeling_deformable_detr.py#L1397-L1398

@sgugger I though I remembered some tensors where not returned by default with `return_loss=False`.
Would that be something acceptable ?

The pipeline fix is fine to avoid all complications, but it's brittle since during batching/debatching the pipeline has only access to the tensor name, so if any other model reuses the same name we're going to have a bigger issue.

Fixes: https://github.com/huggingface/transformers/issues/19024

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Comments:
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Thanks for your PR, so the issue would be resolved if these tensors have `batch_size` in the first dimension (rather than the second)?

I think we can still fix that.

> Thanks for your PR, so the issue would be resolved if these tensors have batch_size in the first dimension (rather than the second)?

Yes it should. When unknown tensors are seen, the batching/unbatching assumes the first dimension is the batch_size.

We fixed it correctly