Use `deformable_detr` kernel from the Hub
Author: danieldk
Pull request: yes

# What does this PR do?

Remove the `deformable_detr` kernel from `kernels/` and use the pre-built kernel from the Hub instead.

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Comments:
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Hey @danieldk! Super nice to see kernels integrated! Is there a way to disable the kernel path dynamically? RT-DETR's `compile` full graph and `torch.export` are broken now, they should go through the torch path.

Ah normally if cuda is not available it should fallback to noraml forwar + if not available should do nothing 

(@qubvel export can be fast test no? sorry that it broke)

it's too slow, even though it's on a small model

we discussed with @danieldk in Slack, the idea is to have a way to control which path to go through, smth like

```python
if self.disable_custom_kernels or is_torchdynamo_compiling():
    with use_kernel_mapping({}, inherit_mapping=False): # inherit_mapping would be a new argument
        output = self.attn(
            value,
            spatial_shapes,
            spatial_shapes_list,
            level_start_index,
            sampling_locations,
            attention_weights,
            self.im2col_step,
        )
else:
    ...
```

Or we register the use kernel with torch API to allow compile ? 

Not aware of this but it can be an option, yes. Anyway it would be nice to be able to disable it manually in case there are any issues with the kernel + understand which path is executed (e.g. for RT-DETR to export for CoreML we need 5D tensors instead of 6D passed into the kernel, so I refactored pytorch path of deformable attention in this [PR](https://github.com/huggingface/transformers/pull/36514))