Rename second input dimension for ONNX-supported CV models
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The second input dimension of `pixel_values` for CV models with ONNX support is currently named "sequence". This PR renames it to "num_channels".

Also:
- MobileViT is added to the list of models to test in `tests/onnx/test_onnx_v2.py`
- for DETR, the second input dimension is removed for `pixel_masks` because there is actually no channel dimension (`batch` x `height` x `width`)
- for MobileViT, a second input dimension is added to `pixel_values` so that it follows the same pattern as other vision models


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As discussed with @NielsRogge, it does not make much sense to call the second input dimension of `pixel_values` "sequence" while it is actually the number of channels. This can be misleading if one takes a closer look at the ONNX model (e.g. with Netron).<|||||>I re-ran all slow tests and they all passed.<|||||>_The documentation is not available anymore as the PR was closed or merged._<|||||>> I am not familiar enough with ONNX to evaluate if this breaks exiting code in any way, so could you please tell us whether a user will have to change anything in their code due to this change?

Axe names do not matter for exporting the model or performing inference with it. The only use case I could think of is if one has a script to parse the ONNX graph and does any kind of operation involving axe names, but this would rely on tools that are not provided by Transformers anyway.

What do you think @lewtun @michaelbenayoun @mfuntowicz ?<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.<|||||>Gently pinging @lewtun here<|||||>Pinging @michaelbenayoun and @JingyaHuang as Lewis is off for a few weeks.<|||||>Yes, I do not think it is a breaking change either. Same question as @JingyaHuang : do we need dynamic axes for the channels?<|||||>@JingyaHuang @michaelbenayoun I guess some models can deal with both grayscale and RGB images. I let @NielsRogge confirm here.<|||||>Yes technically the channel dimension can be 1 for greyscale images, 3 for RGB images, and 4 for RGBA images.<|||||>Thank you! I was super confused while working on #18587, where I don't know if change to better names will break things or not.