[Swin] Replace hard-coded batch size to enable dynamic ONNX export
# What does this PR do?

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This PR tweaks the modeling code of `swin` to enable dynamic batch sizes with the ONNX export. With this fix, the ONNX slow tests for this model now pass, including the slow tests for the original PyTorch model:

```
This passes
RUN_SLOW=1 pytest -x -sv tests/models/swin/test_modeling_swin.py
This also passes
RUN_SLOW=1 pytest -x -sv tests/onnx/test_onnx_v2.py -k "swin"
```

Since this change also impacts other models, I've also checked the modeling slow tests pass for:

- [x] `maskformer`
- [x] `donut_swin`
- [x] `swin_v2`

Related to https://github.com/huggingface/transformers/issues/17476



_The documentation is not available anymore as the PR was closed or merged._<|||||>(a bit off-topic, but still related question)

Viewing the issue and the fix provided this PR, I was thinking we would have a lot of the same errors due to this `hard-coded batch size`. However, when I check `bert`:
https://github.com/huggingface/transformers/blob/10100979ed0594d4cfe1982cdfac9642a68e473e/src/transformers/models/bert/modeling_bert.py#L962
and
https://github.com/huggingface/transformers/blob/10100979ed0594d4cfe1982cdfac9642a68e473e/src/transformers/models/bert/modeling_bert.py#L968

but the onnx tests still pass for `bert`. Are `batch_size` and `seq_length` not hard-coded here? Just wondering if @lewtun has already some insight regarding this.

<|||||>> Is the issue caused by the changes in #19255? More precisely, from (newly added code) https://github.com/dwyatte/transformers/blob/949683675d83cc38620106626822279cd45b076b/src/transformers/onnx/convert.py#L368
> 
> The error shows `Outputs values doesn't match between reference model and ONNX exported model` - it must be non-trivi al to figure out this is coming from the shape things! How are you able to find out 💯 ? Is there some tool we can use to check things (tensor values/shape) when running onnx inference?

Yes, this issue was surfaced by #19255, which implemented a stronger validation test on exported ONNX models. Basically, it generates the ONNX graph using dummy data with one batch size `b`, and then validates the forward pass with a different `b'`. 

The reason it can be non-trivial to figure out when an export fails to have agreement between the PyTorch / ONNX models is that ONNX traces a graph based on dummy data, and this tracing can be incorrect if there are data-dependent flow statements (Swin in particular has a lot of these if/else statements). Currently , the best tool I know of is to visualise the graph with [Netron](https://netron.app/) and manually inspect for discrepancies.

> Viewing the issue and the fix provided this PR, I was thinking we would have a lot of the same errors due to this `hard-coded batch size`. However, when I check `bert`:

I think in those cases we don't hit a problem because `batch_size` is only used to create the attention mask when none is provided. Since our dummy input provides an attention mask, this flow in the graph is never traced AFAICT 