Quick fix for `test_torch_fx` under torch 1.13
# What does this PR do?

Quick fix for `test_torch_fx` under torch 1.13.

Since `torch 1.13`, using `meta` device gives some noisy tensors, which breaks `test_torch_fx`.
@michaelbenayoun is more qualified than me to explain this more clearly.

This PR changes the device to "cpu" if `fx.py` is used in the testing. 

running all `test_torch_fx_*` tests:
  -  with "**meta**": 1m33s
  - with "**cpu**": 1m45s

So the speed-up of using `meta` is tiny , and we can just use `cpu`.

However,

**Questions**:
  - It looks like `fx.py` is only used in the testing (at least in `transformers`)?
  - Is this module in `optimum`?
    - If so:
      -  is it used only in testing?
      -  is it used for large real models?
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_20222). All of your documentation changes will be reflected on that endpoint.<|||||>Apart from the exceptional case where we don't want to use the prod server for testing, I am very much against using env variable/constants that change for testing. Why would the test fail for the meta device but in real life the code would work? It doesn't make any sense.<|||||>Well, using `meta` with `torch 1.13` gives random values, which fail some **input checks** in some modeling files, like the one at the end.

If this is the intended behavior of `meta` (i.e. if the behavior in PT < 1.13 was wrong - but we took advantage of it), then moving away from `meta` seems the way to me.

If you still think we should stick with `meta`, I will leave @michaelbenayoun to take care of the related test though :-)

#### More details

(notice that here we don't provide any input tensor)
```ptyon
traced_model = symbolic_trace(model, input_names)
```
During this call, in `fx.py`, there are tensor preparation, which use `meta` and it gives random values which fail the check in some modeling files

(bart)
```python
        eos_mask = input_ids.eq(self.config.eos_token_id)

        if len(torch.unique_consecutive(eos_mask.sum(1))) > 1:
            raise ValueError("All examples must have the same number of <eos> tokens.")
```<|||||>close for now, unless one day we need this.