No conv bn folding in ipex to avoid warning
# What does this PR do?

Removes attempting convolution-batchnorm folding from ipex optimization, as it always fails and throws a warning.
See https://github.com/intel/intel-extension-for-pytorch/issues/250

Most models won't even benefit from attempting, but note that even a model like ResNet fails due to checks like:
```
if num_channels != self.num_channels:
```

## Who can review?

- trainer: @sgugger

_The documentation is not available anymore as the PR was closed or merged._<|||||>> 

Thanks for pointing this out.
Actually,  IPEX is doing optimizations as much as it could, and convolution-batchnorm folding does have a chance to bring benefit to vision-related models, e.g, beit/resnet/data2vec_vision, etc. 
Hence if we set it as false, there could be losing some potential optimization chances. 
Back for the warning message, it could be improved on the IPEX side. 
<|||||>> Thanks for pointing this out. Actually, IPEX is doing optimizations as much as it could, and convolution-batchnorm folding does have a chance to bring benefit to vision-related models, e.g, beit/resnet/data2vec_vision, etc. Hence if we set it as false, there could be losing some potential optimization chances. 

The philosophy in the huggingface models seems to be to do a lot of input checks, which is incompatible with the tracing used in ipex. I could not find a single model which doesn't fail.
Discussion on whether or not this tracing could be improved in ipex does not seem to have much traction, see the linked issue. *edit* it actually goes all the way up to pytorch internals, will see if there is traction there.

> Back for the warning message, it could be improved on the IPEX side.

This is certainly true as well. <|||||>> So let's leave it as `False` for now and revisit once IPEX has better support?

Yes, will record this enhancement as TODO for IPEX.