Added support for dtype in .to() method. 
Issue #24068. The updated  method now accepts both  "device" and "dtype"  as keyword arguments. When "dtype" is provided, the tensors within the  object will be cast to the specified data type.

# What does this PR do?

This PR adds support for the `dtype` parameter in the `.to()` method of the `BatchEncoding` class. Previously, only the `device` parameter was supported. With this enhancement, users can now specify the desired data type for tensor casting and allocation.


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      Pull Request section?
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- [ ] Did you write any new necessary tests?


## Who can review?

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@amyeroberts @NielsRogge 
@amyeroberts hey, yes will do!<|||||>Thanks for your PR but what is the purpose of this? The tokenization results are all integers and changing their dtype will make then unusable by the model.<|||||>@sgugger I was using `BatchEncoding` in a Processor class (`InstructBlipProcessor`) and noticed that it didn't support `dtype` as `BatchFeature` does. However it's a bit unclear to me whether I need to use `BatchFeature` or `BatchEncoding` for multi-modal processors<|||||>Probably batch feature if you have float values. As the name indicates, `BatchEncoding` is for encoded values (so ints).<|||||>@amyeroberts passed all cases. Kindly check!<|||||>> Thanks for your PR but what is the purpose of this? The tokenization results are all integers and changing their dtype will make then unusable by the model.

You did not answer that question though.<|||||>@amannagarkar thanks for your PR, but given the comment by @sgugger it probably makes sense to close this PR, and instead update multimodal processors in the library that return a`BatchEncoding` instead of a `BatchFeature`.

This is because `BatchEncoding` is only used by text-only tokenizers, for which the `dtype` isn't relevant, since they always return LongTensors.<|||||>@sgugger sorry for not responding, I thought Niels answered your question. I will be more careful in the future!
@NielsRogge okay, noted. I will take a look into it!