feat: add typing overloads to `apply_chat_templates`
Author: winstxnhdw
Pull request: yes

# What does this PR do?

The `transformers` library is notorious for having poor typing support. Anywhere from no type support to the usage of incorrect types altogether. This PR is an attempt to improve the type hinting capability of `apply_chat_templates` via the user of typing overloads. As this is just a typing enhancement, there is no performance cost for this change.

I have tried to cover all the return types with discriminated unions but it seems Pyright isn't able to infer the correct overload once you are past 2 discriminators. For those who want to give it a try, I have pasted my attempts below.

```python
    @overload
    def apply_chat_template(
        self,
        conversation: List[Mapping[str, str]],
        tools: Optional[List[Mapping[Any, Any]]] = None,
        documents: Optional[List[Mapping[str, str]]] = None,
        chat_template: Optional[str] = None,
        add_generation_prompt: bool = False,
        continue_final_message: bool = False,
        tokenize: Literal[False] = False,
        padding: bool = False,
        truncation: bool = False,
        max_length: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        return_dict: bool = False,
        return_assistant_tokens_mask: bool = False,
        tokenizer_kwargs: Optional[Mapping[str, Any]] = None,
        **kwargs: Any,
    ) -> str: ...
    @overload
    def apply_chat_template(
        self,
        conversation: List[List[Mapping[str, str]]],
        tools: Optional[List[Mapping[Any, Any]]] = None,
        documents: Optional[List[Mapping[str, str]]] = None,
        chat_template: Optional[str] = None,
        add_generation_prompt: bool = False,
        continue_final_message: bool = False,
        tokenize: Literal[False] = False,
        padding: bool = False,
        truncation: bool = False,
        max_length: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        return_dict: bool = False,
        return_assistant_tokens_mask: bool = False,
        tokenizer_kwargs: Optional[Mapping[str, Any]] = None,
        **kwargs: Any,
    ) -> List[str]: ...
    @overload
    def apply_chat_template(
        self,
        conversation: Union[List[Mapping[str, str]], List[List[Mapping[str, str]]]],
        tools: Optional[List[Mapping[Any, Any]]] = None,
        documents: Optional[List[Mapping[str, str]]] = None,
        chat_template: Optional[str] = None,
        add_generation_prompt: bool = False,
        continue_final_message: bool = False,
        tokenize: Literal[True] = True,
        padding: bool = False,
        truncation: bool = False,
        max_length: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        return_dict: Literal[True] = True,
        return_assistant_tokens_mask: bool = False,
        tokenizer_kwargs: Optional[Mapping[str, Any]] = None,
        **kwargs: Any,
    ) -> BatchEncoding: ...
```

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @SunMarc

Integrations:

- deepspeed: HF Trainer/Accelerate: @muellerzr
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc

Documentation: @stevhliu

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->


Comments:
cc @Rocketknight1 

Hi @winstxnhdw, I appreciate the PR, but unfortunately we generally don't add `@overload` decorators that significantly bloat the codebase! We generally accept type hints, but they're "nice to have" rather than something we're trying to maintain strict compliance with.

Amazing.