feat: expose the strict flag to allow catching missing model layers while loading a checkpoint
Author: HarikrishnanBalagopal
Pull request: yes

# What does this PR do?

Currently model loading succeeds even when a layer is missing from the saved model weights.
This pull reqeust exposes the `strict` flag from https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.load_state_dict
to allow raising an error when the checkpoint is incomplete (missing some layers).

Also see https://github.com/pytorch/pytorch/issues/82963

<!-- Remove if not applicable -->

# Fixes

```
  warnings.warn(
There were missing keys in the checkpoint model loaded: ['lm_head.weight'].
```

Related issues:
- https://github.com/huggingface/transformers/issues/29128
- https://github.com/huggingface/transformers/issues/27972

## Before submitting
- [n] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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      Pull Request section?
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      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.

- trainer: @muellerzr and @SunMarc

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Comments:
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> Not sure we want to add this flag in trainer as this is related to model loading cc @muellerzr This is something that should be fixed when trying to load the model before the training.

I understand the concern but the `train` function also has `resume_from_checkpoint` which does model loading.