Refactor to inherit from nn.Module instead of nn.ModuleList
# What does this PR do?

Refactors classes inheriting from `nn.ModuleList` to inherit from `nn.Module` instead. This is to make debugging and inspecting layer outputs more easy. 

See also: https://github.com/huggingface/transformers/pull/17493

The following was run to check the weight loading in: 
```
from transformers import BeitForImageClassification, Data2VecVisionForImageClassification

print("\nLoading in Data2VecVision model...")
model_checkpoint = "facebook/data2vec-vision-base"
model = Data2VecVisionForImageClassification.from_pretrained(model_checkpoint)

print("\nLoading in BeiT model...")
model_checkpoint = "microsoft/beit-base-patch16-224-pt22k"
model = BeitForImageClassification.from_pretrained(model_checkpoint)
```

Output:
```
Loading in Data2VecVision model...
/Users/aroberts/.virtualenvs/tenv/lib/python3.9/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  /Users/distiller/project/pytorch/aten/src/ATen/native/TensorShape.cpp:2228.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
Some weights of Data2VecVisionForImageClassification were not initialized from the model checkpoint at facebook/data2vec-vision-base and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Loading in BeiT model...
Some weights of the model checkpoint at microsoft/beit-base-patch16-224-pt22k were not used when initializing BeitForImageClassification: ['layernorm.bias', 'layernorm.weight', 'lm_head.weight', 'lm_head.bias']
- This IS expected if you are initializing BeitForImageClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BeitForImageClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BeitForImageClassification were not initialized from the model checkpoint at microsoft/beit-base-patch16-224-pt22k and are newly initialized: ['beit.pooler.layernorm.bias', 'classifier.bias', 'classifier.weight', 'beit.pooler.layernorm.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```

Running on `main` we see the same weights are newly initialized:
```
Loading in Data2VecVision model...
/Users/aroberts/.virtualenvs/tenv/lib/python3.9/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  /Users/distiller/project/pytorch/aten/src/ATen/native/TensorShape.cpp:2228.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
Some weights of Data2VecVisionForImageClassification were not initialized from the model checkpoint at facebook/data2vec-vision-base and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Loading in BeiT model...
Some weights of the model checkpoint at microsoft/beit-base-patch16-224-pt22k were not used when initializing BeitForImageClassification: ['layernorm.bias', 'lm_head.bias', 'lm_head.weight', 'layernorm.weight']
- This IS expected if you are initializing BeitForImageClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BeitForImageClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BeitForImageClassification were not initialized from the model checkpoint at microsoft/beit-base-patch16-224-pt22k and are newly initialized: ['beit.pooler.layernorm.weight', 'classifier.bias', 'classifier.weight', 'beit.pooler.layernorm.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```


## 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#start-contributing-pull-requests),
      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. 
_The documentation is not available anymore as the PR was closed or merged._<|||||>Let us know when you'd like for us to review! :)<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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