Swin main layer
# What does this PR do?

Refactor the Swin model to have a `MainLayer` which is called by all models to get the Swin outputs (pre-head). 

c.f. [relevant comment](https://github.com/huggingface/transformers/pull/17427#discussion_r895278064) from @sayakpaul on ResNet port

The following script was run to check weights could still be successfully loaded into the TF models:

```
from transformers import AutoFeatureExtractor, TFSwinForImageClassification, TFSwinForMaskedImageModeling

checkpoint = "microsoft/swin-tiny-patch4-window7-224"

# relative_position_index isn't updated during training. In TF set as instance param
print("\nTFSwinForImageClassification - from PyTorch checkpoint")
tf_model = TFSwinForImageClassification.from_pretrained(checkpoint, from_pt=True)
print("\nTFSwinForImageClassification - from TF checkpoint")
tf_model = TFSwinForImageClassification.from_pretrained(checkpoint)

# relative_position_index isn't updated during training. In TF set as instance param
# We don't have a masked image modeling checkpoint - use image classification checkpoint
# Some weights will not be used (classifier head)
# Some weights newly initialised (decoder, mask token)
print("\nTFSwinForMaskedImageModeling - from PyTorch checkpoint")
tf_model = TFSwinForMaskedImageModeling.from_pretrained(checkpoint, from_pt=True)
print("\nTFSwinForMaskedImageModeling - from TF checkpoint")
tf_model = TFSwinForMaskedImageModeling.from_pretrained(checkpoint)
```

Produced the outputs: 
```
TFSwinForImageClassification - from PyTorch checkpoint
Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFSwinForImageClassification: ['swin.encoder.layers.1.blocks.0.attention.self.relative_position_index', 'swin.encoder.layers.0.blocks.0.attention.self.relative_position_index', 'swin.encoder.layers.1.blocks.1.attention.self.relative_position_index', 'swin.encoder.layers.2.blocks.1.attention.self.relative_position_index', 'swin.encoder.layers.0.blocks.1.attention.self.relative_position_index', 'swin.encoder.layers.3.blocks.0.attention.self.relative_position_index', 'swin.encoder.layers.3.blocks.1.attention.self.relative_position_index', 'swin.encoder.layers.2.blocks.4.attention.self.relative_position_index', 'swin.encoder.layers.2.blocks.3.attention.self.relative_position_index', 'swin.encoder.layers.2.blocks.5.attention.self.relative_position_index', 'swin.encoder.layers.2.blocks.0.attention.self.relative_position_index', 'swin.encoder.layers.2.blocks.2.attention.self.relative_position_index']
- This IS expected if you are initializing TFSwinForImageClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing TFSwinForImageClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).
All the weights of TFSwinForImageClassification were initialized from the PyTorch model.
If your task is similar to the task the model of the checkpoint was trained on, you can already use TFSwinForImageClassification for predictions without further training.

TFSwinForImageClassification - from TF checkpoint
All model checkpoint layers were used when initializing TFSwinForImageClassification.

All the layers of TFSwinForImageClassification were initialized from the model checkpoint at microsoft/swin-tiny-patch4-window7-224.
If your task is similar to the task the model of the checkpoint was trained on, you can already use TFSwinForImageClassification for predictions without further training.

TFSwinForMaskedImageModeling - from PyTorch checkpoint
Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFSwinForMaskedImageModeling: ['classifier.weight', 'swin.encoder.layers.1.blocks.0.attention.self.relative_position_index', 'swin.encoder.layers.0.blocks.0.attention.self.relative_position_index', 'swin.encoder.layers.1.blocks.1.attention.self.relative_position_index', 'swin.encoder.layers.2.blocks.1.attention.self.relative_position_index', 'swin.encoder.layers.0.blocks.1.attention.self.relative_position_index', 'swin.encoder.layers.3.blocks.0.attention.self.relative_position_index', 'swin.encoder.layers.3.blocks.1.attention.self.relative_position_index', 'swin.encoder.layers.2.blocks.4.attention.self.relative_position_index', 'swin.encoder.layers.2.blocks.3.attention.self.relative_position_index', 'classifier.bias', 'swin.encoder.layers.2.blocks.5.attention.self.relative_position_index', 'swin.encoder.layers.2.blocks.0.attention.self.relative_position_index', 'swin.encoder.layers.2.blocks.2.attention.self.relative_position_index']
- This IS expected if you are initializing TFSwinForMaskedImageModeling from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing TFSwinForMaskedImageModeling from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).
Some weights or buffers of the TF 2.0 model TFSwinForMaskedImageModeling were not initialized from the PyTorch model and are newly initialized: ['swin.embeddings.mask_token', 'decoder.0.weight', 'decoder.0.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

TFSwinForMaskedImageModeling - from TF checkpoint
Some layers from the model checkpoint at microsoft/swin-tiny-patch4-window7-224 were not used when initializing TFSwinForMaskedImageModeling: ['classifier']
- This IS expected if you are initializing TFSwinForMaskedImageModeling 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 TFSwinForMaskedImageModeling from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some layers of TFSwinForMaskedImageModeling were not initialized from the model checkpoint at microsoft/swin-tiny-patch4-window7-224 and are newly initialized: ['decoder', 'swin/embeddings/mask_token:0']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```


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- [ ] Did you write any new necessary tests?

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