Move logic into pixelshuffle layer
# What does this PR do?

Moves logic relating to PixelShuffle layer into layer class. This is to provide a consistent usage wrt the PyTorch pixel shuffle layer and makes sure all necessary logic is ported if any `#Copied from ` statements are used. 

Also renamed layer `PixelShuffle` -> `TFSwinPixelShuffle`  to reflect naming in the rest of the repo. The following was run to make sure the models are still compatible with current weights: 

```
from transformers import AutoFeatureExtractor, TFSwinForImageClassification

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)
```

With the following output. Note: `relative_position_index` isn't updated during training and is set as an instance param in the TF model
```
TFSwinForImageClassification - from PyTorch checkpoint
Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFSwinForImageClassification: ['swin.encoder.layers.3.blocks.1.attention.self.relative_position_index', 'swin.encoder.layers.2.blocks.0.attention.self.relative_position_index', 'swin.encoder.layers.2.blocks.4.attention.self.relative_position_index', 'swin.encoder.layers.1.blocks.0.attention.self.relative_position_index', 'swin.encoder.layers.2.blocks.2.attention.self.relative_position_index', 'swin.encoder.layers.1.blocks.1.attention.self.relative_position_index', 'swin.encoder.layers.3.blocks.0.attention.self.relative_position_index', 'swin.encoder.layers.0.blocks.1.attention.self.relative_position_index', 'swin.encoder.layers.0.blocks.0.attention.self.relative_position_index', 'swin.encoder.layers.2.blocks.1.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']
- 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.
```


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- [ ] Did you write any new necessary tests?
_The documentation is not available anymore as the PR was closed or merged._