Support mosaic optimizations as plugins

Nobody has claimed this yet.

This library [mosaic](https://github.com/mosaicml/composer) has neat tricks for optimizing the models for faster training.
Each application is done as a single line to the model
```python
import composer.functional as cf
from torchvision import models

my_model = models.resnet18()

# add blurpool and squeeze excite layers
model = cf.apply_blurpool(my_model)
model = cf.apply_squeeze_excite(my_model)

# your own training code starts here
```

Which is something we can automatically do for users under the hood if they want to enable the mosaic optimizations.

I propose an API like this
```python
import pytorch_lightning as pl

trainer = pl.Trainer(plugins=[
mosaic.BlurPool(replace_convs=True, replace_maxpools=True, blur_first=True),
mosaic.ChannelsLast(),
mosaic.CutMix(num_classes=10),
mosaic.LabelSmoothing(smoothing=0.1),
])

```

cc @borda @akihironitta @Borda @carmocca @tchaton

No implementation files or tests are identified; start by reviewing the Trainer plugins entry point and the composer.functional optimization calls shown in the issue. Done means the listed Mosaic optimizations can be enabled through the proposed plugin API with the requested configuration options.

Written by the indexing model from the issue text.