Fix wrong behavior of DDPStrategy option with simple GAN training using DDP

[samsara-ku]

What does this PR do?

Fixes #20866 #20328 #18740 #17212

This PR adds MultiModelDDPStrategy class and its simple execution example, for the multi-gpu training with GAN training.

Simply speaking:

* right way using DDP in pytorch.
# each model is divided and wrapped by DDP class
generator, discriminator = DDP(generator), DDP(discrimiantor)

* current pytorch-lightning GAN training.
# trainer contains two nn.Modules; generator and discriminator
DDP(trainer) -> trainer contains two nn.Modules; generator and discriminator

* proposed `MultiModelDDP` class can use this way.
# trainer also has two nn.Modules, generator and discriminator;
# this way allocate DDP class to the each model in the trainer, not the trainer.
MultiModelDDP(trainer)

• Currently, pytorch lightning simple GAN training has has problem with DistributedDataParallel strategy. It tries to wrap pl.trainer, not the nn.Module models in the pl.trainer

• Although we can activate find_unused_parameters=True options to avoid this issue but it is not right way; I think it is just a trick.

• So the key idea to solve this issue is that we assign DistributedDataParallel to the each model in the pl.trainer, different from the previous strategy DDPStrategy.

• I already tested with my GPUs to visaulize the result and tracked the gradients of model with each epoch; it works and you can see the visulized result in thie google drive link

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📚 Documentation preview 📚: https://pytorch-lightning--20936.org.readthedocs.build/en/20936/

[Borda]

lets move it out s a funtion