âââââââââââââââââââââ       ^^^^^^
  nn.Sequential(Layer1(), Layer1(), Layer2(), Layer3())
  #             ^^^^^^      âââââââââââââââââââââ

To use the same name for multiple skip tensors, they must be
   isolated by different namespaces. See "isolate()".
Raises:
      TypeError -- one or more pairs of stash and pop are not
      matched.
Tutorials
The following tutorials give a good overview of how to use the "Pipe"
API to train your models with the rest of the components that PyTorch
provides:


Training Transformer models using Pipeline Parallelism


Training Transformer models using Distributed Data Parallel and
  Pipeline Parallelism


Acknowledgements
The implementation for pipeline parallelism is based on fairscale's
pipe implementation and torchgpipe. We would like to thank both teams