implementation that does not require re-entrant autograd. This
        allows "checkpoint" to support additional functionality, such
        as working as expected with "torch.autograd.grad" and support
        for keyword arguments input into the checkpointed function.
        Note that future versions of PyTorch will default to
        "use_reentrant=False". Default: "True"
  * **args** -- tuple containing inputs to the "function"

Returns:
      Output of running "function" on "*args"
torch.utils.checkpoint.checkpoint_sequential(functions, segments, input, use_reentrant=True, **kwargs)
A helper function for checkpointing sequential models.
Sequential models execute a list of modules/functions in order
   (sequentially). Therefore, we can divide such a model in various
   segments and checkpoint each segment. All segments except the last
   will run in "torch.no_grad()" manner, i.e., not storing the
   intermediate activations. The inputs of each checkpointed segment