given "context_id" as part of the distributed autograd backward
   pass.
Parameters:
      context_id (int) -- The autograd context id for which we
      should retrieve the gradients.
Returns:
      A map where the key is the Tensor and the value is the
      associated gradient for that Tensor.
Example::
      >>> import torch.distributed.autograd as dist_autograd
      >>> with dist_autograd.context() as context_id:
      >>>     t1 = torch.rand((3, 3), requires_grad=True)
      >>>     t2 = torch.rand((3, 3), requires_grad=True)
      >>>     loss = t1 + t2
      >>>     dist_autograd.backward(context_id, [loss.sum()])
      >>>     grads = dist_autograd.get_gradients(context_id)
      >>>     print(grads[t1])
      >>>     print(grads[t2])
More Information about RPC Autograd
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^


Distributed Autograd Design


Background


Autograd recording during the forward pass


Distributed Autograd Context


Distributed Backward Pass

