code: def backward(ctx, grad_output: "dtensor.DTensor"):  # type: ignore[override]
      previous_spec = ctx.current_spec
      # When we run backward pass of redistribute (i.e. manual redistribute from
      # user code instead of torch_dispatch), we scan first and see if we need
      # to change the target placement for one special case:
      #   replicate -> partial.
      # In this case we keep the grad as replicate, this is because we don't
      # want to convert the replicated gradients back to partial, although
      # that's logically conform with the same layout, converting the gradients
      # back to partial is actually useless as you would have to do reduce later
      # which would be more expensive than keeping it replicate! For this reason,
      # we keep the replicate grad here.
      # TODO: see if this make sense for all cases.
      current_spec = grad_output._spec
  
      target_placements: List[Placement] = []
      for current, target in zip(
          current_spec.placements, previous_spec.placements
      ):
          if not current.is_partial() and target.is_partial():
              # keep target placement to replicate instead of partial in this case
              target_placements.append(Replicate())
          else:
              target_placements.append(target)
      target_spec = DTensorSpec(
          previous_spec.mesh,
          tuple(target_placements),
          tensor_meta=previous_spec.tensor_meta,
      )
  
      local_tensor = grad_output._local_tensor
      output = redistribute_local_tensor(
          local_tensor, current_spec, target_spec
      )
      output_dtensor = dtensor.DTensor(
          output,
          target_spec.mesh,
          target_spec.placements,
          shape=grad_output.shape,
          dtype=grad_output.dtype,
          requires_grad=grad_output.requires_grad,
          stride=grad_output.stride(),
      )
  
      return (
          output_dtensor,
          None,
          None,
      )
  
file_path: pytorch/torch/distributed/_tensor/redistribute.py
imports: from typing import cast, Dict, List, Tuple
  import torch
  import torch.distributed._tensor.api
  from torch.distributed._tensor.placement_types import _Partial, DTensorSpec, Placement, Replicate, Shard
  from torch.distributed.device_mesh import DeviceMesh
label_desc: Use image node_id 2 for calling the Redistribute obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: output_dtensor,  None,  None