code: def forward(  # type: ignore[override]
      # pyre-fixme[2]: Parameter must be annotated.
      ctx,
      input: "dtensor.DTensor",
      device_mesh: DeviceMesh,
      placements: Tuple[Placement, ...],
  ):
      current_spec = input._spec
      ctx.current_spec = current_spec
      target_spec = DTensorSpec(
          device_mesh, placements, tensor_meta=input._spec.tensor_meta
      )
  
      local_tensor = input._local_tensor
      output = redistribute_local_tensor(
          local_tensor, current_spec, target_spec
      )
  
      return dtensor.DTensor(
          output,
          device_mesh,
          target_spec.placements,
          shape=input.shape,
          dtype=input.dtype,
          requires_grad=input.requires_grad,
          stride=input.stride(),
      )
  
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 1 for calling the Redistribute obj's underlying member method code with example usage: obj.forward(ctx, input, device_mesh, placements) and returns: dtensor