docstr: Return the full tensor of this DTensor. It will perform necessary collectives
  to gather the local tensors from other ranks in its DeviceMesh and concatenate
  them together. It's a syntatic sugar of the following code:
  
  `dtensor.redistribute(placements=[Replicate()] * mesh.ndim).to_local()`
  
  Keyword args:
      grad_placements (List[:class:`Placement`], optional): the placements describes
          the future layout of any gradient layout of the full Tensor returned from this
          function.
          `full_tensor` converts DTensor to a full torch.Tensor and the returned torch.tensor
          might not be used as the original replicated DTensor layout later in the code. This
          argument is the hint that user can give to autograd in case the gradient
          layout of the returned tensor does not match the original replicated DTensor layout.
          If not specified, we will assume the gradient layout of the full tensor be replicated.
  
  Returns:
      A :class:`torch.Tensor` object that represents the full tensor of this DTensor.
  
  .. note:: `full_tensor` is differentiable.
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code: def full_tensor(
      self, *, grad_placements: Optional[Sequence[Placement]] = None
  ) -> torch.Tensor:
      """
      Return the full tensor of this DTensor. It will perform necessary collectives
      to gather the local tensors from other ranks in its DeviceMesh and concatenate
      them together. It's a syntatic sugar of the following code:
  
      `dtensor.redistribute(placements=[Replicate()] * mesh.ndim).to_local()`
  
      Keyword args:
          grad_placements (List[:class:`Placement`], optional): the placements describes
              the future layout of any gradient layout of the full Tensor returned from this
              function.
              `full_tensor` converts DTensor to a full torch.Tensor and the returned torch.tensor
              might not be used as the original replicated DTensor layout later in the code. This
              argument is the hint that user can give to autograd in case the gradient
              layout of the returned tensor does not match the original replicated DTensor layout.
              If not specified, we will assume the gradient layout of the full tensor be replicated.
  
      Returns:
          A :class:`torch.Tensor` object that represents the full tensor of this DTensor.
  
      .. note:: `full_tensor` is differentiable.
      """
  
      # TODO: fix issue with full_tensor() for uneven-sharded tensor
      # https://github.com/pytorch/pytorch/issues/115310
      redist_res = self.redistribute(
          placements=[Replicate()] * self.device_mesh.ndim
      )
      return _ToTorchTensor.apply(redist_res, grad_placements, False)
  
imports: import warnings
  from typing import Callable, cast, Optional, Sequence, Tuple
  import torch
  import torch.distributed._functional_collectives
  import torch.distributed._tensor.dispatch
  import torch.distributed._tensor.random
  import torch.nn
  from torch.distributed._tensor._collective_utils import mesh_broadcast
  from torch.distributed._tensor._utils import compute_global_tensor_info
  from torch.distributed._tensor.placement_types import DTensorSpec, Placement, Replicate, Shard, TensorMeta
  from torch.distributed._tensor.random import is_rng_supported_mesh, OffsetBasedRNGTracker
  from torch.distributed._tensor.redistribute import Redistribute, redistribute_local_tensor
  from torch.distributed.device_mesh import _mesh_resources, DeviceMesh
label_desc: Use image node_id 9 for calling the DTensor obj's underlying member method code with example usage: obj.full_tensor() and returns: _ToTorchTensor