code: def _create_sharded_tensor_md_from_dt(
      dt: DTensor, dt_pg: c10d.ProcessGroup
  ) -> ShardedTensorMetadata:
      # This is where it gets tricky, we have to produce a ShardedTensor that has full coverage
      # and yet has only one valid shard for the current rank.
  
      shards_md = []
      my_rank = dist.get_rank(dt_pg)
      scapegoat_rank = 0 if my_rank > 0 else 1
  
      if dt.placements[0].is_shard():
          shard_count = dt_pg.size()
      else:
          shard_count = 1
  
      for i in range(shard_count):
          offsets, sizes = _get_box_for(dt, i)
          shards_md.append(
              ShardMetadata(
                  shard_offsets=list(offsets),
                  shard_sizes=list(sizes),
                  placement=(
                      f"rank:{scapegoat_rank if i > 0 else my_rank}/{dt._local_tensor.device}"
                  ),
              )
          )
  
      return ShardedTensorMetadata(
          shards_metadata=shards_md,
          size=dt.size(),
          tensor_properties=TensorProperties(
              dtype=dt.dtype,
              layout=dt.layout,
              requires_grad=dt.requires_grad,
              # ignore memory_format and pin_memory as those are not supported by DT
          ),
      )
  
imports: import copy
  from typing import Any, cast, List, Optional, Tuple
  import torch
  import torch.distributed
  import torch.distributed._shard.sharding_spec
  import torch.distributed.distributed_c10d
  from torch.distributed._shard.sharded_tensor import Shard, ShardedTensor, ShardedTensorMetadata, TensorProperties
  from torch.distributed._shard.sharding_spec import ShardMetadata
  from torch.distributed._shard.sharding_spec.chunk_sharding_spec import ChunkShardingSpec
  from torch.distributed._tensor import DeviceMesh, DTensor, Replicate, Shard
  from torch.distributed.device_mesh import _mesh_resources
  from torch.distributed.fsdp._common_utils import _set_fsdp_flattened
  from torch.distributed.fsdp._fsdp_extensions import FSDPExtensions
  from torch.distributed.fsdp._shard_utils import _create_chunk_sharded_tensor
  from torch.distributed.remote_device import _remote_device
  from torch.distributed.tensor.parallel._data_parallel_utils import _flatten_tensor, _unflatten_tensor
label_desc: Use image node_id 5 for calling a global function with example usage: _create_sharded_tensor_md_from_dt(dt, dt_pg) and returns: ShardedTensorMetadata