code: def _create_shard_md_from_dt(
      dt: DTensor, current_rank: int
  ) -> ShardMetadata:
      mesh = dt.device_mesh
      assert mesh.ndim == 1, "Only 1D DeviceMeshes currently handled"
  
      offsets, sizes = _get_local_box(dt)
      return ShardMetadata(
          shard_offsets=list(offsets),
          shard_sizes=list(sizes),
          placement=f"rank:{current_rank}/{dt._local_tensor.device}",
      )
  
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 4 for calling a global function with example usage: _create_shard_md_from_dt(dt, current_rank) and returns: ShardMetadata