class_docstr: Represents a shard of the overall Tensor including its
  offsets, lengths and device placement.
  
  Args:
      shard_offsets(List[int]): Offsets in the original tensor indicating
          the start offsets for this shard. Should have the same rank as
          the original tensor.
      shard_sizes(List[int]): Integers indicating the size of each
          dimension for this shard. Should have the same rank as the
          original tensor.
      placement(:class:`torch.distributed._remote_device`):
          Specifies the placement of this shard.
code: def __init__(
      self,
      shard_offsets: List[int],
      shard_sizes: List[int],
      placement: Optional[Union[str, _remote_device]] = None,
  ):
      self.shard_offsets = shard_offsets
      self.shard_sizes = shard_sizes
      if isinstance(placement, str):
          self.placement = _remote_device(placement)
      else:
          self.placement = placement
      if len(self.shard_offsets) != len(self.shard_sizes):
          raise ValueError(
              f"shard_offsets and shard_sizes should have "
              f"the same number of elements, found {len(self.shard_offsets)} "
              f"and {self.shard_sizes} respectively"
          )
  
      for i in range(len(self.shard_offsets)):
          if self.shard_offsets[i] < 0:
              raise ValueError("shard_offsets should be >=0")
          if self.shard_sizes[i] < 0:
              raise ValueError("shard_sizes should be >= 0")
  
imports: from dataclasses import dataclass
  from typing import List, Union, Optional
  from functools import reduce
  from torch.distributed.remote_device import _remote_device
label_desc: Use image node_id 1 to create a new ShardMetadata object  with example: obj = ShardMetadata(shard_offsets, shard_sizes, placement)