class_docstr: This is a type of PlacementSpec that allows users to specify a generic
  sharding scheme by enumerating exactly how each shard is laid out.
  
  Args:
      shards(List[ShardMetadata]): List of :class:`ShardMetadata` objects representing
          each shard. Note that none of the shards should overlap.
class_docstr_tok: ["This","is","a","type","of","PlacementSpec","that","allows","users","to","specify","a","generic","sharding","scheme","by","enumerating","exactly","how","each","shard","is","laid","out",".","Args",":","shards","(","List","[","ShardMetadata","]",")",":","List","of",":","class",":","`","ShardMetadata","`","objects","representing","each","shard",".","Note","that","none","of","the","shards","should","overlap","."]
code: def shard(
      self, tensor: torch.Tensor, src_rank: int = 0, process_group=None
  ) -> "ShardedTensor":
      # TODO: figure out a generic and efficient way to scatter the shards for EnumerableShardingSpec
      raise NotImplementedError(
          "EnumerableShardingSpec.shard not implemented yet!"
      )
  
imports: from abc import ABC, abstractmethod
  from dataclasses import dataclass
  import functools
  from typing import Callable, Dict, List, TYPE_CHECKING
  import torch
  from ._internals import check_tensor, get_chunked_dim_size, get_split_size, validate_non_overlapping_shards_metadata
  from torch.distributed._shard.metadata import ShardMetadata
  import torch.distributed._shard.sharded_tensor.metadata
  from torch.distributed._shard.op_registry_utils import _decorator_func
label_desc: Use image node_id 3 for calling the EnumerableShardingSpec obj's underlying member method code with example usage: obj.shard(tensor, src_rank, process_group) without return types