code: def test_custom_sharding_spec(self):
      ranks = [
          "rank:0/cuda:0",
          "rank:1/cuda:1",
          "rank:2/cuda:2",
          "rank:3/cuda:3",
      ]
  
      grid_spec = GridShardingSpec(grid_size=4, placements=ranks)
  
      tensor_properties = TensorProperties(
          dtype=torch.get_default_dtype(),
          layout=torch.strided,
          requires_grad=False,
          memory_format=torch.contiguous_format,
          pin_memory=False,
      )
  
      meta = grid_spec.build_metadata(
          torch.Size((8, 8)), tensor_properties
      )
      check_tensor(meta.shards_metadata, torch.Size((8, 8)))
  
imports: from typing import List, Union
  from dataclasses import dataclass
  import copy
  import torch
  from torch.testing._internal.common_utils import TestCase
  from torch.testing._internal.common_distributed import requires_nccl, skip_if_lt_x_gpu
  from torch.distributed._shard import sharded_tensor, _shard_tensor
  from torch.distributed._shard.sharding_spec import ShardingSpec, ChunkShardingSpec, DevicePlacementSpec, EnumerableShardingSpec, ShardMetadata, _infer_sharding_spec_from_shards_metadata
  from torch.distributed._shard.sharded_tensor import TensorProperties, ShardedTensor, ShardedTensorMetadata
  from torch.distributed._shard.sharding_spec._internals import check_tensor, get_split_size, get_chunked_dim_size, get_chunk_sharding_params, validate_non_overlapping_shards_metadata
  from torch.testing._internal.common_cuda import TEST_MULTIGPU
  from torch.testing._internal.common_utils import run_tests, skip_but_pass_in_sandcastle_if
  from torch.testing._internal.distributed._shard.sharded_tensor._test_st_common import _chunk_sharding_specs_list_for_test
  from torch.testing._internal.distributed._shard.sharded_tensor import ShardedTensorTestBase, with_comms
label_desc: Use image node_id 1 for calling the TestCustomShardingSpec obj's underlying member method code with example usage: obj.test_custom_sharding_spec() without return types