code: def test_meta_device_dmp_state_dict(self) -> None:
      # pyre-fixme[6]: For 1st param expected `ProcessGroup` but got
      #  `Optional[ProcessGroup]`.
      env = ShardingEnv.from_process_group(dist.GroupMember.WORLD)
  
      m1 = TestSparseNN(
          tables=self.tables,
          num_float_features=self.num_float_features,
          weighted_tables=self.weighted_tables,
          dense_device=self.device,
      )
      # dmp with real device
      dmp1 = DistributedModelParallel(
          module=m1,
          init_data_parallel=False,
          init_parameters=False,
          sharders=get_default_sharders(),
          device=self.device,
          env=env,
          plan=EmbeddingShardingPlanner(
              topology=Topology(
                  world_size=env.world_size,
                  compute_device=self.device.type,
              )
          ).plan(m1, get_default_sharders()),
      )
  
      m2 = TestSparseNN(
          tables=self.tables,
          num_float_features=self.num_float_features,
          weighted_tables=self.weighted_tables,
          dense_device=self.device,
      )
      # dmp with meta device
      dmp2 = DistributedModelParallel(
          module=m2,
          init_data_parallel=False,
          init_parameters=False,
          sharders=get_default_sharders(),
          device=torch.device("meta"),
          env=env,
          plan=EmbeddingShardingPlanner(
              topology=Topology(
                  world_size=env.world_size,
                  compute_device=self.device.type,
              )
          ).plan(m2, get_default_sharders()),
      )
  
      sd1 = dmp1.state_dict()
      for key, v2 in dmp2.state_dict().items():
          v1 = sd1[key]
          if isinstance(
              v2, nn.parameter.UninitializedParameter
          ) and isinstance(v1, nn.parameter.UninitializedParameter):
              continue
          if isinstance(v2, ShardedTensor):
              self.assertTrue(isinstance(v1, ShardedTensor))
              assert len(v2.local_shards()) == 1
              dst = v2.local_shards()[0].tensor
          else:
              dst = v2
          if isinstance(v1, ShardedTensor):
              assert len(v1.local_shards()) == 1
              src = v1.local_shards()[0].tensor
          else:
              src = v1
          self.assertEqual(src.size(), dst.size())
  
imports: import os
  import unittest
  from collections import defaultdict
  from typing import Any, Callable, cast, Dict, List, Optional, OrderedDict, Tuple
  import numpy
  import torch
  import torch.nn
  from fbgemm_gpu.split_embedding_configs import EmbOptimType
  from hypothesis import given, settings, strategies, Verbosity
  from torch import distributed
  from torch.distributed._shard.sharded_tensor import ShardedTensor
  from torchrec import distributed
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel, EmbeddingTableConfig
  from torchrec.distributed.embeddingbag import EmbeddingBagCollectionSharder, ShardedEmbeddingBagCollection
  from torchrec.distributed.fused_embeddingbag import ShardedFusedEmbeddingBagCollection
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.distributed.planner import EmbeddingShardingPlanner, ParameterConstraints, Topology
  from torchrec.distributed.sharding_plan import get_default_sharders
  from torchrec.distributed.test_utils.test_model import _get_default_rtol_and_atol, ModelInput, TestSparseNN, TestSparseNNBase
  from torchrec.distributed.test_utils.test_sharding import copy_state_dict, create_test_sharder, gen_model_and_input, ModelInputCallable, SharderType
  from torchrec.distributed.types import ModuleSharder, ShardingEnv, ShardingPlan, ShardingType
  from torchrec.modules.embedding_configs import BaseEmbeddingConfig, EmbeddingBagConfig, PoolingType
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
  from torchrec.modules.fused_embedding_modules import FusedEmbeddingBagCollection
  from torchrec.test_utils import get_free_port, seed_and_log
label_desc: Use image node_id 5 for calling the ModelParallelStateDictBase obj's underlying member method code with example usage: obj.test_meta_device_dmp_state_dict() without return types