code: def test_load_state_dict_prefix(
      self, sharders: List[ModuleSharder[nn.Module]]
  ) -> None:
      (m1, m2), batch = self._generate_dmps_and_batch(sharders)
  
      # load the second's (m2's) with the first (m1's) state_dict
      m2.load_state_dict(
          cast(
              "OrderedDict[str, torch.Tensor]",
              m1.state_dict(prefix="alpha"),
          ),
          prefix="alpha",
      )
  
      # validate the models are equivalent
      sd1 = m1.state_dict()
      for key, value in m2.state_dict().items():
          v2 = sd1[key]
          if isinstance(value, ShardedTensor):
              assert len(value.local_shards()) == 1
              dst = value.local_shards()[0].tensor
          else:
              dst = value
          if isinstance(v2, ShardedTensor):
              assert len(v2.local_shards()) == 1
              src = v2.local_shards()[0].tensor
          else:
              src = v2
          self.assertTrue(torch.equal(src, dst))
  
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 7 for calling the ModelParallelStateDictBase obj's underlying member method code with example usage: obj.test_load_state_dict_prefix(sharders) without return types