code: def test_parameter_init(self) -> None:
      class MyModel(nn.Module):
          def __init__(self, device: str, val: float) -> None:
              super().__init__()
              self.p = nn.Parameter(
                  torch.empty(3, dtype=torch.float32, device=device)
              )
              self.val = val
              self.reset_parameters()
  
          def reset_parameters(self) -> None:
              nn.init.constant_(self.p, self.val)
  
      dist.destroy_process_group()
      dist.init_process_group(backend="gloo")
  
      # Check that already allocated parameters are left 'as is'.
      cpu_model = MyModel(device="cpu", val=3.2)
      sharded_model = DistributedModelParallel(
          cpu_model,
      )
      sharded_param = next(sharded_model.parameters())
      np.testing.assert_array_equal(
          np.array([3.2, 3.2, 3.2], dtype=np.float32),
          sharded_param.detach().numpy(),
      )
  
      # Check that parameters over 'meta' device are allocated and initialized.
      meta_model = MyModel(device="meta", val=7.5)
      sharded_model = DistributedModelParallel(
          meta_model,
      )
      sharded_param = next(sharded_model.parameters())
      np.testing.assert_array_equal(
          np.array([7.5, 7.5, 7.5], dtype=np.float32),
          sharded_param.detach().numpy(),
      )
  
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 4 for calling the ModelParallelStateDictBase obj's underlying member method code with example usage: obj.test_parameter_init() without return types