code: def _run_init_parameters(cls, path: str) -> None:
      rank = int(os.environ["LOCAL_RANK"])
      world_size = int(os.environ["WORLD_SIZE"])
      if torch.cuda.is_available():
          device: torch.device = torch.device(f"cuda:{rank}")
          backend = "nccl"
          torch.cuda.set_device(device)
      else:
          device: torch.device = torch.device("cpu")
          backend = "gloo"
      dist.init_process_group(
          backend=backend,
          rank=rank,
          world_size=world_size,
          init_method=f"file://{os.path.join(path, 'dist_rdvz')}",
      )
      num_float_features = 32
  
      tables = [
          EmbeddingBagConfig(
              num_embeddings=(i + 1) * 10,
              embedding_dim=(i + 1) * 4 * world_size,
              name="table_" + str(i),
              feature_names=["feature_" + str(i)],
          )
          for i in range(3)
      ]
      weighted_tables = [
          EmbeddingBagConfig(
              num_embeddings=(i + 1) * 10,
              embedding_dim=(i + 1) * 4 * world_size,
              name="weighted_table_" + str(i),
              feature_names=["weighted_feature_" + str(i)],
          )
          for i in range(2)
      ]
      m = TestSparseNN(
          tables=tables,
          num_float_features=num_float_features,
          weighted_tables=weighted_tables,
          dense_device=device,
      )
      # Put all tensors on meta device, then init_params should
      # materialize them.
      for name, param in m._parameters.items():
          if isinstance(param, torch.Tensor):
              m._parameters[name] = torch.nn.Parameter(
                  torch.empty_like(param, device="meta"),
                  requires_grad=param.requires_grad,
              )
  
      shard_modules(m, device=device, init_params=True)
      # init_params should move m to `device`
      for p in m.parameters():
          assert p.device == device
  
imports: import os
  import tempfile
  import unittest
  import uuid
  import torch
  from torch import distributed
  from torch.distributed._composable import replicate
  from torch.distributed._shard.api import ShardedTensor
  from torch.distributed.checkpoint import FileSystemReader, FileSystemWriter, load_state_dict, save_state_dict
  from torch.distributed.launcher.api import elastic_launch, LaunchConfig
  from torchrec.distributed.shard import shard, shard_modules
  from torchrec.distributed.sharding_plan import column_wise
  from torchrec.distributed.test_utils.test_model import ModelInput, TestSparseNN
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.test_utils import skip_if_asan
label_desc: Use image node_id 1 for calling the DDPTest obj's underlying member method code with example usage: obj._run_init_parameters(cls, path) without return types