code: def train(
      num_embeddings: int = 1024**2,
      embedding_dim: int = 128,
      dense_arch_layer_sizes: Optional[List[int]] = None,
      over_arch_layer_sizes: Optional[List[int]] = None,
      learning_rate: float = 0.1,
  ) -> None:
      """
      Constructs and trains a DLRM model (using random dummy data). Each script is run on each process (rank) in SPMD fashion.
      The embedding layers will be sharded across available ranks
      """
      if dense_arch_layer_sizes is None:
          dense_arch_layer_sizes = [64, 128]
      if over_arch_layer_sizes is None:
          over_arch_layer_sizes = [64, 1]
  
      # Init process_group , device, rank, backend
      rank = int(os.environ["LOCAL_RANK"])
      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)
  
      # Construct DLRM module
      eb_configs = [
          EmbeddingBagConfig(
              name=f"t_{feature_name}",
              embedding_dim=embedding_dim,
              num_embeddings=num_embeddings,
              feature_names=[feature_name],
          )
          for feature_idx, feature_name in enumerate(DEFAULT_CAT_NAMES)
      ]
      dlrm_model = DLRM(
          embedding_bag_collection=EmbeddingBagCollection(
              tables=eb_configs, device=torch.device("meta")
          ),
          dense_in_features=len(DEFAULT_INT_NAMES),
          dense_arch_layer_sizes=dense_arch_layer_sizes,
          over_arch_layer_sizes=over_arch_layer_sizes,
          dense_device=device,
      )
      train_model = DLRMTrain(dlrm_model)
  
      # Enable optimizer fusion
      fused_params = {
          "learning_rate": learning_rate,
          "optimizer": OptimType.EXACT_ROWWISE_ADAGRAD,
      }
      sharders = [
          EmbeddingBagCollectionSharder(fused_params=fused_params),
      ]
      # Distribute model across devices
      model = DistributedModelParallel(
          module=train_model,
          device=device,
          sharders=cast(List[ModuleSharder[nn.Module]], sharders),
      )
  
      # Overlap comm/compute/device transfer during training through train_pipeline
      non_fused_optimizer = KeyedOptimizerWrapper(
          dict(in_backward_optimizer_filter(model.named_parameters())),
          lambda params: torch.optim.Adagrad(params, lr=learning_rate),
      )
      train_pipeline = TrainPipelineSparseDist(
          model,
          non_fused_optimizer,
          device,
      )
  
      # train model
      train_iterator = iter(
          _get_random_dataloader(
              num_embeddings=num_embeddings,
              pin_memory=backend == "nccl",
          )
      )
      for _ in tqdm(range(int(1e4)), mininterval=5.0):
          train_pipeline.progress(train_iterator)
  
imports: import os
  from typing import cast, List, Optional
  import torch
  from fbgemm_gpu.split_embedding_configs import EmbOptimType
  from torch import distributed, nn
  from torch.utils.data import DataLoader
  from torchrec.datasets.criteo import DEFAULT_CAT_NAMES, DEFAULT_INT_NAMES
  from torchrec.datasets.random import RandomRecDataset
  from torchrec.distributed import TrainPipelineSparseDist
  from torchrec.distributed.embeddingbag import EmbeddingBagCollectionSharder
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.distributed.types import ModuleSharder
  from torchrec.models.dlrm import DLRM, DLRMTrain
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
  from torchrec.optim.keyed import KeyedOptimizerWrapper
  from torchrec.optim.optimizers import in_backward_optimizer_filter
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling a global function with example usage: train(num_embeddings, embedding_dim, dense_arch_layer_sizes, over_arch_layer_sizes, learning_rate) without return types