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,
      num_iterations: int = 1000,
      qcomm_forward_precision: Optional[CommType] = CommType.FP16,
      qcomm_backward_precision: Optional[CommType] = CommType.BF16,
  ) -> 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
  
      qcomm_forward_precision: Compression used in forwards pass. FP16 is the recommended usage. INT8 and FP8 are in development, but feel free to try them out.
      qcomm_backward_precision: Compression used in backwards pass. We recommend using BF16 to ensure training stability.
  
      The effects of quantized comms will be most apparent in large training jobs across multiple nodes where inter host communication is expensive.
      """
      if dense_arch_layer_sizes is None:
          dense_arch_layer_sizes = [64, embedding_dim]
      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)
  
      apply_optimizer_in_backward(
          RowWiseAdagrad,
          train_model.model.sparse_arch.parameters(),
          {"lr": learning_rate},
      )
      qcomm_codecs_registry = (
          get_qcomm_codecs_registry(
              qcomms_config=QCommsConfig(
                  # pyre-ignore
                  forward_precision=qcomm_forward_precision,
                  # pyre-ignore
                  backward_precision=qcomm_backward_precision,
              )
          )
          if backend == "nccl"
          else None
      )
      sharder = EmbeddingBagCollectionSharder(
          qcomm_codecs_registry=qcomm_codecs_registry
      )
  
      model = DistributedModelParallel(
          module=train_model,
          device=device,
          # pyre-ignore
          sharders=[sharder],
      )
  
      non_fused_optimizer = KeyedOptimizerWrapper(
          dict(in_backward_optimizer_filter(model.named_parameters())),
          lambda params: torch.optim.Adagrad(params, lr=learning_rate),
      )
      # Overlap comm/compute/device transfer during training through train_pipeline
      train_pipeline = TrainPipelineSparseDist(
          model,
          non_fused_optimizer,
          device,
      )
  
      # train model
      train_iterator = iter(
          _get_random_dataset(
              num_embeddings=num_embeddings,
          )
      )
      for _ in tqdm(range(int(num_iterations)), mininterval=5.0):
          train_pipeline.progress(train_iterator)
  
imports: import os
  from typing import List, Optional
  import torch
  from torch import distributed
  from torch.distributed.elastic.multiprocessing.errors import record
  from torch.distributed.optim import _apply_optimizer_in_backward
  from torch.utils.data import IterableDataset
  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.fbgemm_qcomm_codec import CommType, get_qcomm_codecs_registry, QCommsConfig
  from torchrec.distributed.model_parallel import DistributedModelParallel
  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 torchrec.optim.rowwise_adagrad import RowWiseAdagrad
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling a global function with example usage: train(num_embeddings, embedding_dim, dense_arch_layer_sizes, over_arch_layer_sizes, learning_rate, num_iterations, qcomm_forward_precision, qcomm_backward_precision) without return types