code: def main(argv: List[str]) -> None:
      args = parse_args(argv)
  
      batch_size = 1024
      num_dense = 1000
      num_sparse = 20
      num_embeddings = 1000000
  
      configs = [
          EmbeddingBagConfig(
              name=f"table{id}",
              embedding_dim=64,
              num_embeddings=num_embeddings,
              feature_names=[f"feature{id}"],
          )
          for id in range(num_sparse)
      ]
  
      rank = int(os.environ["LOCAL_RANK"])
      if not args.cpu_only and torch.cuda.is_available():
          device = torch.device(f"cuda:{rank}")
          backend = "nccl"
          torch.cuda.set_device(device)
      else:
          device = torch.device("cpu")
          backend = "gloo"
          print(
              "\033[92m"
              + f"WARNING: Running in CPU mode. Cuda is available: {torch.cuda.is_available()}. CPU only: {args.cpu_only}"
          )
  
      dist.init_process_group(backend=backend)
  
      model = DLRM(
          embedding_bag_collection=EmbeddingBagCollection(
              tables=configs, device=torch.device("meta")
          ),
          dense_in_features=num_dense,
          dense_arch_layer_sizes=[500, 64],
          over_arch_layer_sizes=[32, 16, 1],
          dense_device=device,
      )
      model = DistributedModelParallel(
          module=model,
          device=device,
      )
      optimizer = KeyedOptimizerWrapper(
          dict(in_backward_optimizer_filter(model.named_parameters())),
          lambda params: torch.optim.SGD(params, lr=0.01),
      )
  
      random_iterator = RandomIterator(
          batch_size, num_dense, num_sparse, num_embeddings
      )
      loss_fn = torch.nn.BCEWithLogitsLoss()
      for _ in range(10):
          (dense_features, sparse_features, labels) = next(
              random_iterator
          )
          dense_features = dense_features.to(device)
          sparse_features = sparse_features.to(device)
          output = model(dense_features, sparse_features)
          labels = labels.to(device)
          loss = loss_fn(output.squeeze(), labels.float())
          torch.sum(loss, dim=0).backward()
          optimizer.zero_grad()
          optimizer.step()
  
      print(
          "\033[92m"
          + "Successfully ran a few epochs for DLRM. Installation looks good!"
      )
  
file_path: torchrec/test_installation.py
imports: import argparse
  import os
  import sys
  from typing import Iterator, List
  import torch
  import torch.distributed
  from torch.distributed.elastic.multiprocessing.errors import record
  from torchrec import EmbeddingBagCollection, KeyedJaggedTensor
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.models.dlrm import DLRM
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.optim.keyed import KeyedOptimizerWrapper
  from torchrec.optim.optimizers import in_backward_optimizer_filter
label_desc: Use image node_id 2 for calling a global function with example usage: main(argv) without return types