code: def get_dataloader(
      batch_size: int,
      num_embeddings: int,
      pin_memory: bool = False,
      num_workers: int = 0,
  ) -> DataLoader:
      """
      Gets a Random dataloader for the two tower model, containing a two_feature KJT as sparse_features, empty dense_features
      and binary labels
  
      Args:
          batch_size (int): batch_size
          num_embeddings (int): hash_size of the two embedding tables
          pin_memory (bool): Whether to pin_memory on the GPU
          num_workers (int) Number of dataloader workers
  
      Returns:
          dataloader (DataLoader): PyTorch dataloader for the specified options.
  
      """
      two_tower_column_names = DEFAULT_RATINGS_COLUMN_NAMES[:2]
  
      return DataLoader(
          RandomRecDataset(
              keys=two_tower_column_names,
              batch_size=batch_size,
              hash_size=num_embeddings,
              ids_per_feature=1,
              num_dense=0,
          ),
          batch_size=None,
          batch_sampler=None,
          pin_memory=pin_memory,
          num_workers=num_workers,
      )
  
imports: from torch.utils.data import DataLoader
  from torchrec.datasets.movielens import DEFAULT_RATINGS_COLUMN_NAMES
  from torchrec.datasets.random import RandomRecDataset
label_desc: Use image node_id 1 for calling a global function with example usage: get_dataloader(batch_size, num_embeddings, pin_memory, num_workers) and returns: DataLoader