code: def _get_random_dataloader(
      num_embeddings: int,
      batch_size: int = 32,
      pin_memory: bool = False,
  ) -> DataLoader:
      return DataLoader(
          RandomRecDataset(
              keys=DEFAULT_CAT_NAMES,
              batch_size=batch_size,
              hash_size=num_embeddings,
              ids_per_feature=1,
              num_dense=len(DEFAULT_INT_NAMES),
          ),
          batch_size=None,
          batch_sampler=None,
          pin_memory=pin_memory,
          num_workers=0,
      )
  
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 1 for calling a global function with example usage: _get_random_dataloader(num_embeddings, batch_size, pin_memory) and returns: DataLoader