code: def init_dataloader(
      batch_size: int,
      num_batches: int,
      num_embeddings: int,
      backend: str,
      num_embeddings_per_feature: Optional[List[int]] = None,
      seed: Optional[int] = None,
      pin_memory: Optional[bool] = None,
  ) -> DataLoader:
      pin_memory = (
          (backend == "nccl") if pin_memory is None else pin_memory
      )
      return DataLoader(
          RandomRecDataset(
              keys=DEFAULT_CAT_NAMES,
              batch_size=batch_size,
              hash_size=num_embeddings,
              hash_sizes=num_embeddings_per_feature,
              manual_seed=seed,
              ids_per_feature=1,
              num_dense=len(DEFAULT_INT_NAMES),
              num_batches=num_batches,
          ),
          batch_size=None,
          batch_sampler=None,
          pin_memory=pin_memory,
          num_workers=0,
      )
  
imports: import argparse
  import sys
  import tempfile
  import uuid
  from typing import Iterator, List, Optional, Tuple
  import torch
  from pyre_extensions import none_throws
  from torch import distributed
  from torch.distributed import launcher
  from torch.distributed.optim.apply_optimizer_in_backward import _apply_optimizer_in_backward
  from torch.utils.data import DataLoader
  from torcheval.metrics.classification.auroc import BinaryAUROC
  from torcheval.metrics.toolkit import sync_and_compute
  from torchrec import EmbeddingBagCollection
  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.comm import get_local_size
  from torchrec.distributed.model_parallel import DistributedModelParallel, get_default_sharders
  from torchrec.distributed.planner import EmbeddingShardingPlanner, Topology
  from torchrec.distributed.planner.storage_reservations import HeuristicalStorageReservation
  from torchrec.models.dlrm import DLRM, DLRMTrain
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.optim.keyed import KeyedOptimizerWrapper
  from torchrec.optim.optimizers import in_backward_optimizer_filter
  from torchtnt.framework.callbacks import TQDMProgressBar
  from torchtnt.framework.fit import fit
  from torchtnt.framework.state import State
  from torchtnt.framework.unit import EvalUnit, TrainUnit
  from torchtnt.utils import get_process_group_backend_from_device, init_from_env, rank_zero_print
  from torchtnt.utils.loggers.tensorboard import TensorBoardLogger
label_desc: Use image node_id 1 for calling a global function with example usage: init_dataloader(batch_size, num_batches, num_embeddings, backend, num_embeddings_per_feature, seed, pin_memory) and returns: DataLoader