code: def _get_random_dataset(
      num_embeddings: int,
      batch_size: int = 32,
  ) -> IterableDataset:
      return RandomRecDataset(
          keys=DEFAULT_CAT_NAMES,
          batch_size=batch_size,
          hash_size=num_embeddings,
          ids_per_feature=1,
          num_dense=len(DEFAULT_INT_NAMES),
      )
  
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 1 for calling a global function with example usage: _get_random_dataset(num_embeddings, batch_size) and returns: RandomRecDataset