code: def __init__(
      self,
      batch_size: int,
      num_dense: int,
      num_sparse: int,
      num_embeddings: int,
  ) -> None:
      self.batch_size = batch_size
      self.num_dense = num_dense
      self.num_sparse = num_sparse
      self.sparse_keys = [
          f"feature{id}" for id in range(self.num_sparse)
      ]
      self.num_embeddings = num_embeddings
      self.num_ids_per_feature = 3
      self.num_ids_to_generate = (
          self.num_sparse * self.num_ids_per_feature * self.batch_size
      )
  
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 1 to create a new RandomIterator object from inherited base classes: Iterator with example: obj = RandomIterator(batch_size, num_dense, num_sparse, num_embeddings)