code: def __next__(self) -> (torch.Tensor, KeyedJaggedTensor, torch.Tensor):
      float_features = torch.randn(
          self.batch_size,
          self.num_dense,
      )
      labels = torch.randint(
          low=0,
          high=2,
          size=(self.batch_size,),
      )
      sparse_ids = torch.randint(
          high=self.num_sparse,
          size=(self.num_ids_to_generate,),
      )
      sparse_features = KeyedJaggedTensor.from_offsets_sync(
          keys=self.sparse_keys,
          values=sparse_ids,
          offsets=torch.tensor(
              list(
                  range(
                      0,
                      self.num_ids_to_generate + 1,
                      self.num_ids_per_feature,
                  )
              ),
              dtype=torch.int32,
          ),
      )
      return (float_features, sparse_features, labels)
  
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 2 for calling the RandomIterator obj's underlying member method code with example usage: obj.__next__() and returns: float_features,  sparse_features,  labels