code: def __iter__(self) -> Iterator[Tensor]:
      for _ in range(self.count):
          yield torch.randn(self.size)
  
imports: from typing import Iterable, Iterator, Optional, Tuple
  import torch
  from torch import nn, Tensor
  from torch.utils.data import DataLoader, Dataset, IterableDataset, TensorDataset
  from torchtnt.framework.auto_unit import AutoUnit
  from torchtnt.framework.state import EntryPoint, PhaseState, State
  from torchtnt.framework.unit import EvalUnit, PredictUnit, TrainUnit
  from torchtnt.utils.lr_scheduler import TLRScheduler
label_desc: Use image node_id 2 for calling the RandomIterableDataset obj's underlying member method code with example usage: obj.__iter__() without return types