code: def __init__(self, size: int, count: int) -> None:
      self.count: int = count
      self.size: int = 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 1 to create a new RandomIterableDataset object from inherited base classes: IterableDataset with example: obj = RandomIterableDataset(size, count)