code: def __init__(self, num_vals: int) -> None:
      super().__init__()
      self.num_vals = num_vals
      self.vals: List[float] = [
          val / num_vals for val in range(num_vals)
      ]
      self.current_idx = 0
  
imports: import random
  import unittest
  from typing import Any, Iterator, List, Tuple
  from unittest.mock import Mock, patch
  from torch.utils.data import IterDataPipe
  from torchrec.datasets.utils import idx_split_train_val, ParallelReadConcat, rand_split_train_val
label_desc: Use image node_id 1 to create a new _FakeRandom object from inherited base classes: random with example: obj = _FakeRandom(num_vals)