code: def sample_order(self, epoch: int) -> Sequence[int]:
      seed = self.seed * 912300 + epoch
      result_order = np.zeros(len(self.indices), dtype=np.int64)
      generate_order_inner(
          seed,
          self.page_to_samples_array,
          self.page_sizes,
          result_order,
          2 * self.loader.batch_size,
      )
  
      return result_order
  
file_path: ffcv/ffcv/traversal_order/quasi_random.py
imports: import random
  from typing import Sequence, TYPE_CHECKING
  from numba import njit
  import numpy
  from torch.utils.data import DistributedSampler
  from .base import TraversalOrder
label_desc: Use image node_id 3 for calling the QuasiRandom obj's underlying member method code with example usage: obj.sample_order(epoch) and returns: result_order