class_docstr: Base class for standard and DataParallel Sampler.
  
  Every subclass should implement `__iter__` method, providing a way to iterate
  over indices of dataset elements.
  
  Args:
      opts: command line argument
      n_data_samples: Number of samples in the dataset
      is_training: Training mode or not. Default: False
class_docstr_tok: ["Base","class","for","standard","and","DataParallel","Sampler",".","Every","subclass","should","implement","`","__iter__","`","method",",","providing","a","way","to","iterate","over","indices","of","dataset","elements",".","Args",":","opts",":","command","line","argument","n_data_samples",":","Number","of","samples","in","the","dataset","is_training",":","Training","mode","or","not",".","Default",":","False"]
code: def extra_repr(self) -> str:
      extra_repr_str = (
          f"\n\t num_repeat={self.num_repeats}"
          f"\n\t trunc_rep_aug={self.trunc_rep_aug}"
      )
      return extra_repr_str
  
imports: import argparse
  import copy
  import math
  import random
  from typing import Any, Iterator, List, Tuple
  import numpy
  import torch
  import torch.distributed
  from torch.utils.data.sampler import Sampler
label_desc: Use image node_id 9 for calling the BaseSampler obj's underlying member method code with example usage: obj.extra_repr() and returns: extra_repr_str