class_docstr: Variably-size multi-scale batch sampler <https://arxiv.org/abs/2110.02178?context=cs.LG>` for data parallel.
  This sampler yields batches with variable spatial resolution and batch size.
  
  Args:
      opts: command line argument
      n_data_samples: Number of samples in the dataset
      is_training: Training or validation mode. Default: False
class_docstr_tok: ["Variably-size","multi-scale","batch","sampler","<","https",":","\/\/arxiv.org\/abs\/2110.02178","?","context=cs.LG",">","`","for","data","parallel",".","This","sampler","yields","batches","with","variable","spatial","resolution","and","batch","size",".","Args",":","opts",":","command","line","argument","n_data_samples",":","Number","of","samples","in","the","dataset","is_training",":","Training","or","validation","mode",".","Default",":","False"]
code: def extra_repr(self) -> str:
      extra_repr_str = super().extra_repr()
      extra_repr_str += (
          f"\n\t base_im_size=(h={self.crop_size_h}, w={self.crop_size_w})"
          f"\n\t base_batch_size={self.batch_size_gpu0}"
          f"\n\t scales={self.img_batch_tuples}"
          f"\n\t scale_inc={self.scale_inc}"
          f"\n\t min_scale_inc_factor={self.min_scale_inc_factor}"
          f"\n\t max_scale_inc_factor={self.max_scale_inc_factor}"
          f"\n\t ep_intervals={self.scale_ep_intervals}"
      )
  
      return extra_repr_str
  
imports: import argparse
  import random
  from typing import Iterator, Tuple
  from common import DEFAULT_IMAGE_HEIGHT, DEFAULT_IMAGE_WIDTH
  from data.sampler import SAMPLER_REGISTRY
  from data.sampler.base_sampler import BaseSampler, BaseSamplerDDP
  from data.sampler.utils import image_batch_pairs
  from utils import logger
label_desc: Use image node_id 4 for calling the VariableBatchSampler obj's underlying member method code with example usage: obj.extra_repr() and returns: extra_repr_str