class_docstr: Standard Batch Sampler for data parallel. This sampler yields batches of fixed batch size
  and spatial resolutions.
  
  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\tbase_im_size=(h={self.crop_size_h}, w={self.crop_size_w})"
          f"\n\tbase_batch_size={self.batch_size_gpu0}"
      )
      return extra_repr_str
  
file_path: ml-cvnets/data/sampler/batch_sampler.py
imports: import argparse
  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
label_desc: Use image node_id 3 for calling the BatchSampler obj's underlying member method code with example usage: obj.extra_repr() and returns: extra_repr_str