code: def print_sampler_config(self):
      sampler_cfg = {
          "rank": self.rank,
          "world_size": self.world_size,
          "classes_per_batch": self.cpb,
          "unique_classes": self.unique_cpb,
          "batch_size": self.batch_size,
          "num_classes": self.num_classes,
          "epochs": self.epochs,
          "epoch": self.epoch,
          "num_samples": self.num_samples,
          "ipe": self.ipe,
      }
      logging.info(
          "Distributed Sampler config:\n{}".format(sampler_cfg)
      )
  
imports: import contextlib
  import logging
  import queue
  from typing import Dict, List
  import numpy
  import torch
  from PIL import Image
  from torch.utils.data import Dataset
  from torch.utils.data.distributed import DistributedSampler
label_desc: Use image node_id 2 for calling the StratifiedClassSampler obj's underlying member method code with example usage: obj.print_sampler_config() without return types