class_docstr: Use this to upsample rare classes and downsample common classes in your unbalanced classification dataset.
code: def __init__(self) -> None:
      super().__init__(None)
  
imports: import logging
  import random
  from collections import defaultdict
  from typing import Dict
  import torch
  from torch.utils.data.sampler import Sampler
label_desc: Use image node_id 1 to create a new ImbalancedClassificationDatasetSampler object from inherited base classes: FlairSampler with example: obj = ImbalancedClassificationDatasetSampler()