class_docstr: Samples elements randomly from a given list of indices for imbalanced dataset
  
  :param indices: (list, optional): a list of indices
  :param num_samples: (int, optional): number of samples to draw
class_docstr_tok: ["Samples","elements","randomly","from","a","given","list","of","indices","for","imbalanced","dataset",":","param","indices",":","(","list",",","optional",")",":","a","list","of","indices",":","param","num_samples",":","(","int",",","optional",")",":","number","of","samples","to","draw"]
code: def __len__(self):
      return self.num_samples
  
imports: import math
  from .utils import th_random_choice
  import torch.utils.data
  import torchvision
  from .datasets import FolderDataset, MultiFolderDataset, PredictFolderDataset, ClonedFolderDataset
  from torch.utils.data.sampler import Sampler
label_desc: Use image node_id 4 for calling the ImbalancedDatasetSampler obj's underlying member method code with example usage: obj.__len__() and returns: self