class_docstr: Samples elements more than once in a single pass through the data.
  
  This allows the number of samples per epoch to be larger than the number
  of samples itself, which can be useful when training on 2D slices taken
  from 3D images, for instance.
class_docstr_tok: ["Samples","elements","more","than","once","in","a","single","pass","through","the","data",".","This","allows","the","number","of","samples","per","epoch","to","be","larger","than","the","number","of","samples","itself",",","which","can","be","useful","when","training","on","2D","slices","taken","from","3D","images",",","for","instance","."]
code: def __iter__(self):
      return iter(self.gen_sample_array())
  
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 3 for calling the MultiSampler obj's underlying member method code with example usage: obj.__iter__() and returns: iter