class_docstr: Wraps another sampler to yield a mini-batch of indices.
  It enforces that the batch only contain elements from the same group.
  It also tries to provide mini-batches which follows an ordering which is
  as close as possible to the ordering from the original sampler.
class_docstr_tok: ["Wraps","another","sampler","to","yield","a","mini-batch","of","indices",".","It","enforces","that","the","batch","only","contain","elements","from","the","same","group",".","It","also","tries","to","provide","mini-batches","which","follows","an","ordering","which","is","as","close","as","possible","to","the","ordering","from","the","original","sampler","."]
code: def __len__(self):
      raise NotImplementedError(
          "len() of GroupedBatchSampler is not well-defined."
      )
  
imports: import numpy
  from torch.utils.data.sampler import BatchSampler, Sampler
label_desc: Use image node_id 3 for calling the GroupedBatchSampler obj's underlying member method code with example usage: obj.__len__() without return types