class_docstr: Batch Sampler for a MultiDataset. Iterates in parallel over different batch samplers for each dataset.
  Yields batches [(x_1, y_1), ..., (x_s, y_s)] for s datasets.
class_docstr_tok: ["Batch","Sampler","for","a","MultiDataset",".","Iterates","in","parallel","over","different","batch","samplers","for","each","dataset",".","Yields","batches","[","(","x_1",",","y_1",")",",","...",",","(","x_s",",","y_s",")","]","for","s","datasets","."]
code: def __iter__(self):
      for _ in range(self._n_batches):
          yield self._get_nexts()
      self._init_iterators()
  
imports: import logging
  import numpy
  import torch.utils.data
  import torchvision
  from torch.utils.data.sampler import BatchSampler, RandomSampler
label_desc: Use image node_id 4 for calling the MultiDataLoader obj's underlying member method code with example usage: obj.__iter__() without return types