class_docstr: For datasets that need to be read sequentially, usually because the data is
  being streamed or otherwise can't be manipulated on a single machine.
class_docstr_tok: ["For","datasets","that","need","to","be","read","sequentially",",","usually","because","the","data","is","being","streamed","or","otherwise","ca","n't","be","manipulated","on","a","single","machine","."]
code: def __iter__(self):
      raise NotImplementedError
  
imports: import logging
  import numpy
  import torch.utils.data
  from fairseq.data import data_utils
label_desc: Use image node_id 1 for calling the FairseqIterableDataset obj's underlying member method code with example usage: obj.__iter__() without return types