class_docstr: torch.utils.data.DataLoader behaves differently depending on the class of the iterator it is passed.
  This wrapper lets us use the iterable setup.
class_docstr_tok: ["torch.utils.data.DataLoader","behaves","differently","depending","on","the","class","of","the","iterator","it","is","passed",".","This","wrapper","lets","us","use","the","iterable","setup","."]
code: def __iter__(self):
      return iter(self.dataset)
  
file_path: summarize-from-feedback/summarize_from_feedback/task_data.py
imports: import json
  import os
  from functools import partial
  from glob import glob
  from typing import Optional
  import blobfile
  import torch
  import summarize_from_feedback
  from summarize_from_feedback import tasks
  from summarize_from_feedback.datasets import jsonl_encoding, get_dataset
  from summarize_from_feedback.model_layout import ModelLayout
  from summarize_from_feedback.utils import even_more_itertools, blobs
label_desc: Use image node_id 2 for calling the _DataLoaderWrapper obj's underlying member method code with example usage: obj.__iter__() and returns: iter