class_docstr: Loops an underlying iterable indefinitely, with nominal epoch lengths
  
  This is useful for working with IterableDatasets, and particularly
  webdataset-style loading. We recommend using ``.repeat()`` on the
  webdataset IterableDataset instance, so that the underlying dataloader
  naturally continues for ever.
  
  Arguments
  ---------
  loader : iterable
      A DataLoader or other iterable that is looped repeatedly.
  epoch_length : int
      The length of the nominal epoch. After this many steps, raises
      StopIteration
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code: def __iter__(self):
      if self.iterator is None:
          self.iterator = iter(self.loader)
      return self
  
imports: from torch.utils.data import DataLoader
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import _BaseDataLoaderIter
  import logging
  import warnings
  import functools
  from torch.utils.data import DistributedSampler
  from speechbrain.dataio.batch import PaddedBatch, BatchsizeGuesser
  from speechbrain.dataio.dataset import DynamicItemDataset
  from speechbrain.dataio.sampler import ReproducibleRandomSampler, DistributedSamplerWrapper
  from speechbrain.utils.checkpoints import register_checkpoint_hooks, mark_as_saver, mark_as_loader
label_desc: Use image node_id 2 for calling the LoopedLoader obj's underlying member method code with example usage: obj.__iter__() and returns: self