class_docstr: Wrapper class for loading HuggingFace IterableDataset for a char-based ASR model
class_docstr_tok: ["Wrapper","class","for","loading","HuggingFace","IterableDataset","for","a","char-based","ASR","model"]
docstr: Returns definitions of module output ports.
                 
docstr_tok: ["Returns","definitions","of","module","output","ports","."]
code: def output_types(self) -> Optional[Dict[str, NeuralType]]:
      """Returns definitions of module output ports."""
      return {
          "audio_signal": NeuralType(("B", "T"), AudioSignal()),
          "a_sig_length": NeuralType(tuple("B"), LengthsType()),
          "transcripts": NeuralType(("B", "T"), LabelsType()),
          "transcript_length": NeuralType(tuple("B"), LengthsType()),
          "sample_id": NeuralType(
              tuple("B"), LengthsType(), optional=True
          ),
      }
  
imports: from typing import Callable, Dict, List, Optional, Tuple, Union
  import datasets
  import torch
  from datasets import concatenate_datasets
  from datasets.distributed import split_dataset_by_node
  from omegaconf import DictConfig, ListConfig, open_dict
  from nemo.collections.asr.data.audio_to_text import _speech_collate_fn
  from nemo.collections.asr.parts.preprocessing.perturb import AudioAugmentor
  from nemo.collections.asr.parts.preprocessing.segment import AudioSegment
  from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType
  from nemo.collections.common import tokenizers
  from nemo.collections.common.parts.preprocessing import parsers
  from nemo.core.classes import Dataset, IterableDataset
  from nemo.core.neural_types import AudioSignal, LabelsType, LengthsType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling the HFIterableAudioToCharDataset obj's underlying member method code with example usage: obj.output_types() and returns: dict