class_docstr: Dataset that loads tensors via a json file containing paths to video files, transcripts, and durations (in seconds).
  Each new line is a different sample. Example below:
  {"video_filepath": "/path/to/video.mp4", "text_filepath": "/path/to/video.txt", "duration": 23.147}
  ...
  {"video_filepath": "/path/to/video.mp4", "text": "the transcription", "offset": 301.75, "duration": 0.82, "utt":
  "utterance_id", "ctm_utt": "en_4156", "side": "A"}
  Args:
      manifest_filepath: Path to manifest json as described above. Can be comma-separated paths.
      parser: Str for a language specific preprocessor or a callable.
      int_values (bool): If true, load samples as 32-bit integers. Defauts to False.
      max_duration: If video exceeds this length, do not include in dataset
      min_duration: If video is less than this length, do not include in dataset
      max_utts: Limit number of utterances
      trim: whether or not to trim silence. Defaults to False
      bos_id: Id of beginning of sequence symbol to append if not None
      eos_id: Id of end of sequence symbol to append if not None
      pad_id: Id of pad symbol. Defaults to 0
      return_sample_id (bool): whether to return the sample_id as a part of each sample
      channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing.
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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 {
          "video_signal": NeuralType(
              ("B", "C", "T", "H", "W"), VideoSignal()
          ),
          "video_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: import os
  from typing import Callable, Dict, Iterable, List, Optional, Tuple, Union
  import torch
  import webdataset
  from nemo.collections.asr.data.audio_to_text import cache_datastore_manifests, expand_sharded_filepaths
  from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType
  from nemo.collections.common import tokenizers
  from nemo.collections.common.parts.preprocessing import collections, parsers
  from nemo.collections.multimodal.speech_cv.parts.preprocessing.features import VideoFeaturizer
  from nemo.core.classes import Dataset, IterableDataset
  from nemo.core.neural_types import *
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling the _VideoTextDataset obj's underlying member method code with example usage: obj.output_types() and returns: dict