class_docstr: Dataset that loads tensors via a json file containing paths to audio files,
  labels, and durations and offsets(in seconds). Each new line is a
  different sample. Example below:
  and their target labels. JSON files should be of the following format::
      {"audio_filepath": "/path/to/audio_wav_0.wav", "duration": time_in_sec_0, "label": target_label_0, "offset": offset_in_sec_0}
      ...
      {"audio_filepath": "/path/to/audio_wav_n.wav", "duration": time_in_sec_n, "label": target_label_n, "offset": offset_in_sec_n}
  Args:
      manifest_filepath (Union[str, List[str]]): Dataset parameter. Path to JSON containing data.
      labels (list): Dataset parameter. List of target classes that can be output by the speaker recognition model.
      featurizer
      min_duration (float): Dataset parameter. All training files which have a duration less than min_duration
          are dropped. Note: Duration is read from the manifest JSON.
          Defaults to 0.1.
      max_duration (float): Dataset parameter.
          All training files which have a duration more than max_duration
          are dropped. Note: Duration is read from the manifest JSON.
          Defaults to None.
      trim (bool): Whether to use trim silence from beginning and end of audio signal using librosa.effects.trim().
          Defaults to False.
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code: def __getitem__(self, index):
      sample = self.collection[index]
  
      offset = sample.offset
  
      if offset is None:
          offset = 0
  
      features = self.featurizer.process(
          sample.audio_file,
          offset=offset,
          duration=sample.duration,
          trim=self.trim,
      )
      f, fl = features, torch.tensor(features.shape[0]).long()
  
      if not self.is_regression_task:
          t = torch.tensor(self.label2id[sample.label]).long()
      else:
          t = torch.tensor(sample.label).float()
  
      tl = torch.tensor(
          1
      ).long()  # For compatibility with collate_fn used later
  
      return f, fl, t, tl
  
imports: import io
  import os
  from typing import Dict, List, Optional, 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.preprocessing.features import WaveformFeaturizer
  from nemo.collections.asr.parts.preprocessing.segment import available_formats
  from nemo.collections.common.parts.preprocessing import collections
  from nemo.core.classes import Dataset, IterableDataset
  from nemo.core.neural_types import AudioSignal, LabelsType, LengthsType, NeuralType, RegressionValuesType
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the _AudioLabelDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: f,  fl,  t,  tl