class_docstr: Dataset that loads tensors via a json file containing paths to feature files and their labels. 
  Each new line is a different sample. Example below:
  and their target labels. JSON files should be of the following format:
      {"feature_filepath": "/path/to/audio_feature.pt", "label": "1"}
      ...
      {"feature_filepath": "/path/to/audio_feature.pt", "label": "0"} 
  Args:
      manifest_filepath (str): Path to JSON containing data.
      labels (Optional[list]): List of unique labels collected from all samples.
      augmentor (Optional): feature augmentation
      window_length_in_sec (float): Window length in seconds.
      shift_length_in_sec (float): Shift length in seconds.
      is_regression_task (bool): if True, the labels are treated as for a regression task.
      cal_labels_occurrence (bool): if True, the labels occurrence will be calculated.
      zero_spec_db_val (float): Value to replace non-speech signals in log-melspectrogram.
      min_duration (float): Minimum duration of the audio file in seconds.
      max_duration (float): Maximum duration of the audio file in seconds.
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code: def __init__(
      self,
      *,
      manifest_filepath: str,
      labels: List[str] = None,
      augmentor: "nemo.collections.asr.parts.perturb.AudioAugmentor" = None,
      window_length_in_sec: float = 0.63,
      shift_length_in_sec: float = 0.01,
      is_regression_task: bool = False,
      cal_labels_occurrence: Optional[bool] = False,
      zero_spec_db_val: float = -16.635,
      min_duration: Optional[float] = None,
      max_duration: Optional[float] = None,
  ):
      super().__init__()
      self.window_length_in_sec = window_length_in_sec
      self.shift_length_in_sec = shift_length_in_sec
      self.zero_spec_db_val = zero_spec_db_val
  
      if isinstance(manifest_filepath, str):
          manifest_filepath = manifest_filepath.split(",")
  
      self.collection = collections.ASRFeatureLabel(
          manifests_files=manifest_filepath,
          is_regression_task=is_regression_task,
          cal_labels_occurrence=cal_labels_occurrence,
          min_duration=min_duration,
          max_duration=max_duration,
      )
  
      self.feature_loader = ExternalFeatureLoader(augmentor=augmentor)
      self.labels = labels if labels else self.collection.uniq_labels
  
      self.is_regression_task = is_regression_task
  
      if not is_regression_task:
          self.labels = (
              labels if labels else self.collection.uniq_labels
          )
          self.num_classes = (
              len(self.labels) if self.labels is not None else 1
          )
          self.label2id, self.id2label = {}, {}
          self.id2occurrence, self.labels_occurrence = {}, []
  
          for label_id, label in enumerate(self.labels):
              self.label2id[label] = label_id
              self.id2label[label_id] = label
              if cal_labels_occurrence:
                  self.id2occurrence[
                      label_id
                  ] = self.collection.labels_occurrence[label]
  
          if cal_labels_occurrence:
              self.labels_occurrence = [
                  self.id2occurrence[k]
                  for k in sorted(self.id2occurrence)
              ]
  
          for idx in range(len(self.labels[:5])):
              logging.debug(
                  " label id {} and its mapped label {}".format(
                      idx, self.id2label[idx]
                  )
              )
      else:
          self.labels = []
          self.num_classes = 1
  
imports: from typing import Dict, List, Optional
  import torch
  from nemo.collections.asr.parts.preprocessing.feature_loader import ExternalFeatureLoader
  from nemo.collections.common.parts.preprocessing import collections
  from nemo.core.classes import Dataset
  from nemo.core.neural_types import AcousticEncodedRepresentation, LabelsType, LengthsType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 2 to create a new FeatureToLabelDataset object from inherited base classes: Dataset with example: obj = FeatureToLabelDataset()