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 1 0 0 1"}
      ...
      {"feature_filepath": "/path/to/audio_feature.pt", "label": "0 1 0 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
      delimiter (str): delimiter to split the labels.
      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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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."""
      output_types = {
          "audio_feat": NeuralType(
              ("B", "D", "T"), AcousticEncodedRepresentation()
          ),
          "feat_length": NeuralType(tuple("B"), LengthsType()),
          "labels": NeuralType(("B", "T"), LabelsType()),
          "labels_length": NeuralType(tuple("B"), LengthsType()),
      }
  
      return output_types
  
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 1 for calling the FeatureToMultiLabelDataset obj's underlying member method code with example usage: obj.output_types() and returns: output_types