class_docstr: Dataset that loads tensors via a json file containing paths to feature files, sequences of 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/feature_0.p", "seq_label": speakerA speakerB SpeakerA ....}         ...
      {"feature_filepath": "/path/to/feature_n.p", "seq_label": target_seq_label_n} 
  target_seq_label_n is the string of sequence of speaker label, separated by space.
  
  Args:
      manifest_filepath (str): Dataset parameter. Path to JSON containing data.
      labels (Optional[list]): Dataset parameter. List of unique labels collected from all samples.
      feature_loader : Dataset parameter. Feature loader to load (external) feature.       
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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."""
      # TODO output type for external features
      output_types = {
          "external_feat": NeuralType(
              ("B", "D", "T"), AcousticEncodedRepresentation()
          ),
          "feat_length": NeuralType(tuple("B"), LengthsType()),
      }
  
      if self.is_speaker_emb:
          output_types.update(
              {
                  "embs": NeuralType(
                      ("B", "D", "T"), AcousticEncodedRepresentation()
                  ),
                  "embs_length": NeuralType(tuple("B"), LengthsType()),
                  "label": NeuralType(("B", "T"), LabelsType()),
                  "label_length": NeuralType(tuple("B"), LengthsType()),
              }
          )
      else:
          output_types.update(
              {
                  "label": NeuralType(("B", "T"), LabelsType()),
                  "label_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 _FeatureSeqSpeakerLabelDataset obj's underlying member method code with example usage: obj.output_types() and returns: output_types