class_docstr: Dataset that loads tensors via a json file containing paths to feature
  files and sequence of speakers. Each new line is a
  different sample. Example below:
  {"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): Path to manifest json as described above. Canbe comma-separated paths.
      labels (Optional[list]): String containing all the possible labels to map to
          if None then automatically picks from ASRFeatureSequenceLabel collection.
      feature_loader, Feature load to loader (external) feature.
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code: def _collate_fn(self, batch):
      return _feature_collate_fn(batch)
  
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 FeatureToSeqSpeakerLabelDataset obj's underlying member method code with example usage: obj._collate_fn(batch) and returns: _feature_collate_fn