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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code: def __getitem__(self, index):
      sample = self.collection[index]
  
      features = self.feature_loader.process(sample.feature_file)
      f, fl = features, torch.tensor(features.shape[0]).long()
  
      t = torch.tensor(sample.seq_label).float()
      tl = torch.tensor(len(sample.seq_label)).long()
  
      return f, fl, t, tl
  
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 4 for calling the _FeatureSeqSpeakerLabelDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: f,  fl,  t,  tl