class_docstr: Sequence token classification Processor dataset loader.
  Loads a directory with train.txt/test.txt/dev.txt files in tab separeted
  format (one token per line - conll style).
  Label dictionary is given in labels.txt file.
class_docstr_tok: ["Sequence","token","classification","Processor","dataset","loader",".","Loads","a","directory","with","train.txt\/test.txt\/dev.txt","files","in","tab","separeted","format","(","one","token","per","line","-","conll","style",")",".","Label","dictionary","is","given","in","labels.txt","file","."]
code: def get_labels(self):
      if self.labels is not None:
          return self.labels
  
      f_path = self.data_dir + os.sep + "labels.txt"
      if not os.path.exists(f_path):
          logger.error(
              "Labels file (labels.txt) not found in {}".format(
                  self.data_dir
              )
          )
          raise FileNotFoundError
  
      self.labels = []
      with open(f_path, encoding="utf-8") as fp:
          self.labels = [line.strip() for line in fp.readlines()]
  
      return self.labels
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import os
  from os import path
  from typing import List
  import numpy
  from nlp_architect.data.utils import DataProcessor, InputExample, read_column_tagged_file
  from nlp_architect.utils.generic import pad_sentences
  from nlp_architect.utils.io import validate_existing_directory, validate_existing_filepath
  from nlp_architect.utils.text import character_vector_generator, read_sequential_tagging_file, word_vector_generator
  from nlp_architect.utils.text import Vocabulary
label_desc: Use image node_id 6 for calling the TokenClsProcessor obj's underlying member method code with example usage: obj.get_labels() and returns: self, self