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_test_examples(self, filename="test.txt"):
      return self._read_examples(self.data_dir, filename, "test")
  
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 5 for calling the TokenClsProcessor obj's underlying member method code with example usage: obj.get_test_examples(filename) and returns: self