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 _read_examples(self, data_dir, file_name, set_name):
      if not os.path.exists(data_dir + os.sep + file_name):
          logger.error(
              "Requested file {} in path {} for TokenClsProcess not found".format(
                  file_name, data_dir
              )
          )
          return None
      return self._create_examples(
          read_column_tagged_file(
              os.path.join(data_dir, file_name),
              tag_col=self.tag_col,
              ignore_token=self.ignore_token,
          ),
          set_name,
      )
  
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 2 for calling the TokenClsProcessor obj's underlying member method code with example usage: obj._read_examples(data_dir, file_name, set_name) and returns: self, None