class_docstr: Sequential tagging dataset loader.
  Loads train/test files with tabular separation.
  
  Args:
      train_file (str): path to train file
      test_file (str): path to test file
      max_sentence_length (int, optional): max sentence length
      max_word_length (int, optional): max word length
      tag_field_no (int, optional): index of column to use a y-samples
class_docstr_tok: ["Sequential","tagging","dataset","loader",".","Loads","train\/test","files","with","tabular","separation",".","Args",":","train_file","(","str",")",":","path","to","train","file","test_file","(","str",")",":","path","to","test","file","max_sentence_length","(","int",",","optional",")",":","max","sentence","length","max_word_length","(","int",",","optional",")",":","max","word","length","tag_field_no","(","int",",","optional",")",":","index","of","column","to","use","a","y-samples"]
code: def _read_file(self, filepath):
      with open(filepath, encoding="utf-8") as fp:
          data = fp.readlines()
          data = [d.strip() for d in data]
          sentences = self._split_into_sentences(data)
          parsed_sentences = [
              self._parse_sentence(s) for s in sentences if len(s) > 0
          ]
      return parsed_sentences
  
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 9 for calling the SequentialTaggingDataset obj's underlying member method code with example usage: obj._read_file(filepath) and returns: parsed_sentences