class_docstr: A single training/test example for simple sequence token classification.
class_docstr_tok: ["A","single","training\/test","example","for","simple","sequence","token","classification","."]
docstr: Constructs a SequenceClassInputExample.
  Args:
      guid: Unique id for the example.
      text: string. The untokenized text of the sequence.
      tokens (List[str]): The list of tokens.
      shapes (List[str]): List of tokens shapes.
      label (List[str], optional): The tags of the tokens.
docstr_tok: ["Constructs","a","SequenceClassInputExample",".","Args",":","guid",":","Unique","id","for","the","example",".","text",":","string",".","The","untokenized","text","of","the","sequence",".","tokens","(","List","[","str","]",")",":","The","list","of","tokens",".","shapes","(","List","[","str","]",")",":","List","of","tokens","shapes",".","label","(","List","[","str","]",",","optional",")",":","The","tags","of","the","tokens","."]
code: def __init__(
      self,
      guid: str,
      text: str,
      tokens: List[str],
      shapes: List[int] = None,
      label: List[str] = None,
  ):
      """Constructs a SequenceClassInputExample.
      Args:
          guid: Unique id for the example.
          text: string. The untokenized text of the sequence.
          tokens (List[str]): The list of tokens.
          shapes (List[str]): List of tokens shapes.
          label (List[str], optional): The tags of the tokens.
      """
      super(TokenClsInputExample, self).__init__(guid, text, label)
      self.tokens = tokens
      self.shapes = shapes
  
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 1 to create a new TokenClsInputExample object from inherited base classes: InputExample with example: obj = TokenClsInputExample(guid, text, tokens, shapes, label)