class_docstr: Vocabulary Processor.
  
  Maps documents to sequences of word ids.
  
  Arguments:
      max_document_length: Maximum length of documents.
          if documents are longer, they will be trimmed, if shorter - padded.
      min_frequency: Minimum frequency of words in the vocabulary.
      vocabulary: CategoricalVocabulary object.
  
  Attributes:
      vocabulary_: CategoricalVocabulary object.
class_docstr_tok: ["Vocabulary","Processor",".","Maps","documents","to","sequences","of","word","ids",".","Arguments",":","max_document_length",":","Maximum","length","of","documents",".","if","documents","are","longer",",","they","will","be","trimmed",",","if","shorter","-","padded",".","min_frequency",":","Minimum","frequency","of","words","in","the","vocabulary",".","vocabulary",":","CategoricalVocabulary","object",".","Attributes",":","vocabulary_",":","CategoricalVocabulary","object","."]
code: def __setattr__(self, key, value):
      setattr(self._vocabulary_processor, key, value)
  
imports: from __future__ import division, print_function, absolute_import
  import os
  import random
  import numpy
  from PIL import Image
  import pickle
  import csv
  import warnings
  import tensorflow.compat.v1
  from io import BytesIO
label_desc: Use image node_id 3 for calling the VocabularyProcessor obj's underlying member method code with example usage: obj.__setattr__(key, value) without return types