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 __init__(
      self,
      max_document_length,
      min_frequency=0,
      vocabulary=None,
      tokenizer_fn=None,
  ):
      from tensorflow.contrib.learn.python.learn.preprocessing.text import (
          VocabularyProcessor as _VocabularyProcessor,
      )
  
      self.__dict__["_vocabulary_processor"] = _VocabularyProcessor(
          max_document_length, min_frequency, vocabulary, tokenizer_fn
      )
  
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 1 to create a new VocabularyProcessor object from inherited base classes: object with example: obj = VocabularyProcessor(max_document_length, min_frequency, vocabulary, tokenizer_fn)