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.
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docstr: fit_transform.
  
  Learn the vocabulary dictionary and return indices of words.
  
  Arguments:
      raw_documents: An iterable which yield either str or unicode.
      unused_y: to match fit_transform signature of estimators.
  
  Returns:
      X: iterable, [n_samples, max_document_length] Word-id matrix.
docstr_tok: ["fit_transform",".","Learn","the","vocabulary","dictionary","and","return","indices","of","words",".","Arguments",":","raw_documents",":","An","iterable","which","yield","either","str","or","unicode",".","unused_y",":","to","match","fit_transform","signature","of","estimators",".","Returns",":","X",":","iterable",",","[","n_samples",",","max_document_length","]","Word-id","matrix","."]
code: def fit_transform(self, raw_documents, unused_y=None):
      """fit_transform.
  
      Learn the vocabulary dictionary and return indices of words.
  
      Arguments:
          raw_documents: An iterable which yield either str or unicode.
          unused_y: to match fit_transform signature of estimators.
  
      Returns:
          X: iterable, [n_samples, max_document_length] Word-id matrix.
      """
      return self._vocabulary_processor.fit_transform(
          raw_documents, unused_y
      )
  
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 5 for calling the VocabularyProcessor obj's underlying member method code with example usage: obj.fit_transform(raw_documents, unused_y) and returns: self