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: transform.
  
  Transform documents to word-id matrix.
  
  Convert words to ids with vocabulary fitted with fit or the one
  provided in the constructor.
  
  Arguments:
      raw_documents: An iterable which yield either str or unicode.
  
  Yields:
      X: iterable, [n_samples, max_document_length] Word-id matrix.
docstr_tok: ["transform",".","Transform","documents","to","word-id","matrix",".","Convert","words","to","ids","with","vocabulary","fitted","with","fit","or","the","one","provided","in","the","constructor",".","Arguments",":","raw_documents",":","An","iterable","which","yield","either","str","or","unicode",".","Yields",":","X",":","iterable",",","[","n_samples",",","max_document_length","]","Word-id","matrix","."]
code: def transform(self, raw_documents):
      """transform.
  
      Transform documents to word-id matrix.
  
      Convert words to ids with vocabulary fitted with fit or the one
      provided in the constructor.
  
      Arguments:
          raw_documents: An iterable which yield either str or unicode.
  
      Yields:
          X: iterable, [n_samples, max_document_length] Word-id matrix.
      """
      return self._vocabulary_processor.transform(raw_documents)
  
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 6 for calling the VocabularyProcessor obj's underlying member method code with example usage: obj.transform(raw_documents) and returns: self