class_docstr: Text tokenization utility class.
  
  This class allows to vectorize a text corpus, by turning each
  text into either a sequence of integers (each integer being the index
  of a token in a dictionary) or into a vector where the coefficient
  for each token could be binary, based on word count, based on tf-idf...
  
  # Arguments
      num_words: the maximum number of words to keep, based
          on word frequency. Only the most common `num_words-1` words will
          be kept.
      filters: a string where each element is a character that will be
          filtered from the texts. The default is all punctuation, plus
          tabs and line breaks, minus the `'` character.
      lower: boolean. Whether to convert the texts to lowercase.
      split: str. Separator for word splitting.
      char_level: if True, every character will be treated as a token.
      oov_token: if given, it will be added to word_index and used to
          replace out-of-vocabulary words during text_to_sequence calls
      analyzer: function. Custom analyzer to split the text.
          The default analyzer is text_to_word_sequence
  
  By default, all punctuation is removed, turning the texts into
  space-separated sequences of words
  (words maybe include the `'` character). These sequences are then
  split into lists of tokens. They will then be indexed or vectorized.
  
  `0` is a reserved index that won't be assigned to any word.
class_docstr_tok: ["Text","tokenization","utility","class",".","This","class","allows","to","vectorize","a","text","corpus",",","by","turning","each","text","into","either","a","sequence","of","integers","(","each","integer","being","the","index","of","a","token","in","a","dictionary",")","or","into","a","vector","where","the","coefficient","for","each","token","could","be","binary",",","based","on","word","count",",","based","on","tf-idf","...","#","Arguments","num_words",":","the","maximum","number","of","words","to","keep",",","based","on","word","frequency",".","Only","the","most","common","`","num_words-1","`","words","will","be","kept",".","filters",":","a","string","where","each","element","is","a","character","that","will","be","filtered","from","the","texts",".","The","default","is","all","punctuation",",","plus","tabs","and","line","breaks",",","minus","the","`","'","`","character",".","lower",":","boolean",".","Whether","to","convert","the","texts","to","lowercase",".","split",":","str",".","Separator","for","word","splitting",".","char_level",":","if","True",",","every","character","will","be","treated","as","a","token",".","oov_token",":","if","given",",","it","will","be","added","to","word_index","and","used","to","replace","out-of-vocabulary","words","during","text_to_sequence","calls","analyzer",":","function",".","Custom","analyzer","to","split","the","text",".","The","default","analyzer","is","text_to_word_sequence","By","default",",","all","punctuation","is","removed",",","turning","the","texts","into","space-separated","sequences","of","words","(","words","maybe","include","the","`","'","`","character",")",".","These","sequences","are","then","split","into","lists","of","tokens",".","They","will","then","be","indexed","or","vectorized",".","`","0","`","is","a","reserved","index","that","wo","n't","be","assigned","to","any","word","."]
docstr: Transforms each text in texts to a sequence of integers.
  
  Only top `num_words-1` most frequent words will be taken into account.
  Only words known by the tokenizer will be taken into account.
  
  # Arguments
      texts: A list of texts (strings).
  
  # Returns
      A list of sequences.
docstr_tok: ["Transforms","each","text","in","texts","to","a","sequence","of","integers",".","Only","top","`","num_words-1","`","most","frequent","words","will","be","taken","into","account",".","Only","words","known","by","the","tokenizer","will","be","taken","into","account",".","#","Arguments","texts",":","A","list","of","texts","(","strings",")",".","#","Returns","A","list","of","sequences","."]
code: def texts_to_sequences(self, texts):
      """Transforms each text in texts to a sequence of integers.
  
      Only top `num_words-1` most frequent words will be taken into account.
      Only words known by the tokenizer will be taken into account.
  
      # Arguments
          texts: A list of texts (strings).
  
      # Returns
          A list of sequences.
      """
      return list(self.texts_to_sequences_generator(texts))
  
imports: import json
  import warnings
  from collections import OrderedDict, defaultdict
  from hashlib import md5
  import numpy
label_desc: Use image node_id 4 for calling the Tokenizer obj's underlying member method code with example usage: obj.texts_to_sequences(texts) and returns: list