class_docstr: DEPRECATED.
code: def fit_on_texts(self, texts):
      for text in texts:
          self.document_count += 1
          if self.char_level or isinstance(text, list):
              if self.lower:
                  if isinstance(text, list):
                      text = [text_elem.lower() for text_elem in text]
                  else:
                      text = text.lower()
              seq = text
          else:
              if self.analyzer is None:
                  seq = text_to_word_sequence(
                      text,
                      filters=self.filters,
                      lower=self.lower,
                      split=self.split,
                  )
              else:
                  seq = self.analyzer(text)
          for w in seq:
              if w in self.word_counts:
                  self.word_counts[w] += 1
              else:
                  self.word_counts[w] = 1
          for w in set(seq):
              # In how many documents each word occurs
              self.word_docs[w] += 1
  
      wcounts = list(self.word_counts.items())
      wcounts.sort(key=lambda x: x[1], reverse=True)
      # forcing the oov_token to index 1 if it exists
      if self.oov_token is None:
          sorted_voc = []
      else:
          sorted_voc = [self.oov_token]
      sorted_voc.extend(wc[0] for wc in wcounts)
  
      # note that index 0 is reserved, never assigned to an existing word
      self.word_index = dict(
          zip(sorted_voc, list(range(1, len(sorted_voc) + 1)))
      )
  
      self.index_word = {c: w for w, c in self.word_index.items()}
  
      for w, c in list(self.word_docs.items()):
          self.index_docs[self.word_index[w]] = c
  
imports: import collections
  import hashlib
  import json
  import warnings
  import numpy
  from keras.api_export import keras_export
label_desc: Use image node_id 2 for calling the Tokenizer obj's underlying member method code with example usage: obj.fit_on_texts(texts) without return types