class_docstr: DEPRECATED.
code: def sequences_to_matrix(self, sequences, mode="binary"):
      if not self.num_words:
          if self.word_index:
              num_words = len(self.word_index) + 1
          else:
              raise ValueError(
                  "Specify a dimension (`num_words` argument), "
                  "or fit on some text data first."
              )
      else:
          num_words = self.num_words
  
      if mode == "tfidf" and not self.document_count:
          raise ValueError(
              "Fit the Tokenizer on some data before using tfidf mode."
          )
  
      x = np.zeros((len(sequences), num_words))
      for i, seq in enumerate(sequences):
          if not seq:
              continue
          counts = collections.defaultdict(int)
          for j in seq:
              if j >= num_words:
                  continue
              counts[j] += 1
          for j, c in list(counts.items()):
              if mode == "count":
                  x[i][j] = c
              elif mode == "freq":
                  x[i][j] = c / len(seq)
              elif mode == "binary":
                  x[i][j] = 1
              elif mode == "tfidf":
                  # Use weighting scheme 2 in
                  # https://en.wikipedia.org/wiki/Tf%E2%80%93idf
                  tf = 1 + np.log(c)
                  idf = np.log(
                      1
                      + self.document_count
                      / (1 + self.index_docs.get(j, 0))
                  )
                  x[i][j] = tf * idf
              else:
                  raise ValueError("Unknown vectorization mode:", mode)
      return x
  
imports: import collections
  import hashlib
  import json
  import warnings
  import numpy
  from keras.api_export import keras_export
label_desc: Use image node_id 9 for calling the Tokenizer obj's underlying member method code with example usage: obj.sequences_to_matrix(sequences, mode) and returns: x