code: def test_tokenizer():
      sample_texts = [
          "The cat sat on the mat.",
          "The dog sat on the log.",
          "Dogs and cats living together.",
      ]
      tokenizer = text.Tokenizer(num_words=10)
      tokenizer.fit_on_texts(sample_texts)
  
      sequences = []
      for seq in tokenizer.texts_to_sequences_generator(sample_texts):
          sequences.append(seq)
      assert np.max(np.max(sequences)) < 10
      assert np.min(np.min(sequences)) == 1
  
      tokenizer.fit_on_sequences(sequences)
  
      for mode in ["binary", "count", "tfidf", "freq"]:
          tokenizer.texts_to_matrix(sample_texts, mode)
  
imports: from collections import OrderedDict
  import numpy
  import pytest
  from tensorflow import keras
  from keras_preprocessing import text
label_desc: Use image node_id 4 for calling a global function with example usage: test_tokenizer() without return types