input: Write a function named `recognize_handwritten_digits` that takes no input parameters. The function should generate a Machine Learning model in Python using TensorFlow and Keras, which can recognize handwritten digits from 0 to 9. The function should compile the model with categorical cross-entropy loss, Adam optimizer, and accuracy metrics. The model should be trained on the MNIST dataset for 5 epochs with a batch size of 128. The function does not need to return anything.
  
  assert recognize_handwritten_digits() is None
output: def recognize_handwritten_digits():
      import tensorflow as tf
      from tensorflow.keras import layers, models
      from tensorflow.keras.datasets import mnist
      from tensorflow.keras.utils import to_categorical
  
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
      x_train = x_train.reshape((60000, 28, 28, 1)).astype('float32') / 255
      x_test = x_test.reshape((10000, 28, 28, 1)).astype('float32') / 255
      y_train = to_categorical(y_train)
      y_test = to_categorical(y_test)
  
      model = models.Sequential([
          layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
          layers.MaxPooling2D((2, 2)),
          layers.Conv2D(64, (3, 3), activation='relu'),
          layers.MaxPooling2D((2, 2)),
          layers.Conv2D(64, (3, 3), activation='relu'),
          layers.Flatten(),
          layers.Dense(64, activation='relu'),
          layers.Dense(10, activation='softmax')
      ])
  
      model.compile(optimizer='adam',
                    loss='categorical_crossentropy',
                    metrics=['accuracy'])
  
      model.fit(x_train, y_train, epochs=5, batch_size=128)