input: Write a function called `recognize_handwritten_digits` that uses a convolutional neural network to recognize handwritten digits. The function should load the MNIST dataset, preprocess it, construct a neural network model, train it, and finally evaluate its performance on the test dataset.
  
  assert recognize_handwritten_digits() is not None
output: def recognize_handwritten_digits():
      import tensorflow as tf
      from tensorflow.keras import datasets, layers, models
  
      (train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()
      train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
      test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255
  
      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='sparse_categorical_crossentropy',
                    metrics=['accuracy'])
  
      model.fit(train_images, train_labels, epochs=5)
  
      test_loss, test_acc = model.evaluate(test_images, test_labels)
      return test_acc