input: Write a python function to build and train a CNN model using TensorFlow on the MNIST dataset.
  assert isinstance(build_and_train_cnn_model(), tf.keras.Model)
  assert build_and_train_cnn_model().layers[1].output_shape == (None, 26, 26, 32)
  assert build_and_train_cnn_model().layers[-1].units == 10
output: def build_and_train_cnn_model():
      import tensorflow as tf
      from tensorflow.keras import layers, models
      from tensorflow.keras.datasets import mnist
      from tensorflow.keras.utils import to_categorical
  
      # Load and preprocess the data
      (train_images, train_labels), (test_images, test_labels) = 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
      train_labels = to_categorical(train_labels)
      test_labels = to_categorical(test_labels)
  
      # Build the model
      model = models.Sequential()
      model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
      model.add(layers.MaxPooling2D((2, 2)))
      model.add(layers.Conv2D(64, (3, 3), activation='relu'))
      model.add(layers.MaxPooling2D((2, 2)))
      model.add(layers.Conv2D(64, (3, 3), activation='relu'))
      model.add(layers.Flatten())
      model.add(layers.Dense(64, activation='relu'))
      model.add(layers.Dense(10, activation='softmax'))
  
      # Compile the model
      model.compile(optimizer='adam',
                    loss='categorical_crossentropy',
                    metrics=['accuracy'])
  
      # Train the model
      model.fit(train_images, train_labels, epochs=5, batch_size=64, validation_split=0.2)
  
      return model