input: Write a python function to create and compile a Convolutional Neural Network for MNIST dataset. 
  assert create_compile_cnn().summary() is not None
output: def create_compile_cnn():
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
      from tensorflow.keras.optimizers import Adam
  
      model = Sequential([
          Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
          MaxPooling2D((2, 2)),
          Conv2D(64, (3, 3), activation='relu'),
          MaxPooling2D((2, 2)),
          Conv2D(64, (3, 3), activation='relu'),
          Flatten(),
          Dense(64, activation='relu'),
          Dense(10, activation='softmax')
      ])
  
      model.compile(optimizer=Adam(),
                    loss='sparse_categorical_crossentropy',
                    metrics=['accuracy'])
  
      return model