input: Write a function to create a convolutional neural network model using Keras.
  assert isinstance(create_model(), keras.Model)
  assert create_model().input_shape == (None, 28, 28, 1)
  assert create_model().output_shape == (None, 10)
output: def create_model():
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
      
      model = Sequential()
      model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
      model.add(MaxPooling2D((2, 2)))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D((2, 2)))
      model.add(Flatten())
      model.add(Dense(128, activation='relu'))
      model.add(Dense(10, activation='softmax'))
      
      return model