input: 
  def construct_mnist_model() -> keras.Model:
      """Construct a neural network model in Python to classify the MNIST data set correctly.
      >>> model = construct_mnist_model()
      >>> model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      >>> model.summary()
      """
      # Input layer
      input_layer = keras.layers.Input(shape=(784,))
      
      # Hidden layer
      hidden_layer = keras.layers.Dense(128, activation='relu')(input_layer)
      
      # Output layer
      output_layer = keras.layers.Dense(10, activation='softmax')(hidden_layer)
      
      # Model
      model = keras.Model(inputs=input_layer, outputs=output_layer)
      
      return model
  
output:     # Input layer
      input_layer = keras.layers.Input(shape=(784,))
      
      # Hidden layer
      hidden_layer = keras.layers.Dense(128, activation='relu')(input_layer)
      
      # Output layer
      output_layer = keras.layers.Dense(10, activation='softmax')(hidden_layer)
      
      # Model
      model = keras.Model(inputs=input_layer, outputs=output_layer)
      
      return model