input: 
  def construct_mnist_model() -> keras.Model:
      """ Construct a neural network model using Keras to classify the MNIST data set.
      The model should have the following layers:
      - An input layer with shape (784,)
      - A hidden layer with 128 units and ReLU activation
      - An output layer with 10 units and softmax activation
      - The model should be compiled with the Adam optimizer, 
        sparse_categorical_crossentropy loss, and accuracy metric.
      >>> model = construct_mnist_model()
      >>> isinstance(model, keras.Model)
      True
      """
  
output:     inputs = keras.Input(shape=(784,))
      hidden_layer = keras.layers.Dense(128, activation='relu')(inputs)
      outputs = keras.layers.Dense(10, activation='softmax')(hidden_layer)
      model = keras.Model(inputs=inputs, outputs=outputs)
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model