input: def construct_mnist_classifier() -> keras.Model:
      """ Constructs a neural network model in Python using Keras to classify the MNIST data set correctly.
      The model should have an input layer of shape (784,), a hidden layer with 128 units and ReLU activation,
      and an output layer with 10 units and Softmax activation. The model should be compiled using
      Adam optimizer, sparse categorical crossentropy loss, and accuracy as the metric.
      
      Example:
      >>> model = construct_mnist_classifier()
      >>> model.summary()
      """
output:     model = keras.Sequential([
          keras.layers.Input(shape=(784,)),
          keras.layers.Dense(128, activation='relu'),
          keras.layers.Dense(10, activation='softmax')
      ])
      model.compile(optimizer='adam',
                    loss='sparse_categorical_crossentropy',
                    metrics=['accuracy'])
      return model