input: 
  def construct_mnist_classifier() -> keras.models.Model:
      """ Construct a neural network model in Python to classify the MNIST data set correctly.
      The model should have an input layer with 784 neurons (corresponding to a flattened 28x28 image),
      a hidden layer with 128 neurons using the ReLU activation function, and an output layer with 10 neurons
      using the softmax activation function to classify digits 0-9.
      The model should be compiled using the Adam optimizer, sparse categorical crossentropy loss,
      and accuracy as the metric.
      >>> model = construct_mnist_classifier()
      >>> type(model)
      <class 'keras.engine.functional.Functional'>
      """
  
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