input: 
  def construct_mnist_model() -> keras.Model:
      """ Construct a neural network model in Python to classify the MNIST data set correctly.
      The model includes an input layer with 784 inputs, a hidden layer with 128 units using ReLU activation,
      and an output layer with 10 units using Softmax activation. The model is compiled with the Adam optimizer,
      sparse_categorical_crossentropy loss, and accuracy metric.
  
      >>> model = construct_mnist_model()
      >>> isinstance(model, keras.Model)
      True
      >>> model.optimizer.get_config()['name']
      'adam'
      >>> model.loss
      'sparse_categorical_crossentropy'
      """
  
output:     
      inputs = keras.Input(shape=(784,))
      hidden = keras.layers.Dense(128, activation='relu')(inputs)
      outputs = keras.layers.Dense(10, activation='softmax')(hidden)
      model = keras.Model(inputs=inputs, outputs=outputs)
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model
  