input: 
  def construct_mnist_classification_model() -> keras.Model:
      """ Construct a neural network model using Keras to classify the MNIST dataset.
      The model should have an input layer with 784 neurons (for 28x28 images), 
      a hidden layer with 128 neurons and ReLU activation, and an output layer 
      with 10 neurons for the 10 classes and softmax activation.
      The model should then be compiled with the Adam optimizer and 
      sparse categorical cross-entropy loss.
  
      >>> model = construct_mnist_classification_model()
      >>> model.summary()
      """
      input_layer = keras.layers.Input(shape=(784,))
      hidden_layer = keras.layers.Dense(128, activation='relu')(input_layer)
      output_layer = keras.layers.Dense(10, activation='softmax')(hidden_layer)
      model = keras.Model(inputs=input_layer, outputs=output_layer)
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model
  
output:     input_layer = keras.layers.Input(shape=(784,))
      hidden_layer = keras.layers.Dense(128, activation='relu')(input_layer)
      output_layer = keras.layers.Dense(10, activation='softmax')(hidden_layer)
      model = keras.Model(inputs=input_layer, outputs=output_layer)
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model