input: def construct_neural_network() -> keras.Model:
      """ Construct a neural network model using Keras to classify the MNIST dataset correctly.
      The model should include an input layer, 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 metric.
      >>> model = construct_neural_network()
      >>> model.summary()
      """
  
output:     input_layer = keras.layers.Input(shape=(28, 28, 1))
      hidden_layer = keras.layers.Dense(128, activation='relu')(input_layer)
      output_layer = keras.layers.Dense(10, activation='softmax')(hidden_layer)
      
      model = keras.models.Model(inputs=input_layer, outputs=output_layer)
      
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      
      return model