input: Write a function to construct and compile a neural network model in Python using Keras to classify the MNIST data set correctly. The model should include an input layer, one hidden layer with ReLU activation, and an output layer with softmax activation. Then, compile the model using the Adam optimizer and sparse categorical crossentropy loss.
  
  assert construct_mnist_model().summary() == "...(summary of the model)..."
  assert type(construct_mnist_model()) == keras.engine.functional.Functional
output: def construct_mnist_model():
      model = keras.Sequential([
          keras.layers.InputLayer(input_shape=(28, 28)),
          keras.layers.Flatten(),
          keras.layers.Dense(128, activation='relu'),
          keras.layers.Dense(10, activation='softmax')
      ])
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model