input: Write a function to construct a neural network model in Python using Keras to classify the MNIST dataset correctly. The function should return the compiled model object. The input layer should have 784 neurons, the hidden layer should have 128 neurons with ReLU activation, and the output layer should have 10 neurons with softmax activation. The model should be compiled using the Adam optimizer, sparse categorical crossentropy loss, and accuracy as the metric.
  assert isinstance(construct_mnist_model(), keras.Model)
  assert construct_mnist_model().input_shape == (None, 784)
  assert construct_mnist_model().output_shape == (None, 10)
output: def construct_mnist_model():
      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