input: Write a function to construct a neural network model in Python using Keras to classify the MNIST data set correctly. The function should return the compiled model.
  assert type(construct_mnist_model())==keras.engine.functional.Functional
  assert construct_mnist_model().layers[0].input_shape==[(None, 784)]
  assert construct_mnist_model().layers[1].output_shape==(None, 128)
  assert construct_mnist_model().layers[2].output_shape==(None, 10)
  assert construct_mnist_model().optimizer.__class__.__name__=='Adam'
output: def construct_mnist_model():
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.optimizers import Adam
      
      model = Sequential([
          Dense(128, activation='relu', input_shape=(784,)),
          Dense(10, activation='softmax')
      ])
      
      model.compile(optimizer=Adam(), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model