input: Write a python function to create a simple neural network using Keras with one hidden layer and compile it.
  assert isinstance(create_model(), keras.Model)
  assert create_model().layers[0].input_shape == (None, 784)
  assert create_model().layers[1].units == 128
  assert create_model().layers[2].units == 10
output: from keras.models import Sequential
  from keras.layers import Dense
  from keras import Model
  
  def create_model():
      model = Sequential()
      model.add(Dense(128, activation='relu', input_shape=(784,)))
      model.add(Dense(10, activation='softmax'))
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model