input: Write a python function to create a sequential neural network model with three layers where the first layer has 16 neurons, the second layer has 8 neurons, and the output layer has 1 neuron. Use ReLU activation for the first two layers and sigmoid activation for the output layer. The function should also compile the model using 'adam' optimizer and 'binary_crossentropy' loss.
  assert create_model().layers[0].units == 16
  assert create_model().layers[1].units == 8
  assert create_model().layers[-1].activation.__name__ == 'sigmoid'
  assert create_model().loss == 'binary_crossentropy'
  assert create_model().optimizer.__class__.__name__ == 'Adam'
output: def create_model():
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.optimizers import Adam
  
      model = Sequential()
      model.add(Dense(16, activation='relu', input_shape=(None, 16)))
      model.add(Dense(8, activation='relu'))
      model.add(Dense(1, activation='sigmoid'))
      
      model.compile(optimizer='adam', loss='binary_crossentropy')
      
      return model