input: Write a function to create a Python neural network model that takes in a given trajectory of stock prices and predicts the stock price for the next day. The model should be built using TensorFlow and its Keras API. If the trajectory is empty, the function should return `None`.
  assert create_model().__class__.__name__ == 'Sequential'            
  assert create_model().predict(np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]])) is not None
  assert create_model().predict(np.array([[]])) is None
output: def create_model():
      if not np.array([]).size:
          return None
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(64, activation='relu', input_shape=(None, 1)),
          tf.keras.layers.Dense(32, activation='relu'),
          tf.keras.layers.Dense(1)
      ])
      model.compile(optimizer='adam', loss='mean_squared_error')
      return model