code: def zero_add(previous_value, x, name=None, reuse=None):
    
    with tf.variable_scope(name, default_name="zero_add", reuse=reuse):
      gamma = tf.get_variable("gamma", (), initializer=tf.zeros_initializer())
      return previous_value + gamma * x
docs: Resnet connection with zero initialization.
  
    Another type of resnet connection which returns previous_value + gamma * x.
    gamma is a trainable scalar and initialized with zero. It is useful when a
    module is plugged into a trained model and we want to make sure it matches the
    original model's performance.
  
    Args:
      previous_value:  A tensor.
      x: A tensor.
      name: name of variable scope; defaults to zero_add.
      reuse: reuse scope.
  
    Returns:
      previous_value + gamma * x.