parameters: (add_regularization_losses=True, name="total_loss")
return_statement: return math_ops.add_n(losses, name=name)
docstring: Returns a tensor whose value represents the total loss.
  
    Notice that the function adds the given losses to the regularization losses.
  
    Args:
      add_regularization_losses: A boolean indicating whether or not to use the
        regularization losses in the sum.
      name: The name of the returned tensor.
  
    Returns:
      A `Tensor` whose value represents the total loss.
  
    Raises:
      ValueError: if `losses` is not iterable.
docstring_summary: Returns a tensor whose value represents the total loss.
docstring_tokens: Returns a tensor whose value represents the total loss .
function: def get_total_loss(add_regularization_losses=True, name="total_loss"):
    """Returns a tensor whose value represents the total loss.
  
    Notice that the function adds the given losses to the regularization losses.
  
    Args:
      add_regularization_losses: A boolean indicating whether or not to use the
        regularization losses in the sum.
      name: The name of the returned tensor.
  
    Returns:
      A `Tensor` whose value represents the total loss.
  
    Raises:
      ValueError: if `losses` is not iterable.
    """
    losses = get_losses()
    if add_regularization_losses:
      losses += get_regularization_losses()
    return math_ops.add_n(losses, name=name)
function_tokens: def get_total_loss ( add_regularization_losses = True , name = "total_loss" ) : losses = get_losses ( ) if add_regularization_losses : losses += get_regularization_losses ( ) return math_ops . add_n ( losses , name = name )