code: def total_loss(
      content_inputs,
      style_inputs,
      stylized_inputs,
      content_weights,
      style_weights,
      total_variation_weight,
      reuse=False,
  ):
      """Computes the total loss function.
  
      The total loss function is composed of a content, a style and a total
      variation term.
  
      Args:
        content_inputs: Tensor. The input images.
        style_inputs: Tensor. The input images.
        stylized_inputs: Tensor. The stylized input images.
        content_weights: dict mapping layer names to their associated content loss
            weight. Keys that are missing from the dict won't have their content
            loss computed.
        style_weights: dict mapping layer names to their associated style loss
            weight. Keys that are missing from the dict won't have their style
            loss computed.
        total_variation_weight: float. Coefficient for the total variation part of
            the loss.
        reuse: bool. Whether to reuse model parameters. Defaults to False.
  
      Returns:
        Tensor for the total loss, dict mapping loss names to losses.
      """
      # Propagate the input and its stylized version through VGG16.
      with tf.name_scope("content_endpoints"):
          content_end_points = vgg.vgg_16(content_inputs, reuse=reuse)
      with tf.name_scope("style_endpoints"):
          style_end_points = vgg.vgg_16(style_inputs, reuse=True)
      with tf.name_scope("stylized_endpoints"):
          stylized_end_points = vgg.vgg_16(stylized_inputs, reuse=True)
  
      # Compute the content loss
      with tf.name_scope("content_loss"):
          total_content_loss, content_loss_dict = content_loss(
              content_end_points, stylized_end_points, content_weights
          )
  
      # Compute the style loss
      with tf.name_scope("style_loss"):
          total_style_loss, style_loss_dict = style_loss(
              style_end_points, stylized_end_points, style_weights
          )
  
      # Compute the total variation loss
      with tf.name_scope("total_variation_loss"):
          (
              tv_loss,
              total_variation_loss_dict,
          ) = learning_utils.total_variation_loss(
              stylized_inputs, total_variation_weight
          )
  
      # Compute the total loss
      with tf.name_scope("total_loss"):
          loss = total_content_loss + total_style_loss + tv_loss
  
      loss_dict = {"total_loss": loss}
      loss_dict.update(content_loss_dict)
      loss_dict.update(style_loss_dict)
      loss_dict.update(total_variation_loss_dict)
  
      return loss, loss_dict
  
imports: from magenta.models.image_stylization import learning
  from magenta.models.image_stylization import vgg
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: total_loss(content_inputs, style_inputs, stylized_inputs, content_weights, style_weights, total_variation_weight, reuse) and returns: loss,  loss_dict