code: def build_model(
      content_input_,
      style_input_,
      trainable,
      is_training,
      reuse=None,
      inception_end_point="Mixed_6e",
      style_prediction_bottleneck=100,
      adds_losses=True,
      content_weights=None,
      style_weights=None,
      total_variation_weight=None,
  ):
      """The image stylize function.
  
      Args:
        content_input_: Tensor. Batch of content input images.
        style_input_: Tensor. Batch of style input images.
        trainable: bool. Should the parameters be marked as trainable?
        is_training: bool. Is it training phase or not?
        reuse: bool. Whether to reuse model parameters. Defaults to False.
        inception_end_point: string. Specifies the endpoint to construct the
            inception_v3 network up to. This network is used for style prediction.
        style_prediction_bottleneck: int. Specifies the bottleneck size in the
            number of parameters of the style embedding.
        adds_losses: wheather or not to add objectives to the model.
        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.
  
      Returns:
        Tensor for the output of the transformer network, Tensor for the total loss,
        dict mapping loss names to losses, Tensor for the bottleneck activations of
        the style prediction network.
      """
      # Gets scope name and shape of the activations of transformer network which
      # will be used to apply style.
      [
          activation_names,
          activation_depths,
      ] = transformer_model.style_normalization_activations()
  
      # Defines the style prediction network.
      style_params, bottleneck_feat = style_prediction(
          style_input_,
          activation_names,
          activation_depths,
          is_training=is_training,
          trainable=trainable,
          inception_end_point=inception_end_point,
          style_prediction_bottleneck=style_prediction_bottleneck,
          reuse=reuse,
      )
  
      # Defines the style transformer network.
      stylized_images = transformer_model.transform(
          content_input_,
          normalizer_fn=ops.conditional_style_norm,
          reuse=reuse,
          trainable=trainable,
          is_training=is_training,
          normalizer_params={"style_params": style_params},
      )
  
      # Adds losses.
      loss_dict = {}
      total_loss = []
      if adds_losses:
          total_loss, loss_dict = losses.total_loss(
              content_input_,
              style_input_,
              stylized_images,
              content_weights=content_weights,
              style_weights=style_weights,
              total_variation_weight=total_variation_weight,
          )
  
      return stylized_images, total_loss, loss_dict, bottleneck_feat
  
file_path: magenta/magenta/models/arbitrary_image_stylization/arbitrary_image_stylization_build_model.py
imports: from magenta.models.arbitrary_image_stylization import arbitrary_image_stylization_losses
  from magenta.models.arbitrary_image_stylization import nza_model
  from magenta.models.image_stylization import ops
  import tensorflow.compat.v1
  import tf_slim
  from tf_slim.nets import inception_v3
label_desc: Use image node_id 1 for calling a global function with example usage: build_model(content_input_, style_input_, trainable, is_training, reuse, inception_end_point, style_prediction_bottleneck, adds_losses, content_weights, style_weights, total_variation_weight) and returns: stylized_images,  total_loss,  loss_dict,  bottleneck_feat