code: def define_style_transfer_network(
      content_image,
      tf_index,
      style_image,
      weight_dict,
      define_training_graph=False,
  ):
      """
      This function defines the style transfer network using the tensorflow nn api.
  
      Parameters
      ----------
      content_image: tensorflow.Tensor
          The content image to the network. The image is expected to be in RGB
          format.
      tf_index: tensorflow.Tensor
          The index tensor to the network.
      style_image: tensorflow.Tensor
          The content image to the network. The image is expected to be in RGB
          format.
      weights: dictionary
          The dictionary of weights to the network. The naming convention
          used is that from the CoreML export of the Style Transfer Network.
      define_training_graph: boolean
          If `true` both the training graph and the inference graph are returned.
          If `false` only the inference graph is returned.
      Returns
      -------
      optimizer: tensorflow.Tensor | None
          The training output tensor to the network.
      total_loss: tensorflow.Tensor | None
          The loss output tensor to the network.
      content_output: tensorflow.Tensor
          The content image output tensor to the network.
      """
  
      content_output = define_resnet(
          content_image, tf_index, weight_dict
      )
  
      optimizer = None
  
      if define_training_graph:
          pre_processing_output = define_vgg_pre_processing(
              content_output
          )
          (
              output_relu_1,
              output_relu_2,
              output_relu_3,
              output_relu_4,
          ) = define_vgg16(pre_processing_output, weight_dict)
  
          pre_processing_style = define_vgg_pre_processing(style_image)
          (
              style_relu_1,
              style_relu_2,
              style_relu_3,
              style_relu_4,
          ) = define_vgg16(pre_processing_style, weight_dict)
  
          pre_processing_content = define_vgg_pre_processing(
              content_image
          )
          _, _, content_relu_3, _ = define_vgg16(
              pre_processing_content, weight_dict
          )
  
          gram_output_relu_1 = define_gram_matrix(output_relu_1)
          gram_output_relu_2 = define_gram_matrix(output_relu_2)
          gram_output_relu_3 = define_gram_matrix(output_relu_3)
          gram_output_relu_4 = define_gram_matrix(output_relu_4)
  
          gram_style_relu_1 = define_gram_matrix(style_relu_1)
          gram_style_relu_2 = define_gram_matrix(style_relu_2)
          gram_style_relu_3 = define_gram_matrix(style_relu_3)
          gram_style_relu_4 = define_gram_matrix(style_relu_4)
  
          # L2 Loss Between the Nodes
          _tf = _lazy_import_tensorflow()
  
          style_loss_1 = _tf.losses.mean_squared_error(
              gram_style_relu_1, gram_output_relu_1, weights=1e-4
          )
          style_loss_2 = _tf.losses.mean_squared_error(
              gram_style_relu_2, gram_output_relu_2, weights=1e-4
          )
          style_loss_3 = _tf.losses.mean_squared_error(
              gram_style_relu_3, gram_output_relu_3, weights=1e-4
          )
          style_loss_4 = _tf.losses.mean_squared_error(
              gram_style_relu_4, gram_output_relu_4, weights=1e-4
          )
  
          content_loss = _tf.losses.mean_squared_error(
              content_relu_3, output_relu_3
          )
          style_loss = (
              style_loss_1 + style_loss_2 + style_loss_3 + style_loss_4
          )
  
          total_loss = ((content_loss + style_loss) / 10000.0) * 0.5
  
          optimizer = _tf.train.AdamOptimizer().minimize(total_loss)
  
          return optimizer, total_loss, content_output
      else:
          return None, None, content_output
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  from turicreate.toolkits._tf_model import TensorFlowModel
  import numpy
  import turicreate.toolkits._tf_utils
  from turicreate._deps.minimal_package import _minimal_package_import_check
label_desc: Use image node_id 9 for calling a global function with example usage: define_style_transfer_network(content_image, tf_index, style_image, weight_dict, define_training_graph) and returns: optimizer,  total_loss,  content_output, None,  None,  content_output