code: def predict(self, feed_dict):
      for key in feed_dict.keys():
          feed_dict[key] = _utils.convert_shared_float_array_to_numpy(
              feed_dict[key]
          )
  
      with self.st_graph.as_default():
          stylized_image = self.sess.run(
              fetches=[self.output],
              feed_dict={
                  self.tf_input: feed_dict["input"],
                  self.tf_index: feed_dict["index"],
              },
          )
  
      stylized_raw = _np.array(stylized_image)
  
      expected_height = feed_dict["input"].shape[1]
      expected_width = feed_dict["input"].shape[2]
  
      # Crop to remove added padding
      stylized_cropped = stylized_raw[
          :, :, 0:expected_height, 0:expected_width, :
      ][0]
  
      return {"output": _np.array(stylized_cropped)}
  
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 8 for calling the StyleTransferTensorFlowModel obj's underlying member method code with example usage: obj.predict(feed_dict) and returns: dict