code: def run(self, content_img, style_image):
      content_img, style_img, h, w = img_totensor(
          content_img, style_image
      )
      pyr_ci = make_laplace_pyramid(content_img, 2)
      pyr_si = make_laplace_pyramid(style_img, 2)
      pyr_ci.append(content_img)
      pyr_si.append(style_img)
      cF = self.net_enc(pyr_ci[2])
      sF = self.net_enc(pyr_si[2])
      stylized_small = self.net_dec(cF, sF)
      stylized_up = F.interpolate(stylized_small, scale_factor=2)
  
      revnet_input = paddle.concat(x=[pyr_ci[1], stylized_up], axis=1)
      stylized_rev_lap = self.net_rev(revnet_input)
      stylized_rev = fold_laplace_pyramid(
          [stylized_rev_lap, stylized_small]
      )
  
      stylized_up = F.interpolate(stylized_rev, scale_factor=2)
  
      revnet_input = paddle.concat(x=[pyr_ci[0], stylized_up], axis=1)
      stylized_rev_lap_second = self.net_rev_2(revnet_input)
      stylized_rev_second = fold_laplace_pyramid(
          [stylized_rev_lap_second, stylized_rev_lap, stylized_small]
      )
  
      stylized = stylized_rev_second
      stylized_visual = tensor2img(stylized, min_max=(0.0, 1.0))
  
      return stylized_visual
  
imports: import os
  import urllib.request
  import cv2
  import numpy
  import paddle
  import paddle.nn.functional
  from paddle.vision.transforms import functional
  from PIL import Image
  from ppgan.models.generators import DecoderNet
  from ppgan.models.generators import Encoder
  from ppgan.models.generators import RevisionNet
  from ppgan.utils.visual import tensor2img
label_desc: Use image node_id 2 for calling the LapStylePredictor obj's underlying member method code with example usage: obj.run(content_img, style_image) and returns: stylized_visual