code: def run_style_transfer(
      cnn,
      normalization_mean,
      normalization_std,
      content_img,
      style_img,
      input_img,
      num_steps=300,
      style_weight=1000000,
      content_weight=1,
  ):
      """Run the style transfer."""
      print("Building the style transfer model..")
      model, style_losses, content_losses = get_style_model_and_losses(
          cnn,
          normalization_mean,
          normalization_std,
          style_img,
          content_img,
      )
      optimizer = get_input_optimizer(input_img)
  
      print("Optimizing..")
      run = [0]
      while run[0] <= num_steps:
  
          def closure():
              # correct the values of updated input image
              input_img.data.clamp_(0, 1)
  
              optimizer.zero_grad()
              model(input_img)
              style_score = 0
              content_score = 0
  
              for sl in style_losses:
                  style_score += sl.loss
              for cl in content_losses:
                  content_score += cl.loss
  
              style_score *= style_weight
              content_score *= content_weight
  
              loss = style_score + content_score
              loss.backward()
  
              run[0] += 1
              if run[0] % 50 == 0:
                  print("run {}:".format(run))
                  print(
                      "Style Loss : {:4f} Content Loss: {:4f}".format(
                          style_score.item(), content_score.item()
                      )
                  )
                  print()
  
              return style_score + content_score
  
          optimizer.step(closure)
  
      # a last correction...
      input_img.data.clamp_(0, 1)
  
      return input_img
  
imports: from __future__ import print_function
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from PIL import Image
  import matplotlib.pyplot
  import torchvision.transforms
  import torchvision.models
  import copy
  from clearml import Task
label_desc: Use image node_id 6 for calling a global function with example usage: run_style_transfer(cnn, normalization_mean, normalization_std, content_img, style_img, input_img, num_steps, style_weight, content_weight) and returns: input_img, unknown