code: def run(mini_batch):
      result = []
      for image_file_path in mini_batch:
          img = load_image(image_file_path)
  
          with torch.no_grad():
              content_transform = transforms.Compose(
                  [
                      transforms.ToTensor(),
                      transforms.Lambda(lambda x: x.mul(255)),
                  ]
              )
              content_image = content_transform(img)
              content_image = content_image.unsqueeze(0).to(device)
  
              output = style_model(content_image).cpu()
              output_file_path = os.path.join(
                  output_path, os.path.basename(image_file_path)
              )
              save_image(output_file_path, output[0])
              result.append(output_file_path)
  
      return result
  
imports: import argparse
  import os
  import sys
  import re
  import json
  import traceback
  from PIL import Image
  import torch
  from torchvision import transforms
  from azureml.core.model import Model
label_desc: Use image node_id 4 for calling a global function with example usage: run(mini_batch) and returns: result