code: def list_reader(self, list_file_real, list_file_fake):
      with open(list_file_real, "r") as f:
          lines_real = f.readlines()
      with open(list_file_fake, "r") as f:
          lines_fake = f.readlines()
  
      fake_label_start = (
          max([int(l.strip().split(" ")[-1]) for l in lines_real]) + 1
      )
      lines_fake = [
          "{} {}".format(
              l.strip().split(" ")[0],
              int(l.strip().split(" ")[1]) + fake_label_start,
          )
          for l in lines_fake
      ]
  
      lines = lines_real + lines_fake
  
      pid_container = set()
      for line in lines:
          pid = int(line.strip().split(" ")[1])
          pid_container.add(pid)
      pid2label = {
          pid: label for label, pid in enumerate(pid_container)
      }
  
      img_list_R = []
      for line in lines_real:
          img_name, pid = line.strip().split(" ")
          label = pid2label[int(pid)]
          img_list_R.append(
              (os.path.join(self.img_root_R + img_name), label)
          )
  
      img_list_F = []
      for line in lines_fake:
          img_name, pid = line.strip().split(" ")
          label = pid2label[int(pid)]
          img_list_F.append(
              (os.path.join(self.img_root_F + img_name), label)
          )
  
      img_list = img_list_R + img_list_F
  
      return img_list, len(pid_container)
  
imports: from PIL import Image
  import os
  import torch.utils.data
  import torchvision.transforms
  from torch.utils.data.sampler import Sampler
  import numpy
label_desc: Use image node_id 5 for calling the Mix_Dataset_112 obj's underlying member method code with example usage: obj.list_reader(list_file_real, list_file_fake) and returns: img_list,  len