code: def multi_processing_create_image(inputs):
      out_dir, six, foreground_margin, class_diameters, mode = inputs
      print("processing {} {}".format(out_dir, six))
  
      img = np.random.rand(320, 320)
      seg = np.zeros((320, 320)).astype("uint8")
      center_x = np.random.randint(
          foreground_margin, img.shape[0] - foreground_margin
      )
      center_y = np.random.randint(
          foreground_margin, img.shape[1] - foreground_margin
      )
      class_id = np.random.randint(0, 2)
  
      for y in range(img.shape[0]):
          for x in range(img.shape[0]):
              if (
                  (x - center_x) ** 2
                  + (y - center_y) ** 2
                  - class_diameters[class_id] ** 2
              ) < 0:
                  img[y][x] += 0.2
                  seg[y][x] = 1
  
      if "donuts" in mode:
          whole_diameter = 4
          if class_id == 1:
              for y in range(img.shape[0]):
                  for x in range(img.shape[0]):
                      if (
                          (x - center_x) ** 2
                          + (y - center_y) ** 2
                          - whole_diameter**2
                      ) < 0:
                          img[y][x] -= 0.2
                          if mode == "donuts_shape":
                              seg[y][x] = 0
  
      out = np.concatenate((img[None], seg[None]))
      out_path = os.path.join(out_dir, "{}.npy".format(six))
      np.save(out_path, out)
  
      with open(
          os.path.join(out_dir, "meta_info_{}.pickle".format(six)), "wb"
      ) as handle:
          pickle.dump([out_path, class_id, str(six)], handle)
  
file_path: medicaldetectiontoolkit/experiments/toy_exp/generate_toys.py
imports: import os
  import numpy
  import pandas
  import pickle
  from multiprocessing import Pool
  import configs
label_desc: Use image node_id 1 for calling a global function with example usage: multi_processing_create_image(inputs) without return types