code: def load_postprocessing(json_path):
      with open(json_path, "rb") as f:
          a = json.load(f)
      if "min_valid_object_sizes" in a.keys():
          min_valid_object_sizes = ast.literal_eval(
              a["min_valid_object_sizes"]
          )
      else:
          min_valid_object_sizes = None
      return a["for_which_classes"], min_valid_object_sizes
  
file_path: PaddleSeg/contrib/MedicalSeg/nnunet/utils/predict_utils.py
imports: import os
  import sys
  import ast
  import shutil
  import json
  import pickle
  import numpy
  from copy import deepcopy
  from typing import Tuple, Union
  import SimpleITK
  from multiprocessing import Process, Queue, Pool
  from nnunet.utils import load_remove_save
  from nnunet.transforms import resize_segmentation
  from .utils import save_segmentation_nifti_from_softmax
  from tools.preprocess_utils import get_do_separate_z, get_lowres_axis, resample_data_or_seg
label_desc: Use image node_id 1 for calling a global function with example usage: load_postprocessing(json_path) and returns: a,  min_valid_object_sizes