code: def __init__(
      self,
      model_paths,
      param_paths,
      plan_path,
      stage,
      min_subgraph_size=3,
  ):
      self.stage = stage
      self.plans = self.load_plans(plan_path)
      self.num_classes = self.plans["num_classes"] + 1
      self.patch_size = np.array(
          self.plans["plans_per_stage"][self.stage]["patch_size"]
      ).astype(int)
      if len(self.patch_size) == 2:
          self.threeD = False
          self.data_aug_params = default_2D_augmentation_params
      elif len(self.patch_size) == 3:
          self.threeD = True
          self.data_aug_params = default_3D_augmentation_params
      self.intensity_properties = self.plans["dataset_properties"][
          "intensityproperties"
      ]
      self.normalization_schemes = self.plans["normalization_schemes"]
      self.use_mask_for_norm = self.plans["use_mask_for_norm"]
      if (
          self.plans.get("transpose_forward") is None
          or self.plans.get("transpose_backward") is None
      ):
          print(
              "WARNING! You seem to have data that was preprocessed with a previous version of nnU-Net. "
              "You should rerun preprocessing. We will proceed and assume that both transpose_foward "
              "and transpose_backward are [0, 1, 2]. If that is not correct then weird things will happen!"
          )
          self.plans["transpose_forward"] = [0, 1, 2]
          self.plans["transpose_backward"] = [0, 1, 2]
      self.transpose_forward = self.plans["transpose_forward"]
      self.transpose_backward = self.plans["transpose_backward"]
  
      self.predictors = []
      for model_path, param_path in zip(model_paths, param_paths):
          self.predictors.append(
              StaticPredictor(
                  model_path,
                  param_path,
                  self.plans,
                  stage,
                  min_subgraph_size,
              )
          )
  
imports: import argparse
  import os
  import sys
  import pickle
  import shutil
  import numpy
  from copy import deepcopy
  from typing import Tuple, List, Union
  import paddle
  from paddle.inference import create_predictor, PrecisionType
  from paddle.inference import Config
  from nnunet.utils.static_predictor import StaticPredictor
  from nnunet.transforms import default_2D_augmentation_params, default_3D_augmentation_params
  from nnunet.predict import predict_from_folder
  from tools.preprocess_utils import GenericPreprocessor, PreprocessorFor2D
label_desc: Use image node_id 1 to create a new StaticMultiFolderPredictor object  with example: obj = StaticMultiFolderPredictor(model_paths, param_paths, plan_path, stage, min_subgraph_size)