code: def parse_args():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--image_folder",
          "--image_folder",
          help="Must contain all modalities for each patient in the correct"
          " order (same as training). Files must be named "
          "CASENAME_XXXX.nii.gz where XXXX is the modality "
          "identifier (0000, 0001, etc)",
          required=True,
      )
      parser.add_argument(
          "--output_folder",
          "--output_folder",
          required=True,
          help="folder for saving predictions",
      )
      parser.add_argument(
          "--model_type",
          "--model_type",
          required=True,
          type=str,
          help="Model type, only support '2d', '3d', 'cascade_lowres', 'cascade_fullres'.",
      )
  
      parser.add_argument(
          "--plan_path",
          "--plan_path",
          required=True,
          type=str,
          help="the path to plan_path",
      )
      parser.add_argument(
          "--model_paths",
          "--model_paths",
          nargs="+",
          required=True,
          help="The multi model paths.",
      )
      parser.add_argument(
          "--postprocessing_json_path",
          "--postprocessing_json_path",
          required=True,
          default=None,
          type=str,
          help="the path to postprocessing json.",
      )
      parser.add_argument(
          "--folds",
          "--folds",
          required=False,
          type=int,
          default=5,
          help="number of folds, default: 5.",
      )
      parser.add_argument(
          "--lowres_segmentations",
          "--lowres_segmentations",
          required=False,
          default=None,
          help="If model is the highres stage of the cascade then you can use this folder to provide "
          "predictions from the low resolution 3D U-Net.",
      )
      parser.add_argument(
          "--save_npz",
          "--save_npz",
          required=False,
          action="store_true",
          help="use this if you want to ensemble these predictions with those of other models. Softmax "
          "probabilities will be saved as compressed numpy arrays in output_folder and can be "
          "merged between output_folders with nnUNet_ensemble_predictions",
      )
  
      parser.add_argument(
          "--num_threads_preprocessing",
          "--num_threads_preprocessing",
          required=False,
          default=6,
          type=int,
          help="Determines many background processes will be used for data preprocessing. Reduce this if you "
          "run into out of memory (RAM) problems. Default: 6",
      )
  
      parser.add_argument(
          "--num_threads_nifti_save",
          "--num_threads_nifti_save",
          required=False,
          default=2,
          type=int,
          help="Determines many background processes will be used for segmentation export. Reduce this if you "
          "run into out of memory (RAM) problems. Default: 2",
      )
  
      parser.add_argument(
          "--mode",
          "--mode",
          type=str,
          default="normal",
          required=False,
          help="Hands off!",
      )
      parser.add_argument(
          "--step_size",
          "--step_size",
          type=float,
          default=0.5,
          required=False,
          help="don't touch",
      )
      parser.add_argument(
          "--overwrite_existing",
          "--overwrite_existing",
          required=False,
          default=False,
          action="store_true",
          help="Set this flag if the target folder contains predictions that you would like to overwrite",
      )
      parser.add_argument(
          "--disable_postprocessing",
          "--disable_postprocessing",
          required=False,
          default=False,
          action="store_true",
          help="Set this flag if no need postprocessing",
      )
      parser.add_argument(
          "--disable_tta",
          required=False,
          default=False,
          action="store_true",
          help="set this flag to disable test time data augmentation via mirroring. Speeds up inference "
          "by roughly factor 4 (2D) or 8 (3D)",
      )
      parser.add_argument(
          "--precision",
          default="fp16",
          required=False,
          help="whether use mixed precision prediction.",
      )
      return parser.parse_args()
  
file_path: PaddleSeg/contrib/MedicalSeg/nnunet/predict.py
imports: import argparse
  import os
  import sys
  import pickle
  import shutil
  import numpy
  from copy import deepcopy
  from medicalseg.models import NNUNet
  from nnunet.utils import predict_cases, MultiFoldsPredictor
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: parser