code: def __init__(
      self, model_path, param_path, plans, stage, min_subgraph_size=3
  ):
      super().__init__()
      self.stage = stage
      self.min_subgraph_size = min_subgraph_size
      self.plans = plans
      self.inference_apply_nonlin = lambda x: x
      self.model_path = model_path
      self.param_path = param_path
  
      self.patch_size = np.array(
          self.plans["plans_per_stage"][self.stage]["patch_size"]
      ).astype(int)
      self.input_shape_must_be_divisible_by = np.prod(
          self.plans["plans_per_stage"][self.stage][
              "pool_op_kernel_sizes"
          ],
          0,
          dtype=np.int64,
      )
      self.num_classes = self.plans["num_classes"] + 1
  
      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._init_base_config()
      self._init_gpu_config()
      self.predictor = create_predictor(self.pred_cfg)
      input_names = self.predictor.get_input_names()
      self.input_handle = self.predictor.get_input_handle(
          input_names[0]
      )
      output_names = self.predictor.get_output_names()
      self.output_handle = self.predictor.get_output_handle(
          output_names[0]
      )
  
imports: import pickle
  import numpy
  from functools import partial
  from typing import Tuple, List, Union
  from scipy.ndimage.filters import gaussian_filter
  from paddle.inference import create_predictor, PrecisionType
  from paddle.inference import Config
  from tools.preprocess_utils import GenericPreprocessor, PreprocessorFor2D
  from nnunet.utils.utils import no_op, pad_nd_image
  from nnunet.transforms import default_2D_augmentation_params, default_3D_augmentation_params
label_desc: Use image node_id 1 to create a new StaticPredictor object from inherited base classes: StaticBasePredictor with example: obj = StaticPredictor(model_path, param_path, plans, stage, min_subgraph_size)