code: def __init__(self, model, param_paths):
      super().__init__()
      assert hasattr(
          model, "net_num_pool_op_kernel_sizes"
      ), "BasePredictor only used for nnunet predict, but not found net_num_pool_op_kernel_sizes in your model."
      self.input_shape_must_be_divisible_by = np.prod(
          model.net_num_pool_op_kernel_sizes, 0, dtype=np.int64
      )
      self.threeD = model.threeD
      self.num_classes = model.num_classes
      self.inference_apply_nonlin = partial(
          paddle.nn.functional.softmax, axis=1
      )
      self.model = model
      self.param_list = [
          paddle.load(param_path) for param_path in param_paths
      ]
      self.plans = model.plans
      self.stage = model.stage
  
      if self.threeD:
          self.data_aug_params = default_3D_augmentation_params
      else:
          self.data_aug_params = default_2D_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"]
  
imports: import numpy
  from functools import partial
  from typing import Tuple, List, Union
  from scipy.ndimage.filters import gaussian_filter
  from .utils import no_op, pad_nd_image
  import paddle
  from paddle.amp import auto_cast
  from tools.preprocess_utils import GenericPreprocessor, PreprocessorFor2D
  from nnunet.transforms import default_2D_augmentation_params, default_3D_augmentation_params
label_desc: Use image node_id 1 to create a new MultiFoldsPredictor object from inherited base classes: BasePredictor with example: obj = MultiFoldsPredictor(model, param_paths)