code: def __init__(self, model):
      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
  
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 DynamicPredictor object from inherited base classes: BasePredictor with example: obj = DynamicPredictor(model)