class_docstr: Basic predictor for nnunet.
class_docstr_tok: ["Basic","predictor","for","nnunet","."]
code: def __init__(self):
      # The follow 4 attributes must be set in subclass.
      self.input_shape_must_be_divisible_by = None
      self.threeD = None
      self.num_classes = None
      self.inference_apply_nonlin = None
  
      self._gaussian_3d = self._patch_size_for_gaussian_3d = None
      self._gaussian_2d = self._patch_size_for_gaussian_2d = None
  
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 BasePredictor object  with example: obj = BasePredictor()