class_docstr: Basic predictor for nnunet.
class_docstr_tok: ["Basic","predictor","for","nnunet","."]
code: def _internal_maybe_mirror_and_pred_3D(
      self,
      x: Union[np.ndarray, paddle.Tensor],
      mirror_axes: tuple,
      do_mirroring: bool = True,
      mult: np.ndarray or paddle.Tensor = None,
  ) -> paddle.Tensor:
      assert (
          len(x.shape) == 5
      ), "x must be (b, c, x, y, z), but got {}.".format(x.shape)
  
      x = paddle.to_tensor(x).astype("float32")
      result = paddle.zeros(
          [1, self.num_classes] + list(x.shape[2:]), dtype="float32"
      )
  
      if mult is not None:
          mult = paddle.to_tensor(mult).astype("float32")
  
      if do_mirroring:
          mirror_idx = 8
          num_results = 2 ** len(mirror_axes)
      else:
          mirror_idx = 1
          num_results = 1
  
      for m in range(mirror_idx):
          if m == 0:
              pred = self.inference_apply_nonlin(self(x))
              result += 1 / num_results * pred
  
          if m == 1 and (2 in mirror_axes):
              pred = self.inference_apply_nonlin(
                  self(paddle.flip(x, (4,)))
              )
              result += 1 / num_results * paddle.flip(pred, (4,))
  
          if m == 2 and (1 in mirror_axes):
              pred = self.inference_apply_nonlin(
                  self(paddle.flip(x, (3,)))
              )
              result += 1 / num_results * paddle.flip(pred, (3,))
  
          if m == 3 and (2 in mirror_axes) and (1 in mirror_axes):
              pred = self.inference_apply_nonlin(
                  self(paddle.flip(x, (4, 3)))
              )
              result += 1 / num_results * paddle.flip(pred, (4, 3))
  
          if m == 4 and (0 in mirror_axes):
              pred = self.inference_apply_nonlin(
                  self(paddle.flip(x, (2,)))
              )
              result += 1 / num_results * paddle.flip(pred, (2,))
  
          if m == 5 and (0 in mirror_axes) and (2 in mirror_axes):
              pred = self.inference_apply_nonlin(
                  self(paddle.flip(x, (4, 2)))
              )
              result += 1 / num_results * paddle.flip(pred, (4, 2))
  
          if m == 6 and (0 in mirror_axes) and (1 in mirror_axes):
              pred = self.inference_apply_nonlin(
                  self(paddle.flip(x, (3, 2)))
              )
              result += 1 / num_results * paddle.flip(pred, (3, 2))
  
          if (
              m == 7
              and (0 in mirror_axes)
              and (1 in mirror_axes)
              and (2 in mirror_axes)
          ):
              pred = self.inference_apply_nonlin(
                  self(paddle.flip(x, (4, 3, 2)))
              )
              result += 1 / num_results * paddle.flip(pred, (4, 3, 2))
  
      if mult is not None:
          result[:, :] *= mult
      return result
  
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 9 for calling the BasePredictor obj's underlying member method code with example usage: obj._internal_maybe_mirror_and_pred_3D(x, mirror_axes, do_mirroring, mult) and returns: result