class_docstr: Static predictor for nnunet.
class_docstr_tok: ["Static","predictor","for","nnunet","."]
code: def _internal_maybe_mirror_and_pred_2D(
      self,
      x: np.ndarray,
      mirror_axes: tuple,
      do_mirroring: bool = True,
      mult: np.ndarray = None,
  ):
      assert (
          len(x.shape) == 4
      ), "x must be (b, c, x, y), but got {}.".format(x.shape)
  
      result = np.zeros(
          [x.shape[0], self.num_classes] + list(x.shape[2:]),
          dtype="float32",
      )
  
      if do_mirroring:
          mirror_idx = 4
          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 (1 in mirror_axes):
              pred = self.inference_apply_nonlin(self(np.flip(x, (3,))))
              result += 1 / num_results * np.flip(pred, (3,))
  
          if m == 2 and (0 in mirror_axes):
              pred = self.inference_apply_nonlin(self(np.flip(x, (2,))))
              result += 1 / num_results * np.flip(pred, (2,))
  
          if m == 3 and (0 in mirror_axes) and (1 in mirror_axes):
              pred = self.inference_apply_nonlin(
                  self(np.flip(x, (3, 2)))
              )
              result += 1 / num_results * np.flip(pred, (3, 2))
      if mult is not None:
          result[:, :] *= mult
      return result
  
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 10 for calling the StaticBasePredictor obj's underlying member method code with example usage: obj._internal_maybe_mirror_and_pred_2D(x, mirror_axes, do_mirroring, mult) and returns: result