code: def predict_preprocessed_data_return_seg_and_softmax(
      self,
      data: np.ndarray,
      do_mirroring: bool = True,
      mirror_axes: Tuple[int] = None,
      use_sliding_window: bool = True,
      step_size: float = 0.5,
      use_gaussian: bool = True,
      pad_border_mode: str = "constant",
      pad_kwargs: dict = None,
      verbose: bool = True,
      mixed_precision=False,
  ):
      if pad_border_mode == "constant" and pad_kwargs is None:
          pad_kwargs = {"constant_values": 0}
      if do_mirroring and mirror_axes is None:
          mirror_axes = self.data_aug_params["mirror_axes"]
      if do_mirroring:
          assert self.data_aug_params["do_mirror"], (
              "Cannot do mirroring as test time augmentation when training "
              "was done without mirroring"
          )
      res = self.predict_3D(
          x=data,
          do_mirroring=do_mirroring,
          mirror_axes=mirror_axes,
          use_sliding_window=use_sliding_window,
          step_size=step_size,
          patch_size=self.patch_size,
          regions_class_order=None,
          use_gaussian=use_gaussian,
          pad_border_mode=pad_border_mode,
          pad_kwargs=pad_kwargs,
          verbose=verbose,
      )
      return res
  
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 5 for calling the StaticPredictor obj's underlying member method code with example usage: obj.predict_preprocessed_data_return_seg_and_softmax(data, do_mirroring, mirror_axes, use_sliding_window, step_size, use_gaussian, pad_border_mode, pad_kwargs, verbose, mixed_precision) and returns: res