code: def predict_preprocessed_data_return_seg_and_softmax(
      predictor,
      dataset,
      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: bool = True,
  ) -> Tuple[np.ndarray, np.ndarray]:
      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 = dataset.data_aug_params["mirror_axes"]
  
      if do_mirroring:
          assert dataset.data_aug_params["do_mirror"], (
              "Cannot do mirroring as test time augmentation when training "
              "was done without mirroring"
          )
      with paddle.no_grad():
          argmax_pred, softmax_pred = predictor.predict_3D(
              data,
              do_mirroring=do_mirroring,
              mirror_axes=mirror_axes,
              use_sliding_window=use_sliding_window,
              step_size=step_size,
              patch_size=dataset.patch_size,
              regions_class_order=None,
              use_gaussian=use_gaussian,
              pad_border_mode=pad_border_mode,
              pad_kwargs=pad_kwargs,
              verbose=verbose,
              mixed_precision=mixed_precision,
          )
      return argmax_pred, softmax_pred
  
file_path: PaddleSeg/contrib/MedicalSeg/nnunet/utils/cascade_utils.py
imports: import os
  import numpy
  import paddle
  from typing import Tuple
  from multiprocessing import Pool
  from .None import resample_and_save, DynamicPredictor
label_desc: Use image node_id 1 for calling a global function with example usage: predict_preprocessed_data_return_seg_and_softmax(predictor, dataset, data, do_mirroring, mirror_axes, use_sliding_window, step_size, use_gaussian, pad_border_mode, pad_kwargs, verbose, mixed_precision) and returns: argmax_pred,  softmax_pred