code: def multi_folds_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=True,
  ):
      softmax_res = None
      for predictor in self.predictors:
          x = predictor.predict_preprocessed_data_return_seg_and_softmax(
              data=data,
              do_mirroring=do_mirroring,
              mirror_axes=mirror_axes,
              use_sliding_window=use_sliding_window,
              step_size=step_size,
              use_gaussian=use_gaussian,
              pad_border_mode=pad_border_mode,
              pad_kwargs=pad_kwargs,
              verbose=verbose,
              mixed_precision=mixed_precision,
          )[
              1
          ]
          if softmax_res is None:
              softmax_res = x
          else:
              softmax_res += x
      return softmax_res / len(self.predictors)
  
imports: import argparse
  import os
  import sys
  import pickle
  import shutil
  import numpy
  from copy import deepcopy
  from typing import Tuple, List, Union
  import paddle
  from paddle.inference import create_predictor, PrecisionType
  from paddle.inference import Config
  from nnunet.utils.static_predictor import StaticPredictor
  from nnunet.transforms import default_2D_augmentation_params, default_3D_augmentation_params
  from nnunet.predict import predict_from_folder
  from tools.preprocess_utils import GenericPreprocessor, PreprocessorFor2D
label_desc: Use image node_id 4 for calling the StaticMultiFolderPredictor obj's underlying member method code with example usage: obj.multi_folds_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: unknown