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 params in self.param_list:
          self.model.set_state_dict(params)
          x = self.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.param_list)
  
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 5 for calling the MultiFoldsPredictor 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