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, all_in_gpu: bool=False, verbose: bool=True, mixed_precision=True) -> Tuple[(np.ndarray, np.ndarray)]:
    ds = self.network.decoder.deep_supervision
    self.network.decoder.deep_supervision = False
    ret = nnUNetTrainer.predict_preprocessed_data_return_seg_and_softmax(self, 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, all_in_gpu=all_in_gpu, verbose=verbose, mixed_precision=mixed_precision)
    self.network.decoder.deep_supervision = ds
    return ret
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, all_in_gpu: bool=False, verbose: bool=True, mixed_precision=True) -> Tuple[(np.ndarray, np.ndarray)]:
    ds = self.network.decoder.deep_supervision
    self.network.decoder.deep_supervision = False
    ret = nnUNetTrainer.predict_preprocessed_data_return_seg_and_softmax(self, 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, all_in_gpu=all_in_gpu, verbose=verbose, mixed_precision=mixed_precision)
    self.network.decoder.deep_supervision = ds
    return ret