class_docstr: Basic predictor for nnunet.
class_docstr_tok: ["Basic","predictor","for","nnunet","."]
code: def _internal_predict_3D_2Dconv_tiled(
      self,
      x: np.ndarray,
      patch_size: Tuple[int, int],
      do_mirroring: bool,
      mirror_axes: tuple = (0, 1),
      step_size: float = 0.5,
      regions_class_order: tuple = None,
      use_gaussian: bool = False,
      pad_border_mode: str = "edge",
      pad_kwargs: dict = None,
      verbose: bool = True,
  ) -> Tuple[np.ndarray, np.ndarray]:
      assert (
          len(x.shape) == 4
      ), "x must be (c, x, y, z), but got {}.".format(x.shape)
      predicted_segmentation = []
      softmax_pred = []
      for s in range(x.shape[1]):
          (
              pred_seg,
              softmax_pres,
          ) = self._internal_predict_2D_2Dconv_tiled(
              x[:, s],
              step_size,
              do_mirroring,
              mirror_axes,
              patch_size,
              regions_class_order,
              use_gaussian,
              pad_border_mode,
              pad_kwargs,
              verbose,
          )
          predicted_segmentation.append(pred_seg[None])
          softmax_pred.append(softmax_pres[None])
  
      predicted_segmentation = np.vstack(predicted_segmentation)
      softmax_pred = np.vstack(softmax_pred).transpose((1, 0, 2, 3))
      return predicted_segmentation, softmax_pred
  
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 13 for calling the BasePredictor obj's underlying member method code with example usage: obj._internal_predict_3D_2Dconv_tiled(x, patch_size, do_mirroring, mirror_axes, step_size, regions_class_order, use_gaussian, pad_border_mode, pad_kwargs, verbose) and returns: predicted_segmentation,  softmax_pred