class_docstr: Static predictor for nnunet.
class_docstr_tok: ["Static","predictor","for","nnunet","."]
code: def _get_gaussian(patch_size, sigma_scale=1.0 / 8) -> np.ndarray:
      tmp = np.zeros(patch_size)
      center_coords = [i // 2 for i in patch_size]
      sigmas = [i * sigma_scale for i in patch_size]
      tmp[tuple(center_coords)] = 1
      gaussian_importance_map = gaussian_filter(
          tmp, sigmas, 0, mode="constant", cval=0
      )
      gaussian_importance_map = (
          gaussian_importance_map / np.max(gaussian_importance_map) * 1
      )
      gaussian_importance_map = gaussian_importance_map.astype(
          np.float32
      )
  
      gaussian_importance_map[gaussian_importance_map == 0] = np.min(
          gaussian_importance_map[gaussian_importance_map != 0]
      )
  
      return gaussian_importance_map
  
imports: import pickle
  import numpy
  from functools import partial
  from typing import Tuple, List, Union
  from scipy.ndimage.filters import gaussian_filter
  from paddle.inference import create_predictor, PrecisionType
  from paddle.inference import Config
  from tools.preprocess_utils import GenericPreprocessor, PreprocessorFor2D
  from nnunet.utils.utils import no_op, pad_nd_image
  from nnunet.transforms import default_2D_augmentation_params, default_3D_augmentation_params
label_desc: Use image node_id 4 for calling the StaticBasePredictor obj's underlying member method code with example usage: obj._get_gaussian(patch_size, sigma_scale) and returns: gaussian_importance_map