code: def prepare_arrays(image, roi_shape=(128, 128, 128)):
      assert len(roi_shape) == 3 and any(roi_shape)
      image_shape = list(image.shape[2:])
      result = np.zeros((1, 3, *image_shape), dtype=image.dtype)
      norm_map = np.zeros_like(result)
      norm_patch = gaussian_kernel(
          roi_shape[0], 0.125 * roi_shape[0]
      ).astype(norm_map.dtype)
      return result, norm_map, norm_patch
  
file_path: tinygrad/examples/mlperf/helpers.py
imports: from collections import OrderedDict
  import unicodedata
  import numpy
  from scipy import signal
label_desc: Use image node_id 2 for calling a global function with example usage: prepare_arrays(image, roi_shape) and returns: result,  norm_map,  norm_patch