code: def _find_edges_laplacian(image, edge_multiplier, from_colorspace):
      image_gray = colorlib.change_colorspace_(
          np.copy(image),
          to_colorspace=colorlib.CSPACE_GRAY,
          from_colorspace=from_colorspace,
      )
      image_gray = image_gray[..., 0]
      edges_f = cv2.Laplacian(
          _normalize_cv2_input_arr_(image_gray / 255.0), cv2.CV_64F
      )
      edges_f = np.abs(edges_f)
      edges_f = edges_f**2
      vmax = np.percentile(
          edges_f, min(int(90 * (1 / edge_multiplier)), 99)
      )
      edges_f = np.clip(edges_f, 0.0, vmax) / vmax
  
      edges_uint8 = np.clip(np.round(edges_f * 255), 0, 255.0).astype(
          np.uint8
      )
      edges_uint8 = _blur_median(edges_uint8, 3)
      edges_uint8 = _threshold(edges_uint8, 50)
      return edges_uint8
  
imports: from __future__ import print_function, division, absolute_import
  import numpy
  import cv2
  from imgaug.imgaug import _normalize_cv2_input_arr_
  from .None import meta
  from .None import color
  from ..None import dtypes
  from ..None import parameters
label_desc: Use image node_id 3 for calling a global function with example usage: _find_edges_laplacian(image, edge_multiplier, from_colorspace) and returns: edges_uint8