code: def _suppress_edge_blobs(edges, size, thresh, inverse):
      kernel = np.ones((size, size), dtype=np.float32)
      counts = cv2.filter2D(
          _normalize_cv2_input_arr_(edges / 255.0), -1, kernel
      )
  
      if inverse:
          mask = counts < thresh
      else:
          mask = counts >= thresh
  
      edges = np.copy(edges)
      edges[mask] = 0
      return edges
  
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 6 for calling a global function with example usage: _suppress_edge_blobs(edges, size, thresh, inverse) and returns: edges