code: def _edge_map(image):
      """Return a maps of edges suitable for visualization.
  
      Parameters
      ==========
      image: 2D array
          The image that the edges are extracted from.
  
      Returns
      ========
      edge_mask: 2D masked array
          A mask of the edge as a masked array with parts without
          edges masked and the large extents detected with lower
          coefficients.
      """
      edge_mask = _edge_detect(image)[-1]
      edge_mask = edge_mask.astype(np.float64)
      edge_mask = -np.sqrt(ndimage.distance_transform_cdt(edge_mask))
      edge_mask[edge_mask != 0] -= -0.05 + edge_mask.min()
      edge_mask = np.ma.masked_less(edge_mask, 0.01)
      return edge_mask
  
imports: import warnings
  import numpy
  from scipy import ndimage, signal
label_desc: Use image node_id 4 for calling a global function with example usage: _edge_map(image) and returns: edge_mask