code: def hausdorff_distance(a, b, densify=None, **kwargs):
      """Compute the discrete Hausdorff distance between two geometries.
  
      The Hausdorff distance is a measure of similarity: it is the greatest
      distance between any point in A and the closest point in B. The discrete
      distance is an approximation of this metric: only vertices are considered.
      The parameter 'densify' makes this approximation less coarse by splitting
      the line segments between vertices before computing the distance.
  
      Parameters
      ----------
      a, b : Geometry or array_like
      densify : float or array_like, optional
          The value of densify is required to be between 0 and 1.
      **kwargs
          See :ref:`NumPy ufunc docs <ufuncs.kwargs>` for other keyword arguments.
  
      Examples
      --------
      >>> from shapely import LineString
      >>> line1 = LineString([(130, 0), (0, 0), (0, 150)])
      >>> line2 = LineString([(10, 10), (10, 150), (130, 10)])
      >>> hausdorff_distance(line1, line2)  # doctest: +ELLIPSIS
      14.14...
      >>> hausdorff_distance(line1, line2, densify=0.5)
      70.0
      >>> hausdorff_distance(line1, LineString())
      nan
      >>> hausdorff_distance(line1, None)
      nan
      """
      if densify is None:
          return lib.hausdorff_distance(a, b, **kwargs)
      else:
          return lib.hausdorff_distance_densify(a, b, densify, **kwargs)
  
file_path: shapely/shapely/measurement.py
imports: import warnings
  import numpy
  from shapely import lib
  from shapely.decorators import multithreading_enabled
label_desc: Use image node_id 6 for calling a global function with example usage: hausdorff_distance(a, b, densify) and returns: lib, lib