code: def frechet_distance(a, b, densify=None, **kwargs):
      """Compute the discrete Fréchet distance between two geometries.
  
      The Fréchet 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.
  
      Fréchet distance sweep continuously along their respective curves
      and the direction of curves is significant. This makes it a better measure
      of similarity than Hausdorff distance for curve or surface matching.
  
      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([(0, 0), (100, 0)])
      >>> line2 = LineString([(0, 0), (50, 50), (100, 0)])
      >>> frechet_distance(line1, line2)  # doctest: +ELLIPSIS
      70.71...
      >>> frechet_distance(line1, line2, densify=0.5)
      50.0
      >>> frechet_distance(line1, LineString())
      nan
      >>> frechet_distance(line1, None)
      nan
      """
      if densify is None:
          return lib.frechet_distance(a, b, **kwargs)
      return lib.frechet_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 7 for calling a global function with example usage: frechet_distance(a, b, densify) and returns: lib, lib