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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def natural_neighbor_point(xp, yp, variable, grid_loc, tri, neighbors, triangle_info):
 r"""Generate a natural neighbor interpolation of the observations to the given point. This uses the Liang and Hale approach [Liang2010]_. The interpolation will fail if the grid point has no natural neighbors. Parameters xp: (N, ) ndarray x-coordinates of observations yp: (N, ) ndarray y-coordinates of observations variable: (N, ) ndarray observation values associated with (xp, yp) pairs. IE, variable[i] is a unique observation at (xp[i], yp[i]) grid_loc: (float, float) Coordinates of the grid point at which to calculate the interpolation. tri: object Delaunay triangulation of the observations. neighbors: (N, ) ndarray Simplex codes of the grid point's natural neighbors. The codes will correspond to codes in the triangulation. triangle_info: dictionary Pre-calculated triangle attributes for quick look ups. Requires items 'cc' (circumcenters) and 'r' (radii) to be associated with each simplex code key from the delaunay triangulation. Returns ------- value: float Interpolated value for the grid location """

    edges = geometry.find_local_boundary(tri, neighbors)
    edge_vertices = [segment[0] for segment in geometry.order_edges(edges)]
    num_vertices = len(edge_vertices)

    p1 = edge_vertices[0]
    p2 = edge_vertices[1]

    c1 = geometry.circumcenter(grid_loc, tri.points[p1], tri.points[p2])
    polygon = [c1]

    area_list = []
    total_area = 0.0

    for i in range(num_vertices):

        p3 = edge_vertices[(i + 2) % num_vertices]

        try:

            c2 = geometry.circumcenter(grid_loc, tri.points[p3], tri.points[p2])
            polygon.append(c2)

            for check_tri in neighbors:
                if p2 in tri.simplices[check_tri]:
                    polygon.append(triangle_info[check_tri]['cc'])

            pts = [polygon[i] for i in ConvexHull(polygon).vertices]
            value = variable[(tri.points[p2][0] == xp) & (tri.points[p2][1] == yp)]

            cur_area = geometry.area(pts)

            total_area += cur_area

            area_list.append(cur_area * value[0])

        except (ZeroDivisionError, qhull.QhullError) as e:
            message = ('Error during processing of a grid. '
                       'Interpolation will continue but be mindful '
                       'of errors in output. ') + str(e)

            log.warning(message)
            return np.nan

        polygon = [c2]

        p2 = p3

    return sum(x / total_area for x in area_list)