You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: clustering_coefficients.m
    path of file: ./repos/CBIG/external_packages/matlab/default_packages/matlab_bgl_mac64
    
    the code of the file until where you have to start completion: function ccfs = clustering_coefficients(A,varargin)
% CLUSTERING_COEFFICIENTS Compute the clustering coefficients for vertices.
%
% ccfs = clustering_coefficients(A) returns the clustering coefficients for
% all vertices in A.  The clustering coefficient is the ratio of the number
% of edges between a vertex's neighbors to the total possible number of 
% edges between the vertex's neighbors. 
%
% This method works on directed or undirected graphs.
% The runtime is O(nd^2) where d is the maximum vertex degree.
%
% ... = clustering_coefficients(A,...) takes a set of
% key-value pairs or an options structure.  See set_matlab_bgl_options
% for the standard options. 
%   options.undirected: enable optimizations for undirected graphs [{0} | 1]
%   options.unweighted: an optional switch to perform the weighted 
%       computation [{0} | 1], see Note
%   options.edge_weight: a double array over the edges with an edge

    
function ccfs = clustering_coefficients(A,varargin)
% CLUSTERING_COEFFICIENTS Compute the clustering coefficients for vertices.
%
% ccfs = clustering_coefficients(A) returns the clustering coefficients for
% all vertices in A.  The clustering coefficient is the ratio of the number
% of edges between a vertex's neighbors to the total possible number of 
% edges between the vertex's neighbors. 
%
% This method works on directed or undirected graphs.
% The runtime is O(nd^2) where d is the maximum vertex degree.
%
% ... = clustering_coefficients(A,...) takes a set of
% key-value pairs or an options structure.  See set_matlab_bgl_options
% for the standard options. 
%   options.undirected: enable optimizations for undirected graphs [{0} | 1]
%   options.unweighted: an optional switch to perform the weighted 
%       computation [{0} | 1], see Note
%   options.edge_weight: a double array over the edges with an edge
%       weight for each node, see EDGE_INDEX and EXAMPLES/REWEIGHTED_GRAPHS
%       for information on how to use this option correctly
%       [{'matrix'} | length(nnz(A)) double vector]
%
% Note: Prior to version 3.0, this function did not depend