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: KLSH_learn.m
    path of file: ./repos/MatlabFunc/ANNS/Hashing/Unsupervised
    
    the code of the file until where you have to start completion: function [model, B,elapse] = KLSH_learn(A, maxbits, Landmarks)
%   This is a function of Kernelized LSH learning.
%   The  KLSH TPAMI did not mension how to constrct the kernel. We follow
%   the method in Complmentary Projection Hashing ICCV 2013.
%
%	Usage:
%	[model, B,elapse] = KLSH_learn(A, maxbits)
%
%	      A: Rows of vectors of data points. Each row is sample point
%   maxbits: Code length
% Landmarks: Landmarks (anchors), Each row is sample point
%
%     model: Used for encoding a test sample point.
%	      B: The binary code of the input data A. Each row is sample point
%    elapse: The coding time (training time).
%
%
%

    
function [model, B,elapse] = KLSH_learn(A, maxbits, Landmarks)
%   This is a function of Kernelized LSH learning.
%   The  KLSH TPAMI did not mension how to constrct the kernel. We follow
%   the method in Complmentary Projection Hashing ICCV 2013.
%
%	Usage:
%	[model, B,elapse] = KLSH_learn(A, maxbits)
%
%	      A: Rows of vectors of data points. Each row is sample point
%   maxbits: Code length
% Landmarks: Landmarks (anchors), Each row is sample point
%
%     model: Used for encoding a test sample point.
%	      B: The binary code of the input data A. Each row is sample point
%    elapse: The coding time (training time).
%
%
%
%   version 2.0 --Nov/2016 
%   version 1.0 --Jan/2012 
%
%   Written by  Yue Lin (linyue29@gmail.com)
%               Deng Cai (dengcai AT gmail DOT com) 
%                                             

tmp_T = tic;

nLandmarks = 1500;
if ~exist('Landmarks','var')
    [~,Landmarks]=litekmeans(A,nLandmarks,'MaxIter',5,'Replicates',1);
end