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: svmtrain.m
    path of file: ./repos/CS_MoCo_LAB/reconstruction/matlab/CS_LAB_matlab/utils/utils_SURF/toolbox/noprefix
    
    the code of the file until where you have to start completion: function varargout = svmtrain(varargin)
% VL_SVMTRAIN   Train a Support Vector Machine
%   [W B] = VL_SVMTRAIN(X, Y, LAMBDA) trains a linear Support Vector
%   Machine (SVM) from the data vectors X and the labels Y. X is a D
%   by N matrix, with one column per example and D feature dimensions
%   (SINGLE or DOUBLE). Y is a DOUBLE vector with N elements with a
%   binary (-1 or +1) label for each training point. To a first order
%   approximation, the function computes a weight vector W and offset
%   B such that the score W'*X(:,i)+B has the same sign of LABELS(i)
%   for all i.
%
%   VL_SVMTRAIN(DATASET, LABELS, LAMBDA) takes as input a DATASET
%   structure, which allows more sophisticated input formats to be
%   supported (see VL_SVMDATASET()).
%
%   [W, B, INFO] = VL_SVMTRAIN(...) additionally returns a structure
%   INFO with the following fields:
%
%   iteration::
%     Number of iterations performed.
%
%   epoch::
%     Number of iterations over number of training data points.
%
%   elapsedTime::
%     Time elapsed since the start of training.

    
function varargout = svmtrain(varargin)
% VL_SVMTRAIN   Train a Support Vector Machine
%   [W B] = VL_SVMTRAIN(X, Y, LAMBDA) trains a linear Support Vector
%   Machine (SVM) from the data vectors X and the labels Y. X is a D
%   by N matrix, with one column per example and D feature dimensions
%   (SINGLE or DOUBLE). Y is a DOUBLE vector with N elements with a
%   binary (-1 or +1) label for each training point. To a first order
%   approximation, the function computes a weight vector W and offset
%   B such that the score W'*X(:,i)+B has the same sign of LABELS(i)
%   for all i.
%
%   VL_SVMTRAIN(DATASET, LABELS, LAMBDA) takes as input a DATASET
%   structure, which allows more sophisticated input formats to be
%   supported (see VL_SVMDATASET()).
%
%   [W, B, INFO] = VL_SVMTRAIN(...) additionally returns a structure
%   INFO with the following fields:
%
%   iteration::
%     Number of iterations performed.
%
%   epoch::
%     Number of iterations over number of training data points.
%
%   elapsedTime::
%     Time elapsed since the start of training.
%
%   objective::
%     SVM objective value.
%
%   regularizer::
%     Regularizer value.
%
%   loss::
%     Loss value.
%
%   scoreVariation:: [SGD only]
%     Mean square root of the difference between the last two
%     values of the SVM scores for each point.
%
%   dualObjective:: [SDCA only]
%     Dual objective value.
%
%   dualLoss:: [SDCA only]
%     Dual loss value::
%
%   dualityGap:: [SDCA only]
%     Difference between the objective and the dual objective.
%
%   [W, B, INFO, SCORES] = VL_SVMTRAIN(X, Y, LABMDA) returns a row
%   vector of the SVM score for each training point. This can be used
%   in combination with the options SOLVER, MODEL, and BIAS to
%   evaluate an existing SVM on new data points. Furthermore INFO will
%   contain the corresponding SVM loss, regularizer, and objective
%   function value. If this information is not of interest, it is
%   possible to pass a null vector Y instead of the actual labels as
%   well as a null regularizer.
%
%   VL_SVMTRAIN() accepts the following options:
%
%   Verbose::
%     Specify one or multiple times to increase the verbosity level.
%     Given only once, produces messages at the beginning and end