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: vl_roc.m
    path of file: ./repos/robotic_perception/Trafic_Sign_Recognition/code/vlfeat-0.9.20-bin/vlfeat-0.9.20/toolbox/plotop
    
    the code of the file until where you have to start completion: function [tpr,tnr,info] = vl_roc(labels, scores, varargin)
%VL_ROC   ROC curve.
%   [TPR,TNR] = VL_ROC(LABELS, SCORES) computes the Receiver Operating
%   Characteristic (ROC) curve [1]. LABELS is a row vector of ground
%   truth labels, gr
    
function interprets such samples as `not
%     retrieved'). This feature can be used to evaluate the
%     performance of a large-scale retrieval experiment in which only
%     a subset of highly-scoring results are recorded for efficiency
%     reason.
%
%   Stable:: false
%     If set to true, TPR and TNR are returned in the same order
%     of LABELS and SCORES rather than being sorted by decreasing
%     score.
%
%   About the ROC curve::
%     Consider a classifier that predicts as positive all samples whose
%     score is not smaller than a threshold S. The ROC curve represents
%     the performance of such classifier as the threshold S is
%     changed. Formally, define
%
%       P = overall num. of positive samples,
%       N = overall num. of negative samples,
%
%     and for each threshold S
%
%       TP(S) = num. of samples that are correctly classified as positive,
%       TN(S) = num. of samples that are correctly classified as negative,
%       FP(S) = num. of samples that are incorrectly classified as positive,
%       FN(S) = num. of samples that are incorrectly classified as negative.
%
%     Consider also the rates:
%
%       TPR = TP(S) / P,      FNR = FN(S) / P,
%       TNR = TN(S) / N,      FPR = FP(S) / N,
%
%     and notice that, by definition,
%
%       P = TP(S) + FN(S) ,    N = TN(S) + FP(S),
%       1 = TPR(S) + FNR(S),   1 = TNR(S) + FPR(S).
%
%     The ROC curve is the parametric curve (FPR(S), TPR(S)) obtained
%     as the classifier threshold S is varied in the reals. The TPR is
%     the same as `recall' in a PR curve (see VL_PR()).
%
%     The ROC curve is contained in the square with vertices (0,0) The
%     (average) ROC curve of a random classifier is a line which
%     connects (1,0) and (0,1).
%
%     The ROC curve is independ