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_nndropout.m
    path of file: ./repos/Image-Text-Embedding/matlab
    
    the code of the file until where you have to start completion: function [y,mask] = vl_nndropout(x,varargin)
%VL_NNDROPOUT CNN dropout.
%   [Y,MASK] = VL_NNDROPOUT(X) applies dropout to the data X. MASK
%   is the randomly sampled dropout mask. Both Y and MASK have the
%   same size as X.
%
%   VL_NNDROPOUT(X, 'rate', R) sets the dropout rate to R. Rate is defined
%   as a probability of a variable *not* to be zeroed (i.e. it is the
%   expected value of MASK).
%
%   [DZDX] = VL_NNDROPOUT(X, DZDY, 'mask', MASK) computes the
%   derivatives of the blocks projected onto DZDY. Note that MASK must

    
function [y,mask] = vl_nndropout(x,varargin)
%VL_NNDROPOUT CNN dropout.
%   [Y,MASK] = VL_NNDROPOUT(X) applies dropout to the data X. MASK
%   is the randomly sampled dropout mask. Both Y and MASK have the
%   same size as X.
%
%   VL_NNDROPOUT(X, 'rate', R) sets the dropout rate to R. Rate is defined
%   as a probability of a variable *not* to be zeroed (i.e. it is the
%   expected value of MASK).
%
%   [DZDX] = VL_NNDROPOUT(X, DZDY, 'mask', MASK) computes the
%   derivatives of the blocks projected onto DZDY. Note that MASK must
%   be specified in order to compute the derivative consistently with
%   the MASK randomly sampled in the forward pass. DZDX and DZDY have
%   the same dimesnions as X and Y respectivey.
%
%   Note that in the original paper on dropout, at test time the
%   network weights for the dropout layers are scaled down to
%   compensate for having all the neurons active. In this
%   implementation the dropout function itself already does this
%   compensation during training. So at test time no alterations are
%   required.

% Copyright (C) 2014-16 Andrea Vedaldi, Karel Lenc.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).

opts.rate = 0.5 ;
opts.mask = [] ;

backMode = numel(varargin) > 0 && ~ischar(varargin{1}) ;
if backMode
  dzdy = varargin{1} ;
  opts = vl_argparse(opts, varargin(2:end