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: cnn_imagenet_get_batch.m
    path of file: ./repos/cnn-for-image-retrieval/matconvnet-1.0-beta18/examples/imagenet
    
    the code of the file until where you have to start completion: function imo = cnn_imagenet_get_batch(images, varargin)
% CNN_IMAGENET_GET_BATCH  Load, preprocess, and pack images for CNN evaluation

opts.imageSize = [227, 227] ;
opts.border = [29, 29] ;
opts.keepAspect = true ;
opts.numAugments = 1 ;
opts.transformation = 'none' ;
opts.averageImage = [] ;
opts.rgbVariance = zeros(0,3,'single') ;
opts.interpolation = 'bilinear' ;
opts.numThreads = 1 ;
opts.prefetch = false ;
opts = vl_argparse(opts, varargin);

% fetch is true if images is a list of filenames (instead of
% a cell array of images)
fetch = numel(images) >= 1 && ischar(images{1}) ;

% prefetch is used to load images in a separate thread

    
function imo = cnn_imagenet_get_batch(images, varargin)
% CNN_IMAGENET_GET_BATCH  Load, preprocess, and pack images for CNN evaluation

opts.imageSize = [227, 227] ;
opts.border = [29, 29] ;
opts.keepAspect = true ;
opts.numAugments = 1 ;
opts.transformation = 'none' ;
opts.averageImage = [] ;
opts.rgbVariance = zeros(0,3,'single') ;
opts.interpolation = 'bilinear' ;
opts.numThreads = 1 ;
opts.prefetch = false ;
opts = vl_argparse(opts, varargin);

% fetch is true if images is a list of filenames (instead of
% a cell array of images)
fetch = numel(images) >= 1 && ischar(images{1}) ;

% prefetch is used to load images in a separate thread
prefetch = fetch & opts.prefetch ;

if prefetch
  vl_imreadjpeg(images, 'numThreads', opts.numThreads, 'prefetch') ;
  imo = [] ;
  return ;
end