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_simplenn.m
    path of file: ./repos/Image-Text-Embedding/matlab/simplenn
    
    the code of the file until where you have to start completion: function res = vl_simplenn(net, x, dzdy, res, varargin)
%VL_SIMPLENN  Evaluate a SimpleNN network.
%   RES = VL_SIMPLENN(NET, X) evaluates the convnet NET on data X.
%   RES = VL_SIMPLENN(NET, X, DZDY) evaluates the convnent NET and its
%   derivative on data X and output derivative DZDY (foward+bacwkard pass).
%   RES = VL_SIMPLENN(NET, X, [], RES) evaluates the NET on X reusing the
%   structure RES.
%   RES = VL_SIMPLENN(NET, X, DZDY, RES) evaluates the NET on X and its
%   derivatives reusing the structure RES.
%
%   This function process networks using the SimpleNN wrapper
%   format. Such networks are 'simple' in the sense that they consist
%   of a linear sequence of computational layers. You can use the
%   `dagnn.DagNN` wrapper for more complex topologies, or write your
%   own wrapper around MatConvNet computational blocks for even
%   greater flexibility.
%
%   The format of the network structure NET and of the result
%   structure RES are described in some detail below. Most networks
%   expect the input data X to be standardized, for example by
%   rescaling the input image(s) and subtracting a mean. Doing so is
%   left to the user, but information on how to do this is usually
%   contained in the `net.meta` field of the NET structure (see
%   below).
%
%   The NET structure needs to be updated as new features are
%   introduced in MatConvNet; use the `VL_SIMPLENN_TIDY()` function
%   to make an old network current, as well as to cleanup and check
%   the structure of an existing network.
%
%   Networks can run either on the CPU or GPU. Use VL_SIMPLENN_MOVE()
%   to move the network parameters between these devices.
%
%   To print or obtain summary of the network structure, use the
%   VL_SIMPLENN_DISPLAY() function.
%
%   VL_SIMPLENN(NET, X, DZDY, RES, 'OPT', VAL, ...) takes the following
%   options:
%
%   `Mode`:: `'normal'`
%      Specifies the mode of operation. It can be either `'normal'` or
%      `'test'`. In test mode, dropout and batch-normalization are

    
function res = vl_simplenn(net, x, dzdy, res, varargin)
%VL_SIMPLENN  Evaluate a SimpleNN network.
%   RES = VL_SIMPLENN(NET, X) evaluates the convnet NET on data X.
%   RES = VL_SIMPLENN(NET, X, DZDY) evaluates the convnent NET and its
%   derivative on data X and output derivative DZDY (foward+bacwkard pass).
%   RES = VL_SIMPLENN(NET, X, [], RES) evaluates the NET on X reusing the
%   structure RES.
%   RES = VL_SIMPLENN(NET, X, DZDY, RES) evaluates the NET on X and its
%   derivatives reusing the structure RES.
%
%   This function process networks using the SimpleNN wrapper
%   format. Such networks are 'simple' in the sense that they consist
%   of a linear sequence of computational layers. You can use the
%   `dagnn.DagNN` wrapper for more complex topologies, or write your
%   own wrapper around MatConvNet computational blocks for even
%   greater flexibility.
%
%   The format of the network structure NET and of the result
%   structure RES are described in some detail below. Most networks
%   expect the input data X to be standardized, for example by
%   rescaling the input image(s) and subtracting a mean. Doing so is
%   left to the user, but information on how to do this is usually
%   contained in the `net.meta` field of the NET structure (see
%   below).
%
%   The NET structure needs to be updated as new features are
%   introduced in MatConvNet; use the `VL_SIMPLENN_TIDY()` function
%   to make an old network current, as well as to cleanup and check
%   the structure of an existing network.
%
%   Networks can run either on the CPU or GPU. Use VL_SIMPLENN_MOVE()
%   to move the network parameters between these devices.
%
%   To print or obtain summary of the network structure, use the
%   VL_SIMPLENN_DISPLAY() function.
%
%   VL_SIMPLENN(NET, X, DZDY, RES, 'OPT', VAL, ...) takes the following
%   options:
%
%   `Mode`:: `'normal'`
%      Specifies the mode of operation. It can be either `'normal'` or
%      `'test'`. In test mode, dropout and batch-normalization are
%      bypassed. Note that, when a network is deployed, it may be
%      preferable to *remove* such blocks altogether.
%
%   `ConserveMemory`:: `false`
%      Aggressively delete intermediate results. This in practice has
%      a very small performance hit and allows training much larger
%      models. However, it can be useful to disable it for
%      debugging. Keeps the values in `res(1)` (input) and `res(end