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: mlppak.m
    path of file: ./repos/bnt/netlab3.3
    
    the code of the file until where you have to start completion: function w = mlppak(net)
%MLPPAK	Combines weights and biases into one weights vector.
%
%	Description
%	W = MLPPAK(NET) takes a network data structure NET and combines the
%	component weight matrices bias vectors into a single row vector W.
%	The facility to switch between these two representations for the
%	network parameters is useful, for example, in training a network by
%	error function minimization, since a single vector of parameters can
%	be handled by general-purpose optimization routines.
%
%	The ordering of the paramters in W is defined by
%	  w = [net.w1(:)', net.b1, net.w2(:)', net.b2];
%	 where W1 is the first-layer weight matrix, B1 is the first-layer
%	bias vector, W2 is the second-layer weight matrix, and B2 is the
%	second-layer bias vector.
%
%	See also
%	MLP, MLPUNPAK, MLPFWD, MLPERR, MLPBKP, MLPGRAD
%

%	Copyright (c) Ian T Nabney (1996-2001)


    
function w = mlppak(net)
%MLPPAK	Combines weights and biases into one weights vector.
%
%	Description
%	W = MLPPAK(NET) takes a network data structure NET and combines the
%	component weight matrices bias vectors into a single row vector W.
%	The facility to switch between these two representations for the
%	network parameters is useful, for example, in training a network by
%	error function minimization, since a single vector of parameters can
%	be handled by general-purpose optimization routines.
%
%	The ordering of the paramters in W is defined by
%	  w = [net.w1(:)', net.b1, net.w2(:)', net.b2];
%	 where W1 is the first-layer weight matrix, B1 is the first-layer
%	bias vector, W2 is the second-layer weight matrix, and B2 is the
%	second-layer bias vector.
%
%	See also
%	MLP, MLPUNPAK, MLPFWD, MLPERR, MLPBKP, MLPGRAD
%

%	Copyright (c) Ian T Nabney (1996-2001)

% Check arguments for consistency
errstring = consist(net, 'mlp');
if ~isempty(errstring);
  error(errstring);
end