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: randInitializeWeights.m
    path of file: ./repos/digit-recognition/Code
    
    the code of the file until where you have to start completion: function W = randInitializeWeights(L_in, L_out)
%RANDINITIALIZEWEIGHTS Randomly initialize the weights of a layer with L_in
%incoming connections and L_out outgoing connections
%   W = RANDINITIALIZEWEIGHTS(L_in, L_out) randomly initializes the weights 
%   of a layer with L_in incoming connections and L_out outgoing 
%   connections. 
%
%   Note that W should be set to a matrix of size(L_out, 1 + L_in) as
%   the first column of W handles the "bias" terms
%

% We need to return the following variables correctly 
W = zeros(L_out, 1 + L_in);

% ====================== CODE ======================
% Instructions: Initialize W randomly so that we break the symmetry while
%               training the neural network.
%
% Note: The first column of W corresponds to the parameters for the bias unit
%


% Randomly initialize the weights to small values
epsilon_init = 0.09;
W = rand(L_out, 1 + L_in) * 2 * epsilon_init - epsilon_init; 

    
function W = randInitializeWeights(L_in, L_out)
%RANDINITIALIZEWEIGHTS Randomly initialize the weights of a layer with L_in
%incoming connections and L_out outgoing connections
%   W = RANDINITIALIZEWEIGHTS(L_in, L_out) randomly initializes the weights 
%   of a layer with L_in incoming connections and L_out outgoing 
%   connections. 
%
%   Note that W should be set to a matrix of size(L_out, 1 + L_in) as
%   the first column of W handles the "bias" terms
%

% We need to return the following variables correctly 
W = zeros(L_out, 1 + L_in);

% ====================== CODE ======================
% Instructions: Initialize W randomly so that we break the symmetry while
%               training the neural network.
%
% Note: The first column of W corresponds to the parameters for the bias unit
%


% Randomly initialize the weights to small values
epsilon_init = 0.09;
W = rand(L_out, 1 + L_in) * 2 * epsilon_init - epsilon_init; 

% =========================================================================

end