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: logLikelihoodErbBSignaling.m
    path of file: ./repos/PESTO/examples/erbb_signaling
    
    the code of the file until where you have to start completion: function varargout = logLikelihoodErbBSignaling(theta, D)
% Objective function for examples/erbb_signaling
%
% logLikelihoodErbBSignaling.m provides the log-likelihood and its gradient 
% for the model defined in erbb_signaling_pesto_syms.m
% 
% USAGE:
% [llh] = logLikelihoodErbBSignaling(theta, amiData)
% [llh, sllh] = logLikelihoodErbBSignaling(theta, amiData)
%
% Parameters:
%  theta: Model parameters 
%  amiData: an amidata object for the AMICI solver
%
% Return values:
%   varargout:
%     llh: Log-Likelihood, only the LogLikelihood will be returned, no 
%         sensitivity analysis is performed
%     sllh: Gradient of llh, The LogLikelihood and its gradient will be 
%         returned, first order adjoint sensitivity analysis is performed

    
function varargout = logLikelihoodErbBSignaling(theta, D)
% Objective function for examples/erbb_signaling
%
% logLikelihoodErbBSignaling.m provides the log-likelihood and its gradient 
% for the model defined in erbb_signaling_pesto_syms.m
% 
% USAGE:
% [llh] = logLikelihoodErbBSignaling(theta, amiData)
% [llh, sllh] = logLikelihoodErbBSignaling(theta, amiData)
%
% Parameters:
%  theta: Model parameters 
%  amiData: an amidata object for the AMICI solver
%
% Return values:
%   varargout:
%     llh: Log-Likelihood, only the LogLikelihood will be returned, no 
%         sensitivity analysis is performed
%     sllh: Gradient of llh, The LogLikelihood and its gradient will be 
%         returned, first order adjoint sensitivity analysis is performed



%% Model Definition
% The ODE model is set up using the AMICI toolbox. To access the AMICI
% model setup, see erbb_signaling_pesto_syms.m
% For a detailed description for the biological model see the referenced
% papers on the ErbB signaling pathways by Chen et al.

%% AMICI
% Setting the options for the AMICI solver
optionsAmici = amioption;
optionsAmici.atol = 1e-8;
optionsAmici.maxsteps = 2e5;
optionsAmici.interpType = 2;

if(nargout > 1)
    optionsAmici.sensi = 1;
    optionsAmici.sensi_meth = 'adjoint';
    sol = simulate_erbb_pesto(D.t, theta, D.condition, D, optionsAmici);
    if(sol.status < 0)
        error('integration error');
    else
        varargout{1} = sol.llh;
        varargout{2} = sol.sllh;
    end