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: spm_vb_glmar.m
    path of file: ./repos/spm
    
    the code of the file until where you have to start completion: function [block] = spm_vb_glmar (Y,block)
% Variational Bayes for GLM-AR modelling in a block of fMRI
% FORMAT [block] = spm_vb_glmar (Y,block)
%
% Y     -  [T x N] time series with T time points, N voxels
%
% block -  data structure containing the following fields:
%
%          .X              [T x k] the design matrix
%          .p              order of AR model
%          .D              [N x N] spatial precision matrix
%                          (see spm_vb_set_priors.m)
%
%          The above fields are mandatory. The fields below are
%          optional or are filled in by this function.
%
%          OPTIMISIATION PARAMETERS:
%
%          .tol            termination tolerance (default = 0.01% increase in F)
%          .maxits         maximum number of iterations (default=4)
%          .verbose        '1' for description of actions (default=1)
%          .update_???     set to 1 to update parameter ??? (set to 0 to fix)
%                          eg. update_alpha=1; % update prior precision on W
%
%          ESTIMATED REGRESSION COEFFICIENTS:
%
%          .wk_mean        [k x N] VB regression coefficients
%          .wk_ols         [k x N] OLS "  "
%          .w_cov          N-element cell array with entries [k x k]
%          .w_dev          [k x N] standard deviation of regression coeffs
%
%          ESTIMATED AR COEFFICIENTS:
%
%          .ap_mean        [p x N] VB AR coefficients
%          .ap_ols         [p x N] OLS AR coefficients
%          .a_cov          N-element cell array with entries [p x p]
%
%          ESTIMATED NOISE PRECISION:
%
%          .b_lambda       [N x 1] temporal noise precisions
%          .c_lambda
%          .mean_lambda
%
%          MODEL COMPARISON AND COEFFICIENT RESELS:
%
%          .gamma_tot      [k x 1] Coefficient RESELS
%          .F              Negative free energy (used for model selection)
%          .F_record       [its x 1] record of F at each iteration
%          .elapsed_seconds  estimation time
%          PRIORS:
%
%          .b_alpha        [k x 1] spatial prior precisions for W
%          .c_alpha
%          .mean_alpha
%
%          .b_beta         [p x 1] spatial prior precisions for AR
%          .c_beta
%          .mean_beta
%
%          .b              [k x N] prior precision matrix
%

    
function [block] = spm_vb_glmar (Y,block)
% Variational Bayes for GLM-AR modelling in a block of fMRI
% FORMAT [block] = spm_vb_glmar (Y,block)
%
% Y     -  [T x N] time series with T time points, N voxels
%
% block -  data structure containing the following fields:
%
%          .X              [T x k] the design matrix
%          .p              order of AR model
%          .D              [N x N] spatial precision matrix
%                          (see spm_vb_set_priors.m)
%
%          The above fields are mandatory. The fields below are
%          optional or are filled in by this function.
%
%          OPTIMISIATION PARAMETERS:
%
%          .tol            termination tolerance (default = 0.01% increase in F)
%          .maxits         maximum number of iterations (default=4)
%          .verbose        '1' for description of actions (default=1)
%          .update_???     set to 1 to update parameter ??? (set to 0 to fix)
%                          eg. update_alpha=1; % update prior precision on W
%
%          ESTIMATED REGRESSION COEFFICIENTS:
%
%          .wk_mean        [k x N] VB regression coefficients
%          .wk_ols         [k x N] OLS "  "
%          .w_cov          N-element cell array with entries [k x k]
%          .w_dev          [k x N] standard deviation of regression coeffs
%
%          ESTIMATED AR COEFFICIENTS:
%
%          .ap_mean        [p x N] VB AR coefficients
%          .ap_ols         [p x N] OLS AR coefficients
%          .a_cov          N-element cell array with entries [p x p]
%
%          ESTIMATED NOISE PRECISION:
%
%          .b_lambda       [N x 1] temporal noise precisions
%          .c_lambda
%          .mean_lambda
%
%          MODEL COMPARISON AND COEFFICIENT RESELS:
%
%          .gamma_tot      [k x 1] Coefficient RESELS
%          .F              Negative free energy (used for model selection)
%          .F_record       [its x 1] record of F at each iteration
%          .elapsed_seconds  estimation time
%          PRIORS:
%
%          .b_alpha        [k x 1] spatial prior precisions for W
%          .c_alpha
%          .mean_alpha
%
%          .b_beta         [p x 1] spatial prior precisions for AR
%          .c_beta
%          .mean_beta
%
%          .b              [k x N] prior precision matrix
%
%          HYPERPRIORS:
%
%          .b_alpha_prior   priors on alpha
%          .c_alpha_prior
%
%          .b_beta_prior    priors on beta
%          .c_beta_prior
%
%          .b_lambda_prior  priors on temporal noise precisions
%          .c_lambda_prior
%
%          There are other fields that are used internally
%__________________________________________________________________________

% Will Penny and Nelson Trujillo-Barreto
% Copyright (C) 2005-2022 Wellcome Centre for Human Neuroimaging


t0 = clock;

%-Set defaults
%--------------------------------------------------------------------------
[T,N]   = size(Y);
block.T = T;
block.N = N;

try
    X = block.X;
catch
    error('Mandatory field X missing.');
end