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: submit.m
    path of file: ./repos/Machine-Learning--Coursera/machine-learning-ex3/ex3
    
    the code of the file until where you have to start completion: function submit()
  addpath('./lib');

  conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
  conf.itemName = 'Multi-class Classification and Neural Networks';
  conf.partArrays = { ...
    { ...
      '1', ...
      { 'lrCostFunction.m' }, ...
      'Regularized Logistic Regression', ...
    }, ...
    { ...
      '2', ...
      { 'oneVsAll.m' }, ...
      'One-vs-All Classifier Training', ...
    }, ...
    { ...
      '3', ...
      { 'predictOneVsAll.m' }, ...
      'One-vs-All Classifier Prediction', ...
    }, ...
    { ...
      '4', ...
      { 'predict.m' }, ...

    
function submit()
  addpath('./lib');

  conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
  conf.itemName = 'Multi-class Classification and Neural Networks';
  conf.partArrays = { ...
    { ...
      '1', ...
      { 'lrCostFunction.m' }, ...
      'Regularized Logistic Regression', ...
    }, ...
    { ...
      '2', ...
      { 'oneVsAll.m' }, ...
      'One-vs-All Classifier Training', ...
    }, ...
    { ...
      '3', ...
      { 'predictOneVsAll.m' }, ...
      'One-vs-All Classifier Prediction', ...
    }, ...
    { ...
      '4', ...
      { 'predict.m' }, ...
      'Neural Network Prediction Function' ...
    }, ...
  };
  conf.output = @output;

  submitWithConfiguration(conf);
end

function out = output(partId, auxdata)
  % Random Test Cases
  X = [ones(20,1) (exp(1) * sin(1:1:20))' (exp(0.5) * cos(1:1:20))'];
  y = sin(X(:,1) + X(:,2)) > 0;
  Xm = [ -1 -1 ; -1 -2 ; -2 -1 ; -2 -2 ; ...
          1 1 ;  1 2 ;  2 1 ; 2 2 ; ...
         -1 1 ;  -1 2 ;  -2 1 ; -2 2 ; ...
          1 -1 ; 1 -2 ;  -2 -1 ; -2 -2 ];
  ym = [ 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 ]';
  t1 = sin(reshape(1:2:24, 4, 3));
  t2 = cos(reshape(1:2:40, 4, 5));

  if partId == '1'
    [J, grad] = lrCostFunction([0.25 0.5 -0.5]', X, y, 0.1);
    out = sprintf('%0.5f ', J);
    out = [out sprintf('%0.5f ', grad)];
  elseif partId == '2'
    out = sprintf('%0.5f ', oneVsAll(Xm, ym, 4, 0.1));
  elseif partId == '3'
    out = sprintf('%0.5f ', predictOneVsAll(t1, Xm));
  elseif partId == '4'
    out = sprintf('%0.5f ', predict(t1, t2, Xm));
  end