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/Coursera-Machine-Learning-Andrew-NG/machine-learning-ex1/ex1
    
    the code of the file until where you have to start completion: function submit()
  addpath('./lib');

  conf.assignmentSlug = 'linear-regression';
  conf.itemName = 'Linear Regression with Multiple Variables';
  conf.partArrays = { ...
    { ...
      '1', ...
      { 'warmUpExercise.m' }, ...
      'Warm-up Exercise', ...
    }, ...
    { ...
      '2', ...
      { 'computeCost.m' }, ...
      'Computing Cost (for One Variable)', ...

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

  conf.assignmentSlug = 'linear-regression';
  conf.itemName = 'Linear Regression with Multiple Variables';
  conf.partArrays = { ...
    { ...
      '1', ...
      { 'warmUpExercise.m' }, ...
      'Warm-up Exercise', ...
    }, ...
    { ...
      '2', ...
      { 'computeCost.m' }, ...
      'Computing Cost (for One Variable)', ...
    }, ...
    { ...
      '3', ...
      { 'gradientDescent.m' }, ...
      'Gradient Descent (for One Variable)', ...
    }, ...
    { ...
      '4', ...
      { 'featureNormalize.m' }, ...
      'Feature Normalization', ...
    }, ...
    { ...
      '5', ...
      { 'computeCostMulti.m' }, ...
      'Computing Cost (for Multiple Variables)', ...
    }, ...
    { ...
      '6', ...
      { 'gradientDescentMulti.m' }, ...
      'Gradient Descent (for Multiple Variables)', ...
    }, ...
    { ...
      '7', ...
      { 'normalEqn.m' }, ...
      'Normal Equations', ...
    }, ...
  };
  conf.output = @output;

  submitWithConfiguration(conf);
end

function out = output(partId)
  % Random Test Cases
  X1 = [ones(20,1) (exp(1) + exp(2) * (0.1:0.1:2))'];
  Y1 = X1(:,2) + sin(X1(:,1)) + cos(X1(:,2));
  X2 = [X1 X1(:,2).^0.5 X1(:,2).^0.25];
  Y2 = Y1.^0.5 + Y1;
  if partId == '1'
    out = sprintf('%0.5f ', warmUpExercise());
  elseif partId == '2'
    out = sprintf('%0.5f ', computeCost(X1, Y1, [0.5 -0.5]'));
  elseif partId == '3'
    out = sprintf('%0.5f ', gradientDescent(X1, Y1, [0.5 -0.5]', 0.01, 10));
  elseif partId == '4'
    out = sprintf('%0.5f ', featureNormalize(X2(:,2:4)));
  elseif partId == '5'
    out = sprintf('%0.5f ', computeCostMulti(X2, Y2, [0.1 0.2 0.3 0.4]'));
  elseif partId == '6'
    out = sprintf('%0.5f ', gradientDescentMulti(X2, Y2, [-0.1 -0.2 -0.3 -0.4]', 0.01, 10));
  elseif partId == '7'
    out = sprintf('%0.5f ', normalEqn(X2, Y2));
  end