code: def squared_error(eval_data, predictions, scores='ignored', learner='ignored'):
      '''
      Return the squared error of each prediction in `predictions` with respect
      to the correct output in `eval_data`.
  
      >>> data = [Instance('input', (0., 0., 1.)),
      ...         Instance('input', (0., 1., 1.)),
      ...         Instance('input', (1., 0., 0.))]
      >>> squared_error(data, [(0., 1., 1.), (0., 1., 1.), (-1., 1., 0.)])
      [1.0, 0.0, 5.0]
      '''
      return [np.sum((np.array(pred) - np.array(inst.output)) ** 2)
              for inst, pred in zip(eval_data, predictions)f squared_error(eval_data, predictions, scores='ignored', learner='ignored'):
      '''
      Return the squared error of each prediction in `predictions` with respect
      to the correct output in `eval_data`.
  
      >>> data = [Instance('input', (0., 0., 1.)),
      ...         Instance('input', (0., 1., 1.)),
      ...         Instance('input', (1., 0., 0.))]
      >>> squared_error(data, [(0., 1., 1.), (0., 1., 1.), (-1., 1., 0.)])
      [1.0, 0.0, 5.0]
      '''
      return [np.sum((np.array(pred) - np.array(inst.output)) ** 2)
              for inst, pred in zip(eval_data, predictions)]
docs: Return the squared error of each prediction in `predictions` with respect
      to the correct output in `eval_data`.
  
      >>> data = [Instance('input', (0., 0., 1.)),
      ...         Instance('input', (0., 1., 1.)),
      ...         Instance('input', (1., 0., 0.))]
      >>> squared_error(data, [(0., 1., 1.), (0., 1., 1.), (-1., 1., 0.)])
      [1.0, 0.0, 5.0]