like so and then once we have D train and D test will compute the parameters using D train and then evaluate those predicted parameters using the test data points and it's quite important that d'être and D tests are disjoint so that we're not training for memorization of the labels but instead training for generalization and then once we update our meta parameters of them were of course going to repeat this process for new tasks and if we use a mini batch of tasks the gradient in step four is gonna be averaged across that mini batch great