parameter vector that we're meta-learning and Phi I star is the optimal parameter vector for task I am then you can view the meta learning process as a thick black line where when we're at this point during Mediterranean we take a gradient step with respect to task 3 we're quite far from the optimum for task 3 whereas at the end of meta learning if we take a gradient up with respect to toss 3 or quite close to the optimum and likewise for a number of other tasks we refer to this procedure as model agnostic medal earning in the