representing F theta and then we'll take the outputted parameters use those parameters for another neural network that's gonna make predictions about test data points and so these are gonna be the data points from D test okay and then once we have this model we can train it with standard supervised learning this is just a standard recurrent neural network so we can train it to maximize a log probability of the labels of the test data points given the test inputs and we can do this optimization across all of the tasks in