from ray import tune def my_trainable(config): for i in range(config["iterations"]): print("Iteration", i) trial = tune.Trial( trainable=my_trainable, config={"iterations": 10}, stop={"training_iteration": 5}, trial_name="my_trial" ) runner = tune.TrialRunner() runner.add_trial(trial)In this example, we define a simple `trainable` function that prints out a message for each iteration of the trial. We set the number of iterations to 10 and specify that the trial should stop after 5 iterations. Finally, we create a `TrialRunner` object and add the trial to it using `add_trial`. Overall, the `ray.tune.trial_runner.TrialRunner` is a powerful tool for managing and executing trials in a search algorithm.