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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def run(run_or_experiment, name=None, stop=None, config=None, resources_per_trial=None, num_samples=1, local_dir=None, upload_dir=None, trial_name_creator=None, loggers=None, sync_function=None, checkpoint_freq=0, checkpoint_at_end=False, export_formats=None, max_failures=3, restore=None, search_alg=None, scheduler=None, with_server=False, server_port=TuneServer.DEFAULT_PORT, verbose=2, resume=False, queue_trials=False, reuse_actors=False, trial_executor=None, raise_on_failed_trial=True):
 """Executes training. Args: run_or_experiment (function|class|str|Experiment):
 If function|class|str, this is the algorithm or model to train. This may refer to the name of a built-on algorithm (e.g. RLLib's DQN or PPO), a user-defined trainable function or class, or the string identifier of a trainable function or class registered in the tune registry. If Experiment, then Tune will execute training based on Experiment.spec. name (str):
 Name of experiment. stop (dict):
 The stopping criteria. The keys may be any field in the return result of 'train()', whichever is reached first. Defaults to empty dict. config (dict):
 Algorithm-specific configuration for Tune variant generation (e.g. env, hyperparams). Defaults to empty dict. Custom search algorithms may ignore this. resources_per_trial (dict):
 Machine resources to allocate per trial, e.g. ``{"cpu": 64, "gpu": 8}``. Note that GPUs will not be assigned unless you specify them here. Defaults to 1 CPU and 0 GPUs in ``Trainable.default_resource_request()``. num_samples (int):
 Number of times to sample from the hyperparameter space. Defaults to 1. If `grid_search` is provided as an argument, the grid will be repeated `num_samples` of times. local_dir (str):
 Local dir to save training results to. Defaults to ``~/ray_results``. upload_dir (str):
 Optional URI to sync training results to (e.g. ``s3://bucket``). trial_name_creator (func):
 Optional function for generating the trial string representation. loggers (list):
 List of logger creators to be used with each Trial. If None, defaults to ray.tune.logger.DEFAULT_LOGGERS. See `ray/tune/logger.py`. sync_function (func|str):
 Function for syncing the local_dir to upload_dir. If string, then it must be a string template for syncer to run. If not provided, the sync command defaults to standard S3 or gsutil sync comamnds. checkpoint_freq (int):
 How many training iterations between checkpoints. A value of 0 (default) disables checkpointing. checkpoint_at_end (bool):
 Whether to checkpoint at the end of the experiment regardless of the checkpoint_freq. Default is False. export_formats (list):
 List of formats that exported at the end of the experiment. Default is None. max_failures (int):
 Try to recover a trial from its last checkpoint at least this many times. Only applies if checkpointing is enabled. Setting to -1 will lead to infinite recovery retries. Defaults to 3. restore (str):
 Path to checkpoint. Only makes sense to set if running 1 trial. Defaults to None. search_alg (SearchAlgorithm):
 Search Algorithm. Defaults to BasicVariantGenerator. scheduler (TrialScheduler):
 Scheduler for executing the experiment. Choose among FIFO (default), MedianStopping, AsyncHyperBand, and HyperBand. with_server (bool):
 Starts a background Tune server. Needed for using the Client API. server_port (int):
 Port number for launching TuneServer. verbose (int):
 0, 1, or 2. Verbosity mode. 0 = silent, 1 = only status updates, 2 = status and trial results. resume (bool|"prompt"):
 If checkpoint exists, the experiment will resume from there. If resume is "prompt", Tune will prompt if checkpoint detected. queue_trials (bool):
 Whether to queue trials when the cluster does not currently have enough resources to launch one. This should be set to True when running on an autoscaling cluster to enable automatic scale-up. reuse_actors (bool):
 Whether to reuse actors between different trials when possible. This can drastically speed up experiments that start and stop actors often (e.g., PBT in time-multiplexing mode). This requires trials to have the same resource requirements. trial_executor (TrialExecutor):
 Manage the execution of trials. raise_on_failed_trial (bool):
 Raise TuneError if there exists failed trial (of ERROR state) when the experiments complete. Returns: List of Trial objects. Raises: TuneError if any trials failed and `raise_on_failed_trial` is True. Examples: "PG", num_samples=5, config={ "env": "CartPole-v0", "lr": tune.sample_from(lambda _: np.random.rand()) } ) """

    experiment = run_or_experiment
    if not isinstance(run_or_experiment, Experiment):
        experiment = Experiment(
            name, run_or_experiment, stop, config, resources_per_trial,
            num_samples, local_dir, upload_dir, trial_name_creator, loggers,
            sync_function, checkpoint_freq, checkpoint_at_end, export_formats,
            max_failures, restore)
    else:
        logger.debug("Ignoring some parameters passed into tune.run.")

    checkpoint_dir = _find_checkpoint_dir(experiment)
    should_restore = _prompt_restore(checkpoint_dir, resume)

    runner = None
    if should_restore:
        try:
            runner = TrialRunner.restore(checkpoint_dir, search_alg, scheduler,
                                         trial_executor)
        except Exception:
            logger.exception("Runner restore failed. Restarting experiment.")
    else:
        logger.info("Starting a new experiment.")

    if not runner:
        scheduler = scheduler or FIFOScheduler()
        search_alg = search_alg or BasicVariantGenerator()

        search_alg.add_configurations([experiment])

        runner = TrialRunner(
            search_alg,
            scheduler=scheduler,
            metadata_checkpoint_dir=checkpoint_dir,
            launch_web_server=with_server,
            server_port=server_port,
            verbose=bool(verbose > 1),
            queue_trials=queue_trials,
            reuse_actors=reuse_actors,
            trial_executor=trial_executor)

    if verbose:
        print(runner.debug_string(max_debug=99999))

    last_debug = 0
    while not runner.is_finished():
        runner.step()
        if time.time() - last_debug > DEBUG_PRINT_INTERVAL:
            if verbose:
                print(runner.debug_string())
            last_debug = time.time()

    if verbose:
        print(runner.debug_string(max_debug=99999))

    wait_for_log_sync()

    errored_trials = []
    for trial in runner.get_trials():
        if trial.status != Trial.TERMINATED:
            errored_trials += [trial]

    if errored_trials:
        if raise_on_failed_trial:
            raise TuneError("Trials did not complete", errored_trials)
        else:
            logger.error("Trials did not complete: %s", errored_trials)

    return runner.get_trials()