# Buggy code
def _wait_for_and_process_task(self, task):
    """Wait for a task to be ready and process the task.

    Args:
        task: The task to execute.
    """
    function_id = task.function_id()
    # Wait until the function to be executed has actually been registered
    # on this worker. We will push warnings to the user if we spend too
    # long in this loop.
    with log_span("ray:wait_for_function", worker=self):
        self._wait_for_function(function_id, task.driver_id().id())

    # Execute the task.
    # TODO(rkn): Consider acquiring this lock with a timeout and pushing a
    # warning to the user if we are waiting too long to acquire the lock
    # because that may indicate that the system is hanging, and it'd be
    # good to know where the system is hanging.
    log(event_type="ray:acquire_lock", kind=LOG_SPAN_START, worker=self)
    with self.lock:
        log(event_type="ray:acquire_lock", kind=LOG_SPAN_END, worker=self)

        function_name, _ = self.functions[task.driver_id().id()][function_id.id()]
        contents = {
            "function_name": function_name,
            "task_id": task.task_id().hex(),
            "worker_id": binary_to_hex(self.worker_id),
        }
        with log_span("ray:task", contents=contents, worker=self):
            self._process_task(task)

    # Push all of the log events to the global state store.
    flush_log()

    # Increase the task execution counter.
    (self.num_task_executions[task.driver_id().id()][function_id.id()]) += 1

    reached_max_executions = (
        self.num_task_executions[task.driver_id().id()][function_id.id()]
        == self.function_properties[task.driver_id().id()][function_id.id()].max_calls
    )
    if reached_max_executions:
        ray.worker.global_worker.local_scheduler_client.disconnect()
        os._exit(0)


# Traceback (ImportError)
Traceback (most recent call last):
File "/home/ubuntu/ray/python/ray/worker.py", line 1694, in fetch_and_execute_function_to_run
function = pickle.loads(serialized_function)
File "/home/ubuntu/ray/python/ray/tune/registry.py", line 12, in <module>
from ray.tune.trainable import Trainable, wrap_function
File "/home/ubuntu/ray/python/ray/tune/__init__.py", line 6, in <module>
from ray.tune.tune import run_experiments
File "/home/ubuntu/ray/python/ray/tune/tune.py", line 8, in <module>
from ray.tune.hyperband import HyperBandScheduler
File "/home/ubuntu/ray/python/ray/tune/hyperband.py", line 8, in <module>
from ray.tune.trial_scheduler import FIFOScheduler, TrialScheduler
File "/home/ubuntu/ray/python/ray/tune/trial_scheduler.py", line 5, in <module>
from ray.tune.trial import Trial
File "/home/ubuntu/ray/python/ray/tune/trial.py", line 15, in <module>
from ray.tune.registry import _default_registry, get_registry, TRAINABLE_CLASS
ImportError: cannot import name '_default_registry'

# Fixed code
def _wait_for_and_process_task(self, task):
    """Wait for a task to be ready and process the task.

    Args:
        task: The task to execute.
    """
    function_id = task.function_id()

    # TODO(rkn): It would be preferable for actor creation tasks to share
    # more of the code path with regular task execution.
    if task.actor_creation_id() != ray.local_scheduler.ObjectID(NIL_ACTOR_ID):
        self._become_actor(task)
        return

    # Wait until the function to be executed has actually been registered
    # on this worker. We will push warnings to the user if we spend too
    # long in this loop.
    with log_span("ray:wait_for_function", worker=self):
        self._wait_for_function(function_id, task.driver_id().id())

    # Execute the task.
    # TODO(rkn): Consider acquiring this lock with a timeout and pushing a
    # warning to the user if we are waiting too long to acquire the lock
    # because that may indicate that the system is hanging, and it'd be
    # good to know where the system is hanging.
    log(event_type="ray:acquire_lock", kind=LOG_SPAN_START, worker=self)
    with self.lock:
        log(event_type="ray:acquire_lock", kind=LOG_SPAN_END, worker=self)

        function_name, _ = self.functions[task.driver_id().id()][function_id.id()]
        contents = {
            "function_name": function_name,
            "task_id": task.task_id().hex(),
            "worker_id": binary_to_hex(self.worker_id),
        }
        with log_span("ray:task", contents=contents, worker=self):
            self._process_task(task)

    # Push all of the log events to the global state store.
    flush_log()

    # Increase the task execution counter.
    (self.num_task_executions[task.driver_id().id()][function_id.id()]) += 1

    reached_max_executions = (
        self.num_task_executions[task.driver_id().id()][function_id.id()]
        == self.function_properties[task.driver_id().id()][function_id.id()].max_calls
    )
    if reached_max_executions:
        ray.worker.global_worker.local_scheduler_client.disconnect()
        os._exit(0)