text: import logging
  from functools import wraps
  
  from ray import cloudpickle as pickle
  from ray._raylet import PythonFunctionDescriptor
  from ray import cross_language, Language
  from ray.util.placement_group import PlacementGroup, \
      check_placement_group_index
  import ray.signature
  
  # Default parameters for remote functions.
  DEFAULT_REMOTE_FUNCTION_CPUS = 1
  DEFAULT_REMOTE_FUNCTION_NUM_RETURN_VALS = 1
  DEFAULT_REMOTE_FUNCTION_MAX_CALLS = 0
  # Normal tasks may be retried on failure this many times.
  # TODO(swang): Allow this to be set globally for an application.
  DEFAULT_REMOTE_FUNCTION_NUM_TASK_RETRIES = 3
  
  logger = logging.getLogger(__name__)
  
  
  class RemoteFunction:
      """A remote function.
  
      This is a decorated function. It can be used to spawn tasks.
  
      Attributes:
          _language: The target language.
          _function: The original function.
          _function_descriptor: The function descriptor. This is not defined
              until the remote function is first invoked because that is when the
              function is pickled, and the pickled function is used to compute
              the function descriptor.
          _function_name: The module and function name.
          _num_cpus: The default number of CPUs to use for invocations of this
              remote function.
          _num_gpus: The default number of GPUs to use for invocations of this
              remote function.
          _memory: The heap memory request for this task.
          _object_store_memory: The object store memory request for this task.
          _resources: The default custom resource requirements for invocations of
              this remote function.
          _num_returns: The default number of return values for invocations
              of this remote function.
          _max_calls: The number of times a worker can execute this function
              before exiting.
          _decorator: An optional decorator that should be applied to the remote
              function invocation (as opposed to the function execution) before
              invoking the function. The decorator must return a function that
              takes in two arguments ("args" and "kwargs"). In most cases, it
              should call the function that was passed into the decorator and
              return the resulting ObjectRefs. For an example, see
              "test_decorated_function" in "python/ray/tests/test_basic.py".
          _function_signature: The function signature.
          _last_export_session_and_job: A pair of the last exported session
              and job to help us to know whether this function was exported.
              This is an imperfect mechanism used to determine if we need to
              export the remote function again. It is imperfect in the sense that
              the actor class definition could be exported multiple times by
              different workers.
      """
  
      def __init__(self, language, function, function_descriptor, num_cpus,
                   num_gpus, memory, object_store_memory, resources,
                   accelerator_type, num_returns, max_calls, max_retries,
                   placement_group, placement_group_bundle_index):
          self._language = language
          self._function = function
          self._function_name = (
              self._function.__module__ + "." + self._function.__name__)
          self._function_descriptor = function_descriptor
          self._is_cross_language = language != Language.PYTHON
          self._num_cpus = (DEFAULT_REMOTE_FUNCTION_CPUS
                            if num_cpus is None else num_cpus)
          self._num_gpus = num_gpus
          self._memory = memory
          if object_store_memory is not None:
              raise NotImplementedError(
                  "setting object_store_memory is not implemented for tasks")
          self._object_store_memory = None
          self._resources = resources
          self._accelerator_type = accelerator_type
          self._num_returns = (DEFAULT_REMOTE_FUNCTION_NUM_RETURN_VALS
                               if num_returns is None else num_returns)
          self._max_calls = (DEFAULT_REMOTE_FUNCTION_MAX_CALLS
                             if max_calls is None else max_calls)
          self._max_retries = (DEFAULT_REMOTE_FUNCTION_NUM_TASK_RETRIES
                               if max_retries is None else max_retries)
          self._decorator = getattr(function, "__ray_invocation_decorator__",
                                    None)
          self._function_signature = ray.signature.extract_signature(
              self._function)
  
          self._last_export_session_and_job = None
  
          # Override task.remote's signature and docstring
          @wraps(function)
          def _remote_proxy(*args, **kwargs):
              return self._remote(args=args, kwargs=kwargs)
  
          self.remote = _remote_proxy
  
      def __call__(self, *args, **kwargs):
          raise TypeError("Remote functions cannot be called directly. Instead "
                          f"of running '{self._function_name}()', "
                          f"try '{self._function_name}.remote()'.")
  
      def _submit(self,
                  args=None,
                  kwargs=None,
                  num_returns=None,
                  num_cpus=None,
                  num_gpus=None,
                  resources=None):
          logger.warning(
              "WARNING: _submit() is being deprecated. Please use _remote().")
          return self._remote(
              args=args,
              kwargs=kwargs,
              num_returns=num_returns,
              num_cpus=num_cpus,
              num_gpus=num_gpus,
              resources=resources)
  
      def options(self, **options):
          """Convenience method for executing a task with options.
  
          Same arguments as func._remote(), but returns a wrapped function
          that a non-underscore .remote() can be called on.
  
          Examples:
              # The following two calls are equivalent.
              >>> func._remote(num_cpus=4, args=[x, y])
              >>> func.options(num_cpus=4).remote(x, y)
          """
  
          func_cls = self
  
          class FuncWrapper:
              def remote(self, *args, **kwargs):
                  return func_cls._remote(args=args, kwargs=kwargs, **options)
  
          return FuncWrapper()
  
      def _remote(self,
                  args=None,
                  kwargs=None,
                  num_returns=None,
                  num_cpus=None,
                  num_gpus=None,
                  memory=None,
                  object_store_memory=None,
                  accelerator_type=None,
                  resources=None,
                  max_retries=None,
                  placement_group=None,
                  placement_group_bundle_index=-1,
                  name=""):
          """Submit the remote function for execution."""
          worker = ray.worker.global_worker
          worker.check_connected()
  
          # If this function was not exported in this session and job, we need to
          # export this function again, because the current GCS doesn't have it.
          if not self._is_cross_language and \
                  self._last_export_session_and_job != \
                  worker.current_session_and_job:
              # There is an interesting question here. If the remote function is
              # used by a subsequent driver (in the same script), should the
              # second driver pickle the function again? If yes, then the remote
              # function definition can differ in the second driver (e.g., if
              # variables in its closure have changed). We probably want the
              # behavior of the remote function in the second driver to be
              # independent of whether or not the function was invoked by the
              # first driver. This is an argument for repickling the function,
              # which we do here.
              self._pickled_function = pickle.dumps(self._function)
  
              self._function_descriptor = PythonFunctionDescriptor.from_function(
                  self._function, self._pickled_function)
  
              self._last_export_session_and_job = worker.current_session_and_job
              worker.function_actor_manager.export(self)
  
          kwargs = {} if kwargs is None else kwargs
          args = [] if args is None else args
  
          if num_returns is None:
              num_returns = self._num_returns
          if max_retries is None:
              max_retries = self._max_retries
  
          if placement_group is None:
              placement_group = PlacementGroup.empty()
  
          check_placement_group_index(placement_group,
                                      placement_group_bundle_index)
  
          resources = ray.utils.resources_from_resource_arguments(
              self._num_cpus, self._num_gpus, self._memory,
              self._object_store_memory, self._resources, self._accelerator_type,
              num_cpus, num_gpus, memory, object_store_memory, resources,
              accelerator_type)
  
          def invocation(args, kwargs):
              if self._is_cross_language:
                  list_args = cross_language.format_args(worker, args, kwargs)
              elif not args and not kwargs and not self._function_signature:
                  list_args = []
              else:
                  list_args = ray.signature.flatten_args(
                      self._function_signature, args, kwargs)
  
              if worker.mode == ray.worker.LOCAL_MODE:
                  assert not self._is_cross_language, \
                      "Cross language remote function " \
                      "cannot be executed locally."
              object_refs = worker.core_worker.submit_task(
                  self._language, self._function_descriptor, list_args, name,
                  num_returns, resources, max_retries, placement_group.id,
                  placement_group_bundle_index)
  
              if len(object_refs) == 1:
                  return object_refs[0]
              elif len(object_refs) > 1:
                  return object_refs
  
          if self._decorator is not None:
              invocation = self._decorator(invocation)
  
          return invocation(args, kwargs)
  