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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 remote(*args, **kwargs):
 """Define a remote function or an actor class. This can be used with no arguments to define a remote function or actor as follows: .. code-block:: python @ray.remote def f():
 return 1 @ray.remote class Foo(object):
 def method(self):
 return 1 It can also be used with specific keyword arguments: * **num_return_vals:** This is only for *remote functions*. It specifies the number of object IDs returned by the remote function invocation. * **num_cpus:** The quantity of CPU cores to reserve for this task or for the lifetime of the actor. * **num_gpus:** The quantity of GPUs to reserve for this task or for the lifetime of the actor. * **resources:** The quantity of various custom resources to reserve for this task or for the lifetime of the actor. This is a dictionary mapping strings (resource names) to numbers. * **max_calls:** Only for *remote functions*. This specifies the maximum number of times that a given worker can execute the given remote function before it must exit (this can be used to address memory leaks in third-party libraries or to reclaim resources that cannot easily be released, e.g., GPU memory that was acquired by TensorFlow). By default this is infinite. * **max_reconstructions**: Only for *actors*. This specifies the maximum number of times that the actor should be reconstructed when it dies unexpectedly. The minimum valid value is 0 (default), which indicates that the actor doesn't need to be reconstructed. And the maximum valid value is ray.ray_constants.INFINITE_RECONSTRUCTIONS. This can be done as follows: .. code-block:: python @ray.remote(num_gpus=1, max_calls=1, num_return_vals=2) def f():
 return 1, 2 @ray.remote(num_cpus=2, resources={"CustomResource": 1}) class Foo(object):
 def method(self):
 return 1 """

    worker = get_global_worker()

    if len(args) == 1 and len(kwargs) == 0 and callable(args[0]):
        # This is the case where the decorator is just @ray.remote.
        return make_decorator(worker=worker)(args[0])

    # Parse the keyword arguments from the decorator.
    error_string = ("The @ray.remote decorator must be applied either "
                    "with no arguments and no parentheses, for example "
                    "'@ray.remote', or it must be applied using some of "
                    "the arguments 'num_return_vals', 'num_cpus', 'num_gpus', "
                    "'resources', 'max_calls', "
                    "or 'max_reconstructions', like "
                    "'@ray.remote(num_return_vals=2, "
                    "resources={\"CustomResource\": 1})'.")
    assert len(args) == 0 and len(kwargs) > 0, error_string
    for key in kwargs:
        assert key in [
            "num_return_vals", "num_cpus", "num_gpus", "resources",
            "max_calls", "max_reconstructions"
        ], error_string

    num_cpus = kwargs["num_cpus"] if "num_cpus" in kwargs else None
    num_gpus = kwargs["num_gpus"] if "num_gpus" in kwargs else None
    resources = kwargs.get("resources")
    if not isinstance(resources, dict) and resources is not None:
        raise Exception("The 'resources' keyword argument must be a "
                        "dictionary, but received type {}.".format(
                            type(resources)))
    if resources is not None:
        assert "CPU" not in resources, "Use the 'num_cpus' argument."
        assert "GPU" not in resources, "Use the 'num_gpus' argument."

    # Handle other arguments.
    num_return_vals = kwargs.get("num_return_vals")
    max_calls = kwargs.get("max_calls")
    max_reconstructions = kwargs.get("max_reconstructions")

    return make_decorator(
        num_return_vals=num_return_vals,
        num_cpus=num_cpus,
        num_gpus=num_gpus,
        resources=resources,
        max_calls=max_calls,
        max_reconstructions=max_reconstructions,
        worker=worker)