How to use the ray.remote function in ray

    @ray.remote
    def multiple_dependency(i, arg1, arg2, arg3):
        arg1 = np.copy(arg1)
        arg1[0] = i
        return arg1

def run_func(func, *args, **kwargs):
    """Helper function for running examples"""
    ray.init()

    func = ray.remote(func)

    # NOTE: kwargs not allowed for now
    result = ray.get(func.remote(*args))

    # Inspect the stack to get calling example
    caller = inspect.stack()[1][3]
    print("%s: %s" % (caller, str(result)))

    return result

import pickle
from collections import defaultdict

import projekt


from forge import trinity
from forge.trinity.timed import runtime

from forge.ethyr.io import Stimulus, Action, utils
from forge.ethyr.torch import optim
from forge.ethyr.experience import RolloutManager

import torch

@ray.remote(num_gpus=1)
class God(trinity.God):
   '''Server level God API demo

   This server level optimizer node aggregates experience
   across all core level rollout worker nodes. It
   uses the aggregated experience compute gradients.

   This is effectively a lightweight variant of the
   Rapid computation model, with the potential notable
   difference that we also recompute the forward pass
   from small observation buffers rather than 
   communicating large activation tensors.

   This demo builds up the ExperienceBuffer utility, 
   which handles rollout batching.'''

import argparse
import time

import ray
import model

parser = argparse.ArgumentParser(description="Run the asynchronous parameter "
                                             "server example.")
parser.add_argument("--num-workers", default=4, type=int,
                    help="The number of workers to use.")
parser.add_argument("--redis-address", default=None, type=str,
                    help="The Redis address of the cluster.")


@ray.remote
class ParameterServer(object):
    def __init__(self, keys, values):
        # These values will be mutated, so we must create a copy that is not
        # backed by the object store.
        values = [value.copy() for value in values]
        self.weights = dict(zip(keys, values))

    def push(self, keys, values):
        for key, value in zip(keys, values):
            self.weights[key] += value

    def pull(self, keys):
        return [self.weights[key] for key in keys]


@ray.remote

    @DeveloperAPI
    @classmethod
    def as_remote(cls, num_cpus=None, num_gpus=None, resources=None):
        return ray.remote(
            num_cpus=num_cpus, num_gpus=num_gpus, resources=resources)(cls)

from ray.experimental.serve.policy import RoutePolicy
from ray.experimental.serve.server import HTTPActor


def start_initial_state(kv_store_connector):
    nursery_handle = ActorNursery.remote()
    ray.experimental.register_actor(SERVE_NURSERY_NAME, nursery_handle)

    ray.get(
        nursery_handle.store_bootstrap_state.remote(
            BOOTSTRAP_KV_STORE_CONN_KEY, kv_store_connector))
    return nursery_handle


@ray.remote
class ActorNursery:
    """Initialize and store all actor handles.

    Note:
        This actor is necessary because ray will destory actors when the
        original actor handle goes out of scope (when driver exit). Therefore
        we need to initialize and store actor handles in a seperate actor.
    """

    def __init__(self):
        # Dict: Actor handles -> tag
        self.actor_handles = dict()

        self.bootstrap_state = dict()

    def start_actor(self, actor_cls, tag, init_args=(), init_kwargs={}):

if self.is_setup:
            raise ValueError("setup can be only invoke once")
        self.is_setup = True
        import ray

        if not self.is_in_mlsql:
            if func_for_rows is not None:
                func = ray.remote(func_for_rows)
                return ray.get(func.remote(self.mock_data))
            else:
                func = ray.remote(func_for_row)

                def iter_all(rows):
                    return [ray.get(func.remote(row)) for row in rows]

                iter_all_func = ray.remote(iter_all)
                return ray.get(iter_all_func.remote(self.mock_data))

        buffer = []
        for server_info in self.build_servers_in_ray():
            server = ray.experimental.get_actor(server_info.server_id)
            buffer.append(ray.get(server.connect_info.remote()))
            server.serve.remote(func_for_row, func_for_rows)
        items = [vars(server) for server in buffer]
        self.python_context.build_result(items, 1024)
        return buffer

@ray.remote
def slogdet(a):
    raise NotImplementedError

def _create_single_worker(self, config):
        logger.warning("AVAIL CLUSTER RES {}".format(ray.cluster_resources()))
        RemotePyTorchRunner = ray.remote(num_gpus=1)(PyTorchRunner)
        worker = RemotePyTorchRunner.remote(
            config["batch_per_device"],
            momentum=config["momentum"],
            weight_decay=config["weight_decay"],
            model_creator=config["model_creator"],
            data_creator=config["data_creator"],
            loss_creator=config["loss_creator"],
            verbose=config["verbose"],
            use_nccl=config["use_nccl"],
            worker_config=config["worker_config"],
            lr_config=config["lr_config"],
        )
        worker.set_device.remote()
        return worker

write_with_length(ex.encode("utf-8"), out)
                out.flush()
                read_int(infile)
            except IOError:
                # JVM close the socket
                pass
            except Exception:
                # Write the error to stderr if it happened while serializing
                print("Py worker failed with exception:")
                print(traceback.format_exc())
                pass

        conn.close()


@ray.remote
class RayDataServer(object):

    def __init__(self, server_id, java_server, port=0, timezone="Asia/Harbin"):

        self.server = OnceServer(
            self.get_address(), port, java_server.timezone)
        try:
            (rel_host, rel_port) = self.server.bind()
        except Exception:
            print(traceback.format_exc())

        self.host = rel_host
        self.port = rel_port
        self.timezone = timezone
        self.server_id = server_id
        self.java_server = java_server