How to use the syft.TorchHook function in syft

import time

import torch

import syft as sy
from grid.workers import WebsocketIOServerWorker


def _payload(location):
    x = torch.tensor([10, 20, 30, 40, 50.0])
    x.send(location)


hook = sy.TorchHook(torch)
server_worker = WebsocketIOServerWorker(
    hook, "localhost", 5000, log_msgs=True, payload=_payload
)


def test_client_id():
    android = server_worker.socketio.test_client(server_worker.app)
    android.emit("client_id", "android")
    assert len(server_worker.clients) == 1
    android.disconnect()
    server_worker.terminate()


def test_payload_execution():
    android = server_worker.socketio.test_client(server_worker.app)
    android.emit("client_id", "android")

def test___init__():
    hook = sy.TorchHook(torch)

    tensor = torch.tensor([1, 2, 3, 4])

    worker_id = sy.ID_PROVIDER.pop()
    alice_id = f"alice{worker_id}"
    alice = VirtualWorker(hook, id=alice_id)
    worker_id = sy.ID_PROVIDER.pop()
    bob = VirtualWorker(hook, id=f"bob{worker_id}")
    worker_id = sy.ID_PROVIDER.pop()
    charlie = VirtualWorker(hook, id=f"charlie{worker_id}")
    worker_id = sy.ID_PROVIDER.pop()
    dawson = VirtualWorker(hook, id=f"dawson{worker_id}", data=[tensor])

    # Ensure adding data on signup functionality works as expected
    assert tensor.owner == dawson

parser.add_argument(
        "--testing",
        action="store_true",
        help="if set, websocket server worker will load the test dataset instead of the training dataset",
    )
    parser.add_argument(
        "--verbose",
        "-v",
        action="store_true",
        help="if set, websocket server worker will be started in verbose mode",
    )

    args = parser.parse_args()

    # Hook and start server
    hook = sy.TorchHook(torch)
    server = start_websocket_server_worker(
        id=args.id,
        host=args.host,
        port=args.port,
        hook=hook,
        verbose=args.verbose,
        keep_labels=KEEP_LABELS_DICT[args.id] if args.id in KEEP_LABELS_DICT else list(range(10)),
        training=not args.testing,
    )

def init(self, input_array, id, owner):

        # add the new attribute to the created instance
        if id is None:
            id = random.randint(0, 1e10)

        self.id = id

        if owner is None:
            # cache the local_worker object locally which we will
            # use for all outgoing communications
            if not hasattr(sy, "local_worker"):
                sy.TorchHook()
            owner = sy.local_worker

        self.owner = owner

        self.owner.register(self)

        self.initialized = True
        # Finally, we must return the newly created object:
        return self

import torch as th
import syft as sy
import grid as gr
import requests
import time

addr = "http://localhost:5000"

hook = sy.TorchHook(th)
worker = gr.GridClient(addr=addr)


def main():
    x = th.tensor([1, 2, 3, 4]).send(worker)
    y = x + x
    print("Y = ", y.get())


if __name__ == "__main__":
    main()
# del x

import binascii
import json
import os

from flask import Flask, session, request
import redis
import syft as sy
import torch as th


hook = sy.TorchHook(th)

app = Flask(__name__)
app.secret_key = b'keepmesecret'

try:
    db = redis.from_url(os.environ['REDISCLOUD_URL'])
except:
    db = redis.from_url('redis://localhost:6379')

def _maybe_create_worker(worker_name: str = 'worker', virtual_worker_id: str = 'grid'):
    worker = db.get(worker_name)

    if worker is None:
        worker = sy.VirtualWorker(hook, virtual_worker_id, auto_add=False)
        print("\t \nCREATING NEW WORKER!!")
    else:

parser.add_argument(
        "--id", type=str, help="name (id) of the websocket server worker, e.g. --id alice"
    )
    parser.add_argument(
        "--verbose",
        "-v",
        action="store_true",
        help="if set, websocket server worker will be started in verbose mode",
    )

    args = parser.parse_args()
    return args


if __name__ == "__main__":
    hook = sy.TorchHook(torch)
    args = get_args()
    kwargs = {
        "id": args.id,
        "host": args.host,
        "port": args.port,
        "hook": hook,
        "verbose": args.verbose,
    }

    server = WebsocketServerWorker(**kwargs)
    server.start()

import torch as th
import torch.nn.functional as F
from torch import nn

import syft as sy
from syft import workers
from syft.frameworks.torch import pointers

use_cuda = th.cuda.is_available()
th.manual_seed(1)
device = th.device("cuda" if use_cuda else "cpu")

hook = sy.TorchHook(th)
me = hook.local_worker

kwargs_websocket = {"host": "localhost", "hook": hook, "verbose": False}
alice = workers.WebsocketClientWorker(id="alice", port=8777, **kwargs_websocket)

# Loss function
@th.jit.script
def loss_fn(real, pred):
    return ((real - pred) ** 2).mean()


# Model
class Net(th.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(2, 3)

def main():
    args = define_and_get_arguments()

    hook = sy.TorchHook(torch)

    if args.use_virtual:
        alice = VirtualWorker(id="alice", hook=hook, verbose=args.verbose)
        bob = VirtualWorker(id="bob", hook=hook, verbose=args.verbose)
        charlie = VirtualWorker(id="charlie", hook=hook, verbose=args.verbose)
    else:
        kwargs_websocket = {"host": "localhost", "hook": hook, "verbose": args.verbose}
        alice = WebsocketClientWorker(id="alice", port=8777, **kwargs_websocket)
        bob = WebsocketClientWorker(id="bob", port=8778, **kwargs_websocket)
        charlie = WebsocketClientWorker(id="charlie", port=8779, **kwargs_websocket)

    workers = [alice, bob, charlie]

    use_cuda = args.cuda and torch.cuda.is_available()

    torch.manual_seed(args.seed)

from multiprocessing import Process

import syft as sy
from syft.workers import WebsocketServerWorker
import torch
import argparse


hook = sy.TorchHook(torch)


def start_proc(participant, kwargs):  # pragma: no cover
    """ helper function for spinning up a websocket participant """

    def target():
        server = participant(**kwargs)
        server.start()

    p = Process(target=target)
    p.start()
    return p


parser = argparse.ArgumentParser(description="Run websocket server worker.")
parser.add_argument(