How to use the syft.VirtualWorker function in syft

def test_DASH(hook, workers):
    """
    Test DASH (Distributed Association Scan Hammer), i.e. distributed linear regression for genetics with SMPC
    """

    bob = workers["bob"]
    alice = workers["alice"]
    james = workers["james"]
    crypto_prov = sy.VirtualWorker(hook, id="crypto_prov")
    hbc_worker = sy.VirtualWorker(hook, id="hbc_worker")

    ###### Simulate data ######
    torch.manual_seed(0)  # Truncation might not always work so we set the random seed

    K = 2  # Number of permanent covariates
    M = 5  # Number of transient covariates

    # Alice
    N1 = 100
    y1 = torch.randn(N1).send(alice)
    X1 = torch.randn(N1, M).send(alice)
    C1 = torch.randn(N1, K).send(alice)

    # Bob
    N2 = 200

def test_section_3_securing_fl(hook):
    """This tests the Udacity course content found at
    https://github.com/Udacity/private-ai
    """

    import syft as sy
    import torch as th

    # hook = sy.TorchHook(th)
    from torch import nn, optim

    # create a couple workers

    bob = sy.VirtualWorker(hook, id="bob_udacity_3")
    alice = sy.VirtualWorker(hook, id="alice_udacity_3")
    secure_worker = sy.VirtualWorker(hook, id="secure_worker_udacity_3")

    bob.add_workers([alice, secure_worker])
    alice.add_workers([bob, secure_worker])
    secure_worker.add_workers([alice, bob])

    # A Toy Dataset
    data = th.tensor([[0, 0], [0, 1], [1, 0], [1, 1.0]], requires_grad=True)
    target = th.tensor([[0], [0], [1], [1.0]], requires_grad=True)

    # get pointers to training data on each worker by
    # sending some training data to bob and alice
    bobs_data = data[0:2].send(bob)
    bobs_target = target[0:2].send(bob)

    alices_data = data[2:].send(alice)

def test_fl_with_trainconfig(isolated_filesystem, start_remote_server_worker_only, hook):
    os.chdir("advanced/Federated Learning with TrainConfig/")
    notebook = "Introduction to TrainConfig.ipynb"
    p_name = Path("examples/tutorials/advanced/Federated Learning with TrainConfig/")
    not_excluded_notebooks.remove(p_name / notebook)
    hook.local_worker.remove_worker_from_registry("alice")
    kwargs = {"id": "alice", "host": "localhost", "port": 8777, "hook": hook}
    data = torch.tensor([[0.0, 1.0], [1.0, 0.0], [1.0, 1.0], [0.0, 0.0]], requires_grad=True)
    target = torch.tensor([[1.0], [1.0], [0.0], [0.0]], requires_grad=False)
    dataset = sy.BaseDataset(data, target)
    process_remote_worker = start_remote_server_worker_only(dataset=(dataset, "xor"), **kwargs)
    res = pm.execute_notebook(notebook, "/dev/null", timeout=300)
    assert isinstance(res, nbformat.notebooknode.NotebookNode)
    process_remote_worker.terminate()
    sy.VirtualWorker(id="alice", hook=hook, is_client_worker=False)

def setUp(self):
        hook = sy.TorchHook(torch, verbose=True)

        self.me = hook.local_worker
        self.me.is_client_worker = True

        instance_id = str(sy.ID_PROVIDER.pop())
        bob = sy.VirtualWorker(id=f"bob{instance_id}", hook=hook, is_client_worker=False)
        alice = sy.VirtualWorker(id=f"alice{instance_id}", hook=hook, is_client_worker=False)
        james = sy.VirtualWorker(id=f"james{instance_id}", hook=hook, is_client_worker=False)

        bob.add_workers([alice, james])
        alice.add_workers([bob, james])
        james.add_workers([bob, alice])

        self.hook = hook

        self.bob = bob
        self.alice = alice
        self.james = james

        # A Toy Dataset
        data = torch.tensor([[0, 0], [0, 1], [1, 0], [1, 1.0]], requires_grad=True)
        target = torch.tensor([[0], [0], [1], [1.0]], requires_grad=True)

        # get pointers to training data on each worker by

def test_create_already_existing_worker(hook):
    # Shares tensor with bob
    bob = sy.VirtualWorker(hook, "bob")
    x = th.tensor([1, 2, 3]).send(bob)

    # Recreates bob and shares a new tensor
    bob = sy.VirtualWorker(hook, "bob")
    y = th.tensor([2, 2, 2]).send(bob)

    # Recreates bob and shares a new tensor
    bob = sy.VirtualWorker(hook, "bob")
    z = th.tensor([2, 2, 10]).send(bob)

    # Both workers should be the same, so the following operation should be valid
    _ = x + y * z

def force_detail(worker: AbstractWorker, worker_tuple: tuple) -> tuple:
        worker_id, _objects, auto_add = worker_tuple
        worker_id = sy.serde._detail(worker, worker_id)

        result = sy.VirtualWorker(sy.hook, worker_id, auto_add=auto_add)
        _objects = sy.serde._detail(worker, _objects)
        result._objects = _objects

        # make sure they weren't accidentally double registered
        for _, obj in _objects.items():
            if obj.id in worker._objects:
                del worker._objects[obj.id]

        return result

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:
        worker = sy.serde.deserialize(worker)
        print("\t \nFOUND OLD WORKER!! " + str(worker._objects.keys()))
    
    return worker

def __init__(self, user: UserAuthentication, active=True):
        """ Handle session with User Authentication.
            
            Args:
                user (UserAuthentication) : User instance.
                active (bool) : Session state.
        """
        self.id = uuid.uuid5(uuid.NAMESPACE_DNS, UserSession.NAMESPACE_DNS)
        self.user = user  # PyGrid UserAuthentication object
        self.tensor_requests = list()

        # If it is the first session of this user at this node.
        if user.username not in hook.local_worker._known_workers:
            node_name = user.username + "_" + str(local_worker.id)
            self.node = sy.VirtualWorker(hook, id=node_name)
        else:
            self.node = hook.local_worker._known_workers[user.username]
        self.active = True

elif isinstance(obj, np.ndarray):
            return obj.ser(private=private_local, to_json=False)
        # Iterables non json-serializable
        elif isinstance(obj, (tuple, set, bytearray, range)):
            key = get_serialized_key(obj)
            return {key: [self.python_encode(i, private_local) for i in obj]}
        # Slice
        elif isinstance(obj, slice):
            key = get_serialized_key(obj)
            return {key: {"args": [obj.start, obj.stop, obj.step]}}
        # Generator
        elif isinstance(obj, types.GeneratorType):
            logging.warning("Generator args can't be transmitted")
            return []
        # worker
        elif isinstance(obj, (sy.SocketWorker, sy.VirtualWorker)):
            return {"__worker__": obj.id}
        # Else log the error
        else:
            raise ValueError("Unhandled type", type(obj))

# Ellipsis
        elif isinstance(obj, type(...)):
            return "..."
        # np.array
        elif isinstance(obj, np.ndarray):
            return obj.ser(private=private_local, to_json=False)
        # Slice
        elif isinstance(obj, slice):
            key = get_serialized_key(obj)
            return {key: {"args": [obj.start, obj.stop, obj.step]}}
        # Generator
        elif isinstance(obj, types.GeneratorType):
            logging.warning("Generator args can't be transmitted")
            return []
        # worker
        elif isinstance(obj, (sy.SocketWorker, sy.VirtualWorker)):
            return {"__worker__": obj.id}
        # Else log the error
        else:
            raise ValueError("Unhandled type", type(obj))