def deploy_service_registry_and_set_urls(
    private_keys, web3, contract_manager, service_registry_address
) -> Tuple[ServiceRegistry, List[str]]:
    urls = ["http://foo", "http://boo", "http://coo"]
    c1_client = JSONRPCClient(web3, private_keys[0])
    c1_service_proxy = ServiceRegistry(
        jsonrpc_client=c1_client,
        service_registry_address=service_registry_address,
        contract_manager=contract_manager,
    )
    c2_client = JSONRPCClient(web3, private_keys[1])
    c2_service_proxy = ServiceRegistry(
        jsonrpc_client=c2_client,
        service_registry_address=service_registry_address,
        contract_manager=contract_manager,
    )
    c3_client = JSONRPCClient(web3, private_keys[2])
    c3_service_proxy = ServiceRegistry(
        jsonrpc_client=c3_client,
        service_registry_address=service_registry_address,
        contract_manager=contract_manager,
    )

    # Test that getting a random service for an empty registry returns None
    pfs_address = get_random_service(c1_service_proxy, "latest")
    assert pfs_address is None

    # Test that setting the urls works
    c1_service_proxy.set_url(urls[0])
    c2_service_proxy.set_url(urls[1])
    c3_service_proxy.set_url(urls[2])

    return c1_service_proxy, urls
def add_interim_values(module, input, output):
    """The forward hook used to save interim tensors, detached
    from the graph. Used to calculate the multipliers
    """
    try:
        del module.x
    except AttributeError:
        pass
    try:
        del module.y
    except AttributeError:
        pass
    module_type = module.__class__.__name__
    if module_type in op_handler:
        func_name = op_handler[module_type].__name__
        # First, check for cases where we don't need to save the x and y tensors
        if func_name == 'passthrough':
            pass
        else:
            # check only the 0th input varies
            for i in range(len(input)):
                if i != 0 and type(output) is tuple:
                    assert input[i] == output[i], "Only the 0th input may vary!"
            # if a new method is added, it must be added here too. This ensures tensors
            # are only saved if necessary
            if func_name in ['maxpool', 'nonlinear_1d']:
                # only save tensors if necessary
                if type(input) is tuple:
                    setattr(module, 'x', torch.nn.Parameter(input[0].detach()))
                else:
                    setattr(module, 'x', torch.nn.Parameter(input.detach()))
                if type(output) is tuple:
                    setattr(module, 'y', torch.nn.Parameter(output[0].detach()))
                else:
                    setattr(module, 'y', torch.nn.Parameter(output.detach()))
            if module_type in failure_case_modules:
                input[0].register_hook(deeplift_tensor_grad)