code: def test_optimizer(
      use_local_updates: bool,
      delay_state_averaging: bool,
      delay_optimizer_step: bool,
      delay_grad_averaging: bool,
      reuse_grad_buffers: bool,
  ):
      _test_optimizer(
          use_local_updates=use_local_updates,
          delay_state_averaging=delay_state_averaging,
          delay_grad_averaging=delay_grad_averaging,
          delay_optimizer_step=delay_optimizer_step,
          reuse_grad_buffers=reuse_grad_buffers,
      )
  
imports: import ctypes
  import multiprocessing
  import time
  from functools import partial
  import numpy
  import pytest
  import torch
  import torch.nn
  import torch.nn.functional
  import hivemind
  from hivemind.averaging.control import AveragingStage
  from hivemind.optim.grad_averager import GradientAverager, GradientAveragerFactory
  from hivemind.optim.optimizer import Optimizer
  from hivemind.optim.power_sgd_averager import PowerSGDGradientAverager
  from hivemind.optim.progress_tracker import ProgressTracker
  from hivemind.optim.state_averager import ZERO_GRAD_SET_TO_NONE_DEFAULT, TrainingStateAverager
  from hivemind.utils.crypto import RSAPrivateKey
label_desc: Use image node_id 6 for calling a global function with example usage: test_optimizer(use_local_updates, delay_state_averaging, delay_optimizer_step, delay_grad_averaging, reuse_grad_buffers) without return types