code: def test_grad_averager_wrong_shape(
      grad_averager_factory: GradientAveragerFactory,
  ):
      parameter_shape = (5, 5)
      model = nn.ParameterDict(
          {"w": nn.Parameter(torch.zeros(parameter_shape))}
      )
      dht = hivemind.DHT(start=True)
  
      with pytest.raises(ValueError):
          grad_averager_factory(
              model.parameters(),
              dht=dht,
              prefix="test_fail",
              target_group_size=2,
              reuse_grad_buffers=False,
              start=True,
              averaged_grads=[torch.zeros(parameter_shape + (1,))],
          )
  
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 2 for calling a global function with example usage: test_grad_averager_wrong_shape(grad_averager_factory) without return types