code: def test_batch_splitter_zero_grad(self) -> None:
      privacy_engine = PrivacyEngine()
      model, optimizer, dl = privacy_engine.make_private(
          module=self.model,
          optimizer=self.optimizer,
          data_loader=self.dl,
          noise_multiplier=1.0,
          max_grad_norm=1.0,
          poisson_sampling=False,
          grad_sample_mode=self.GRAD_SAMPLE_MODE,
      )
  
      with BatchMemoryManager(
          data_loader=dl, max_physical_batch_size=2, optimizer=optimizer
      ) as new_data_loader:
          self.model_forward_backward(
              model,
              new_data_loader,
              optimizer,
              num_steps=3,
              do_zero_grad=True,
          )
  
          with self.assertRaises(ValueError):
              self.model_forward_backward(
                  model,
                  new_data_loader,
                  optimizer,
                  num_steps=3,
                  do_zero_grad=False,
              )
  
imports: import unittest
  from typing import Iterable, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from opacus import PrivacyEngine
  from opacus.grad_sample.utils import get_gsm_class
  from opacus.utils.batch_memory_manager import BatchMemoryManager
  from torch.utils.data import DataLoader
  from torchvision import transforms
  from torchvision.datasets import FakeData
label_desc: Use image node_id 9 for calling the GradientAccumulationTest obj's underlying member method code with example usage: obj.test_batch_splitter_zero_grad() without return types