code: def test_privacy_engine_poisson_accumulation(self) -> None:
      privacy_engine = PrivacyEngine()
      model, optimizer, dl = privacy_engine.make_private(
          module=self.model,
          optimizer=self.optimizer,
          data_loader=self.dl,
          noise_multiplier=0.0,
          max_grad_norm=999,
          grad_sample_mode=self.GRAD_SAMPLE_MODE,
      )
  
      self.model_forward_backward(model, dl, num_steps=1)
  
      with self.assertRaises(ValueError):
          self.model_forward_backward(model, dl, num_steps=1)
  
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 6 for calling the GradientAccumulationTest obj's underlying member method code with example usage: obj.test_privacy_engine_poisson_accumulation() without return types