code: def test_privacy_engine_no_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,
          poisson_sampling=False,
          grad_sample_mode=self.GRAD_SAMPLE_MODE,
      )
  
      self.model_forward_backward(model, dl, num_steps=8)
      self.assertEqual(optimizer.accumulated_iterations, 8)
  
      for grad_sample in optimizer.grad_samples:
          self.assertEqual(grad_sample.shape[0], 8 * self.BATCH_SIZE)
  
      optimizer.step()
  
      # .grad should contain the average gradient over the entire dataset
      accumulated_grad = torch.cat(
          [
              p.grad.reshape(-1)
              for p in self.model.parameters()
              if p.requires_grad
          ]
      )
  
      # the accumulated gradients in .grad without any hooks
      orig_grad = self.effective_batch_grad
  
      self.assertTrue(
          torch.allclose(
              accumulated_grad, orig_grad, atol=10e-5, rtol=10e-3
          ),
          f"Values are {accumulated_grad} vs {orig_grad}."
          f"MAD is {(orig_grad - accumulated_grad).abs().mean()}",
      )
  
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 7 for calling the GradientAccumulationTest obj's underlying member method code with example usage: obj.test_privacy_engine_no_poisson_accumulation() without return types