code: def setUp_model_and_optimizer(self) -> None:
      self.model = SampleConvNet()
      self.optimizer = torch.optim.SGD(
          self.model.parameters(), lr=self.LR, momentum=0
      )
  
      self.optimizer.zero_grad()
  
      # accumulate .grad over the entire dataset
      for x, y in self.dl:
          logits = self.model(x)
          loss = self.criterion(logits, y)
          loss.backward()
  
      self.effective_batch_grad = torch.cat(
          [
              p.grad.reshape(-1)
              for p in self.model.parameters()
              if p.requires_grad
          ]
      ) * (self.BATCH_SIZE / self.DATA_SIZE)
  
      self.optimizer.zero_grad()
  
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 3 for calling the GradientAccumulationTest obj's underlying member method code with example usage: obj.setUp_model_and_optimizer() without return types