code: def setUp(self) -> None:
      self.DATA_SIZE = 128
      self.BATCH_SIZE = 16
      self.SAMPLE_RATE = self.BATCH_SIZE / self.DATA_SIZE
      self.LR = 0  # we want to call optimizer.step() without modifying the model
      self.ALPHAS = [1 + x / 10.0 for x in range(1, 100, 10)]
      self.criterion = nn.CrossEntropyLoss()
      self.GRAD_SAMPLE_MODE = "hooks"
  
      self.setUp_data()
      self.setUp_model_and_optimizer()
  
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 1 for calling the GradientAccumulationTest obj's underlying member method code with example usage: obj.setUp() without return types