code: def _init_training(self, generator, noise: float = 1.0):
      dl_gen = torch.Generator()
      dl_gen.manual_seed(42)
      dl = DataLoader(
          TensorDataset(self.data), batch_size=8, generator=dl_gen
      )
  
      model = nn.Linear(32, 16)
      torch.nn.init.ones_(model.weight)
      torch.nn.init.zeros_(model.bias)
      model = GradSampleModule(model)
  
      optim = torch.optim.SGD(model.parameters(), lr=0.1)
  
      dp_optim = DPOptimizer(
          optimizer=optim,
          noise_multiplier=noise,
          max_grad_norm=1.0,
          expected_batch_size=8,
          generator=generator,
      )
  
      return model, dp_optim, dl
  
imports: import unittest
  from typing import Optional
  import torch
  import torch.nn
  from opacus import PrivacyEngine
  from opacus.data_loader import DPDataLoader, switch_generator
  from opacus.grad_sample import GradSampleModule
  from opacus.optimizers import DPOptimizer
  from torch.utils.data import DataLoader, TensorDataset
label_desc: Use image node_id 2 for calling the OptimizerRandomnessTest obj's underlying member method code with example usage: obj._init_training(generator, noise) and returns: model,  dp_optim,  dl