code: def demo_basic(
      rank,
      world_size,
      weight,
      dp,
      noise_multiplier=0,
      max_grad_norm=1e8,
  ):
      # We don't want the 2 GPUs to work on the same examples/labels in parallel
      torch.manual_seed(rank)
      batch_size = 32
      withdp = "with" + ("out " if not dp else "")
      print(
          f"Running basic DDP {withdp} differential privacy example on rank {rank}."
      )
  
      device = setup_and_get_device(rank, world_size)
  
      # create model and move it to GPU with id rank
      model = ToyModel().to(device)
      print(f"Initial weight: {model.net1.weight.data}")
  
      # Freeze all the parameters except one, to ensure that the noise is the same
      # (the DDP hook does not browse the layers in the same order as the naive implementation)
      model.net1.bias.requires_grad = False
      model.net2.bias.requires_grad = False
      model.net2.weight.requires_grad = False
  
      if dp:
          ddp_model = DPDDP(model)
          engine = PrivacyEngine(
              ddp_model,
              batch_size=batch_size,
              sample_size=10 * batch_size,
              alphas=PRIVACY_ALPHAS,
              noise_multiplier=noise_multiplier,
              max_grad_norm=[max_grad_norm],
          )
          engine.random_number_generator = engine._set_seed(0)
      else:
          ddp_model = DDP(model, device_ids=[device])
  
      loss_fn = nn.MSELoss()
      optimizer = optim.SGD(ddp_model.parameters(), lr=1)
      if dp:
          engine.attach(optimizer)
  
      optimizer.zero_grad()
      labels = torch.randn(batch_size, 5).to(device)
  
      outputs = ddp_model(torch.randn(batch_size, 10).to(device))
      loss_fn(outputs, labels).backward()
      optimizer.step()
  
      weight.copy_(model.net1.weight.data.cpu())
  
      cleanup()
  
imports: import os
  import sys
  import unittest
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.optim
  from opacus import PrivacyEngine
  from opacus.distributed import DifferentiallyPrivateDistributedDataParallel
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 3 for calling a global function with example usage: demo_basic(rank, world_size, weight, dp, noise_multiplier, max_grad_norm) without return types