code: def demo_basic(
      rank, weight, world_size, dp, clipping, grad_sample_mode
  ):
      torch.manual_seed(world_size)
      batch_size = 32
      setup(rank, world_size)
  
      # create model and move it to GPU with id rank
      model = ToyModel().to(rank)
      model.net1.weight.data.zero_()
      optimizer = optim.SGD(model.parameters(), lr=1)
  
      labels = torch.randn(2 * batch_size, 5).to(rank)
      data = torch.randn(2 * batch_size, 10)
  
      dataset = TensorDataset(data, labels)
  
      loss_fn = nn.MSELoss()
      if dp and clipping == "flat":
          ddp_model = DPDDP(model)
      else:
          ddp_model = DDP(model, device_ids=[rank])
  
      privacy_engine = PrivacyEngine()
  
      sampler = DistributedSampler(
          dataset, num_replicas=world_size, rank=rank, shuffle=False
      )
      data_loader = DataLoader(
          dataset, batch_size=batch_size, sampler=sampler
      )
      if dp:
          max_grad_norm = 1e8
          if clipping == "per_layer":
              max_grad_norm = [
                  max_grad_norm for _ in model.parameters()
              ]
          (
              ddp_model,
              optimizer,
              data_loader,
          ) = privacy_engine.make_private(
              module=ddp_model,
              optimizer=optimizer,
              data_loader=data_loader,
              noise_multiplier=0,
              max_grad_norm=max_grad_norm,
              poisson_sampling=False,
              clipping=clipping,
              grad_sample_mode=grad_sample_mode,
          )
          if clipping == "per_layer":
              assert isinstance(
                  optimizer,
                  (
                      DistributedPerLayerOptimizer,
                      SimpleDistributedPerLayerOptimizer,
                  ),
              )
          else:
              assert isinstance(optimizer, DistributedDPOptimizer)
  
      for x, y in data_loader:
          outputs = ddp_model(x.to(rank))
          loss = loss_fn(outputs, y)
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
          break
  
      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 opacus.optimizers.ddp_perlayeroptimizer import DistributedPerLayerOptimizer, SimpleDistributedPerLayerOptimizer
  from opacus.optimizers.ddpoptimizer import DistributedDPOptimizer
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data import DataLoader, TensorDataset
  from torch.utils.data.distributed import DistributedSampler
label_desc: Use image node_id 3 for calling a global function with example usage: demo_basic(rank, weight, world_size, dp, clipping, grad_sample_mode) without return types