code: def _test_zero_model_parallel(self, parameters_as_bucket_view: bool):
      # Use two processes each with two GPUs
      assert self.rank < 2
      NUM_EPOCHS = 2
      NUM_INPUTS = 4
      LR = 0.01
      torch.manual_seed(0)
      torch.cuda.manual_seed(0)
  
      class ModelParallelModel(torch.nn.Module):
          def __init__(self, dev0, dev1):
              super().__init__()
              self.dev0 = dev0
              self.dev1 = dev1
              self.net0 = torch.nn.Linear(10, 10).to(dev0)
              self.relu = torch.nn.ReLU()
              self.net1 = torch.nn.Linear(10, 5).to(dev1)
  
          def forward(self, x):
              x = x.to(self.dev0)
              x = self.relu(self.net0(x))
              x = x.to(self.dev1)
              return self.net1(x)
  
      class LocalModel(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.net0 = torch.nn.Linear(10, 10)
              self.relu = torch.nn.ReLU()
              self.net1 = torch.nn.Linear(10, 5)
  
          def forward(self, x):
              return self.net1(self.relu(self.net0(x)))
  
      dev0 = torch.device(2 * self.rank)
      dev1 = torch.device(2 * self.rank + 1)
      mp_model = ModelParallelModel(dev0, dev1)
      ddp_model = DDP(mp_model)
      local_model = LocalModel().to(dev0)
  
      # Ensure the parameters are the same across the two models
      def copy_param(p):
          return torch.nn.Parameter(p.detach().clone().to(dev0))
  
      local_model.net0.weight = copy_param(mp_model.net0.weight)
      local_model.net0.bias = copy_param(mp_model.net0.bias)
      local_model.net1.weight = copy_param(mp_model.net1.weight)
      local_model.net1.bias = copy_param(mp_model.net1.bias)
  
      # Compare parity between DDP with model parallelism using ZeRO and
      # a local model using a local optimizer
      zero_optim = ZeroRedundancyOptimizer(
          ddp_model.parameters(),
          optimizer_class=torch.optim.Adam,
          parameters_as_bucket_view=parameters_as_bucket_view,
          lr=LR,
      )
      local_optim = torch.optim.Adam(local_model.parameters(), lr=LR)
      inputs = [torch.randn(20, 10).to(dev0) for _ in range(NUM_INPUTS)]
  
      for _ in range(NUM_EPOCHS):
          for input in inputs:
  
              def closure_local():
                  local_optim.zero_grad()
                  local_loss = local_model(input).abs().sum()
                  local_loss.backward()
                  return local_loss
  
              def closure_ddp():
                  zero_optim.zero_grad()
                  ddp_loss = ddp_model(input).abs().sum()
                  ddp_loss.backward()
                  return ddp_loss
  
              local_loss = cast(
                  torch.Tensor, local_optim.step(closure=closure_local)
              )
              ddp_loss = cast(
                  torch.Tensor, zero_optim.step(closure=closure_ddp)
              )
  
              # Increased tolerances are needed to pass when using TF32
              # See: https://github.com/pytorch/pytorch/issues/67764
              torch.testing.assert_close(
                  local_loss.cpu(),
                  ddp_loss.cpu(),
                  rtol=1e-03,
                  atol=1e-08,
              ), "Losses differ between local optimizer and ZeRO"
  
              for local_p, ddp_p in zip(
                  local_model.parameters(), ddp_model.parameters()
              ):
                  torch.testing.assert_close(
                      local_p.cpu(),
                      ddp_p.cpu(),
                      rtol=1e-03,
                      atol=1e-04,
                  ), "Models differ after a step"
  
imports: import copy
  import os
  import sys
  import unittest
  from contextlib import nullcontext
  from typing import Any, cast, List
  import numpy
  import torch
  import torch.distributed
  from torch.distributed.algorithms.ddp_comm_hooks.ddp_zero_hook import hook_with_zero_step, hook_with_zero_step_interleaved
  from torch.distributed.algorithms.ddp_comm_hooks.default_hooks import allreduce_hook
  from torch.distributed.algorithms.join import Join, Joinable, JoinHook
  from torch.distributed.optim import ZeroRedundancyOptimizer
  from torch.distributed.optim.zero_redundancy_optimizer import _broadcast_object
  from torch.nn.parallel import DistributedDataParallel
  from torch.optim import AdamW, SGD
  from torch.testing._internal import common_distributed
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, IS_WINDOWS, parametrize, run_tests, TEST_WITH_ASAN, TEST_WITH_DEV_DBG_ASAN
label_desc: Use image node_id 17 for calling the TestZeroRedundancyOptimizerDistributed obj's underlying member method code with example usage: obj._test_zero_model_parallel(parameters_as_bucket_view) and returns: torch, self, self, local_loss, ddp_loss