code: def _test_ddp_zero_overlap(
      self,
      device,
      hook_constructor,
      gradient_as_bucket_view,
      static_graph,
      **kwargs,
  ):
      SGD_LR = 0.01
      SGD_MOMENTUM = 0.9
      SGD_WEIGHT_DECAY = 0.001
      NUM_INPUTS = 5
      torch.manual_seed(0)
      torch.cuda.manual_seed(0)
  
      rank = self.rank
      is_gpu = device.type == "cuda"
      if is_gpu:
          torch.cuda.set_device(device)
      models_to_test = [
          (
              torch.nn.Sequential(
                  torch.nn.Linear(1000, 2000),
                  torch.nn.Linear(2000, 500),
              ),
              [
                  torch.randn(1, 1000).to(device)
                  for _ in range(NUM_INPUTS)
              ],
          )
      ]
      if HAS_TORCHVISION:
          models_to_test.append(
              (
                  torchvision.models.resnet50(),
                  [
                      torch.randn(1, 3, 3, 1000).to(device)
                      for _ in range(NUM_INPUTS)
                  ],
              )
          )
      for model, inputs in models_to_test:
          # Enable determinism in cudnn operators
          with torch.backends.cudnn.flags(
              enabled=True, deterministic=True, benchmark=False
          ):
              device_ids = [rank] if is_gpu else None
              # Set up the DDP model overlapping with ZeRO
              ddp_model_overlap = DDP(
                  copy.deepcopy(model).to(device),
                  device_ids=device_ids,
                  gradient_as_bucket_view=gradient_as_bucket_view,
              )
              if static_graph:
                  ddp_model_overlap._set_static_graph()
              zero_optim = ZeroRedundancyOptimizer(
                  ddp_model_overlap.parameters(),
                  optimizer_class=torch.optim.SGD,
                  overlap_with_ddp=True,
                  lr=SGD_LR,
                  momentum=SGD_MOMENTUM,
                  weight_decay=SGD_WEIGHT_DECAY,
              )
              ddp_model_overlap.register_comm_hook(
                  None,
                  hook_constructor(
                      allreduce_hook,
                      ddp_model_overlap,
                      zero_optim,
                      **kwargs,
                  ),
              )
  
              # Set up the DDP model with local optimizer
              ddp_model_local = DDP(
                  copy.deepcopy(model).to(device),
                  device_ids=device_ids,
                  gradient_as_bucket_view=gradient_as_bucket_view,
              )
              if static_graph:
                  ddp_model_local._set_static_graph()
              local_optim = torch.optim.SGD(
                  ddp_model_local.parameters(),
                  lr=SGD_LR,
                  momentum=SGD_MOMENTUM,
                  weight_decay=SGD_WEIGHT_DECAY,
              )
  
              # Check that the parameters match initially
              for p1, p2 in zip(
                  ddp_model_overlap.parameters(),
                  ddp_model_local.parameters(),
              ):
                  self.assertEqual(p1, p2)
  
              # Save the parameters to ensure they were updated
              init_params_overlap = copy.deepcopy(
                  list(ddp_model_overlap.parameters())
              )
  
              # Ensure that this test runs independently
              dist.barrier()
  
              # Run the DDP model overlapping with ZeRO
              # NOTE: Overlapping currently requires 2 or 3 warmup iterations
              # to ensure DDP buckets have been rebuilt (depending on the
              # value of `static_graph`)
              num_warmup_inputs = 2 if not static_graph else 3
              for input in inputs[:num_warmup_inputs]:
                  output = ddp_model_overlap(input)
                  loss = output.sum()
                  loss.backward()
              for input in inputs:
                  zero_optim.zero_grad()
                  output = ddp_model_overlap(input)
                  loss = output.sum()
                  loss.backward()
  
              # Run the DDP model with local optimizer
              for input in inputs:
                  local_optim.zero_grad()
                  output = ddp_model_local(input)
                  loss = output.sum()
                  loss.backward()
                  local_optim.step()
              dist.barrier()
  
              # Check that the parameters are equal
              for p1, p2 in zip(
                  ddp_model_overlap.parameters(),
                  ddp_model_local.parameters(),
              ):
                  self.assertEqual(p1, p2)
  
              # Check that the parameters were updated
              self.assertNotEqual(
                  init_params_overlap,
                  list(ddp_model_overlap.parameters()),
              )
  
              # Ensure that this test runs independently
              dist.barrier()
  
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 19 for calling the TestZeroRedundancyOptimizerDistributed obj's underlying member method code with example usage: obj._test_ddp_zero_overlap(device, hook_constructor, gradient_as_bucket_view, static_graph) without return types