docstr: Check the state consolidation mechanism and the state dict exposed
  by ZeroRedundancyOptimizer.
docstr_tok: ["Check","the","state","consolidation","mechanism","and","the","state","dict","exposed","by","ZeroRedundancyOptimizer","."]
code: def test_collect_shards(self):
      """Check the state consolidation mechanism and the state dict exposed
      by ZeroRedundancyOptimizer."""
      self.dist_init(self.rank)
      LR = 1e-3
      MOMENTUM = 0.99
      BATCH_SIZE, INPUT_DIM, HIDDEN_DIM, OUTPUT_DIM = 3, 20, 10, 5
      REFERENCE_RANK = 0
      target = torch.rand((BATCH_SIZE, OUTPUT_DIM), device=self.device)
      inputs = torch.rand((BATCH_SIZE, INPUT_DIM), device=self.device)
      model = torch.nn.Sequential(
          torch.nn.Linear(INPUT_DIM, HIDDEN_DIM),
          torch.nn.Linear(HIDDEN_DIM, OUTPUT_DIM),
      ).to(self.device)
      loss_fn = torch.nn.L1Loss()
      loss_fn.to(self.device)
      optimizer = ZeroRedundancyOptimizer(
          model.parameters(),
          optimizer_class=SGD,
          lr=LR,
          momentum=MOMENTUM,  # ensure there exists state to shard
      )
  
      def closure():
          optimizer.zero_grad()
          output = model(inputs)
          loss = loss_fn(output, target)
          loss.backward()
          return loss
  
      # Run a dummy step so that the optimizer state dict exists
      _ = optimizer.step(closure=closure)
  
      # Get the optimizer state on the reference rank
      optimizer.consolidate_state_dict(to=REFERENCE_RANK)
      if self.rank == REFERENCE_RANK:
          # Check that the state has the correct size
          optimizer_state_dict = optimizer.state_dict()
          self.assertEqual(
              len(optimizer_state_dict["state"]),
              len(list(model.parameters())),
          )
      else:
          optimizer_state_dict = {}
  
      # Load the optimizer state on all ranks without any exceptions
      optimizer_state_dict = _broadcast_object(
          optimizer_state_dict,
          src_rank=REFERENCE_RANK,
          group=dist.group.WORLD,
          device=self.device,
      )
      optimizer.load_state_dict(optimizer_state_dict)
  
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 11 for calling the TestZeroRedundancyOptimizerDistributed obj's underlying member method code with example usage: obj.test_collect_shards() and returns: loss