docstr: Check that the ``zero_grad`` method is properly handled.
docstr_tok: ["Check","that","the","``","zero_grad","``","method","is","properly","handled","."]
code: def test_zero_grad(self):
      """Check that the ``zero_grad`` method is properly handled."""
      self.dist_init(self.rank)
      LR = 0.01
      x = torch.rand(1)
      m = torch.nn.Linear(1, 1)
      o = ZeroRedundancyOptimizer(
          m.parameters(), optimizer_class=SGD, lr=LR
      )
      y = m(x)
      y.backward(x)
      self.assertNotEqual(m.weight.grad, torch.zeros_like(m.weight))
      self.assertNotEqual(m.weight.grad, torch.zeros_like(m.weight))
      o.zero_grad()
      self.assertIsNone(m.weight.grad)
      self.assertIsNone(m.bias.grad)
  
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 6 for calling the TestZeroRedundancyOptimizerSingleRank obj's underlying member method code with example usage: obj.test_zero_grad() without return types