code: def _check_low_precision_hook(
      self, state, hook, sharding_strategy, dtype, has_wrapping
  ):
      # keep everything deterministic for input data
      torch.manual_seed(0)
      torch.cuda.manual_seed(0)
  
      fsdp_with_hook = self._init_model(
          Net(
              has_wrapping=has_wrapping,
              sharding_strategy=sharding_strategy,
          ),
          sharding_strategy=sharding_strategy,
      )
      fsdp_with_hook.register_comm_hook(state, hook)
  
      mp_only_grad = MixedPrecision(reduce_dtype=dtype)
      fsdp_with_mp = self._init_model(
          Net(
              has_wrapping=has_wrapping,
              sharding_strategy=sharding_strategy,
              mixed_precision=mp_only_grad,
          ),
          sharding_strategy=sharding_strategy,
          mixed_precision=mp_only_grad,
      )
  
      optim_hook = torch.optim.SGD(fsdp_with_hook.parameters(), lr=0.1)
      optim_mp = torch.optim.SGD(fsdp_with_mp.parameters(), lr=0.1)
  
      in_data = torch.rand(16, 8).cuda()
      fsdp_with_hook.train()
      fsdp_with_mp.train()
      loss_hook = fsdp_with_hook(in_data).sum()
      loss_mp = fsdp_with_mp(in_data).sum()
      loss_hook.backward()
      # Make sure grads were cast to the parameter's precision
      self.assertEqual(
          fsdp_with_hook.params[0].grad.dtype, state.parameter_type
      )
      loss_mp.backward()
      optim_hook.step()
      optim_mp.step()
  
      dist.barrier()
  
      for hook_param, mp_param in zip(
          fsdp_with_hook.parameters(), fsdp_with_mp.parameters()
      ):
          self.assertEqual(hook_param.grad, mp_param.grad)
  
imports: import sys
  from typing import Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import distributed
  from torch.distributed.algorithms._comm_hooks import default_hooks
  from torch.distributed.distributed_c10d import _get_default_group
  from torch.distributed.fsdp import FullyShardedDataParallel, MixedPrecision
  from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy
  from torch.distributed.fsdp.wrap import ModuleWrapPolicy
  from torch.testing._internal.common_distributed import requires_nccl, requires_nccl_version, skip_but_pass_in_sandcastle_if, skip_if_lt_x_gpu
  from torch.testing._internal.common_fsdp import FSDPTest
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, parametrize, run_tests
label_desc: Use image node_id 8 for calling the TestCommunicationHooks obj's underlying member method code with example usage: obj._check_low_precision_hook(state, hook, sharding_strategy, dtype, has_wrapping) without return types