code: def _test_pure_fp16_training(self, cpu_offload: CPUOffload):
      self._test_fsdp_parity(
          NestedWrappedModule,
          FSDPInitMode.RECURSIVE,
          cuda_init_mode=CUDAInitMode.CUDA_BEFORE,
          # Run one iteration to avoid NaN without a gradient scaler
          num_iters=1,
          cpu_offload=cpu_offload,
          use_pure_fp16=True,
      )
  
file_path: pytorch/test/distributed/fsdp/test_fsdp_pure_fp16.py
imports: import sys
  import torch
  import torch.distributed.fsdp._traversal_utils
  from torch import distributed
  from torch.distributed.fsdp import CPUOffload, FullyShardedDataParallel, MixedPrecision
  from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
  from torch.testing._internal.common_fsdp import CUDAInitMode, FSDPInitMode, FSDPTest, NestedWrappedModule
  from torch.testing._internal.common_utils import instantiate_parametrized_tests, run_tests, TEST_WITH_DEV_DBG_ASAN
label_desc: Use image node_id 3 for calling the TestPureFP16 obj's underlying member method code with example usage: obj._test_pure_fp16_training(cpu_offload) without return types