docstr: Tests pure FP16 training, including when the parameter's dtype is
  changed after FSDP initialization and before training.
docstr_tok: ["Tests","pure","FP16","training",",","including","when","the","parameter","'s","dtype","is","changed","after","FSDP","initialization","and","before","training","."]
code: def test_pure_fp16_training(self):
      """Tests pure FP16 training, including when the parameter's dtype is
      changed after FSDP initialization and before training."""
      self.run_subtests(
          {
              "cpu_offload": [
                  CPUOffload(offload_params=True),
                  CPUOffload(offload_params=False),
              ]
          },
          self._test_pure_fp16_training,
      )
  
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 2 for calling the TestPureFP16 obj's underlying member method code with example usage: obj.test_pure_fp16_training() without return types