class_docstr: Tests parameter flattening and shard metadata logic.
code: def _get_pnorm_after_step(self, module):
      optim = torch.optim.SGD(module.parameters(), lr=0.01)
      loss = self._get_output(module).sum()
      loss.backward()
      optim.step()
      return torch.norm(
          torch.stack([p.detach().norm() for p in module.parameters()])
      )
  
imports: import sys
  import torch
  import torch.nn
  from torch import distributed
  from torch.distributed.fsdp import FullyShardedDataParallel
  from torch.distributed.fsdp._flat_param import FlatParamHandle, FlatParamShardMetadata, HandleShardingStrategy
  from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
  from torch.testing._internal.common_fsdp import FSDPTest
  from torch.testing._internal.common_utils import run_tests, TEST_WITH_DEV_DBG_ASAN
label_desc: Use image node_id 24 for calling the TestFlattenParams obj's underlying member method code with example usage: obj._get_pnorm_after_step(module) and returns: torch