class_docstr: Tests parameter flattening and shard metadata logic.
docstr: Returns a module with no parameters.
code: def _get_empty_module(self):
      """Returns a module with no parameters."""
      torch.manual_seed(0)  # keep everything deterministic
  
      class EmptyModule(torch.nn.Module):
          def forward(self, x):
              return x + 1
  
          def get_input(self, device, dtype):
              torch.manual_seed(1)  # keep everything deterministic
              return torch.rand(1).to(device=device, dtype=dtype)
  
      return EmptyModule()
  
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 10 for calling the TestFlattenParams obj's underlying member method code with example usage: obj._get_empty_module() and returns: EmptyModule, unknown, torch