class_docstr: Tests parameter flattening and shard metadata logic.
code: def _get_transformer(self, seed=0):
      torch.manual_seed(seed)  # keep everything deterministic
      module = torch.nn.Transformer(
          d_model=32,
          num_encoder_layers=2,
          num_decoder_layers=2,
          dim_feedforward=128,
          dropout=0.1,
      )
      module.register_buffer("dummy_buffer", torch.tensor(1.0))
  
      def get_input(device, dtype):
          torch.manual_seed(1)  # keep everything deterministic
          src = torch.rand(20, 8, 32).to(
              device=device, dtype=dtype
          )  # T x B x C
          tgt = torch.rand(10, 8, 32).to(
              device=device, dtype=dtype
          )  # T x B x C
          return (src, tgt)
  
      module.get_input = get_input
      return module
  
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 3 for calling the TestFlattenParams obj's underlying member method code with example usage: obj._get_transformer(seed) and returns: module, src,  tgt