class_docstr: Tests parameter flattening and shard metadata logic.
code: def world_size(self) -> int:
      # Clamp the world size to 1 since these unit tests either exercise only
      # the flattening logic or check sharding subroutines directly without
      # requiring multiple ranks
      return 1
  
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 1 for calling the TestFlattenParams obj's underlying member method code with example usage: obj.world_size() and returns: int