class_docstr: Model that supports two computation paths: `layer0` -> `layer1` and
  `layer0` -> `layer2`. Notably, both `layer1` and `layer2` have 36 elements
  when flattened, which means that their corresponding all-gathers and
  reduce-scatters may be silently matched if we do not perform any checks.
class_docstr_tok: ["Model","that","supports","two","computation","paths",":","`","layer0","`","-",">","`","layer1","`","and","`","layer0","`","-",">","`","layer2","`",".","Notably",",","both","`","layer1","`","and","`","layer2","`","have","36","elements","when","flattened",",","which","means","that","their","corresponding","all-gathers","and","reduce-scatters","may","be","silently","matched","if","we","do","not","perform","any","checks","."]
code: def run_backward(self, loss):
      loss.backward()
  
imports: import sys
  import warnings
  from contextlib import nullcontext
  import torch
  from torch import distributed
  from torch.distributed.fsdp import FullyShardedDataParallel
  from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy
  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 instantiate_parametrized_tests, parametrize, run_tests, TEST_WITH_DEV_DBG_ASAN
label_desc: Use image node_id 5 for calling the Model obj's underlying member method code with example usage: obj.run_backward(loss) without return types