class_docstr: LARC requires a distributed norm computation when the parameters
  and gradients are sharded.
  
  This test ensures that the implementation of LARC_FSDP does
  this computation correctly.
docstr: Sanity check: the sharded and non-sharded norms should be trivially
  the same when the number of GPU involved in 1 (no sharding)
docstr_tok: ["Sanity","check",":","the","sharded","and","non-sharded","norms","should","be","trivially","the","same","when","the","number","of","GPU","involved","in","1","(","no","sharding",")"]
code: def test_local_norm_computations(self) -> None:
      """
      Sanity check: the sharded and non-sharded norms should be trivially
      the same when the number of GPU involved in 1 (no sharding)
      """
      with with_temp_files(count=1) as sync_file:
          world_size = 1
          mp.spawn(
              self._norm_computation_worker,
              (sync_file, world_size),
              nprocs=world_size,
          )
  
file_path: vissl/tests/test_larc_fsdp.py
imports: import unittest
  import torch
  import torch.multiprocessing
  import torch.nn
  import torch.optim
  from fairscale.nn import FullyShardedDataParallel
  from torch.nn.parallel import DistributedDataParallel
  from vissl.optimizers.larc_fsdp import LARC_FSDP
  from vissl.utils.test_utils import gpu_test, init_distributed_on_file, with_temp_files
label_desc: Use image node_id 2 for calling the TestLarcFSDP obj's underlying member method code with example usage: obj.test_local_norm_computations() without return types