code: def test_forward_overlap(world_size, flatten, mixed):
      fsdp_config = {
          "flatten_parameters": flatten == "flatten",
          "mixed_precision": mixed == "mixed",
      }
      with temp_files_ctx(2) as temp_files:
          mp.spawn(
              _distributed_worker,
              (world_size, fsdp_config, temp_files[0], temp_files[1]),
              nprocs=world_size,
          )
  
imports: from statistics import mean
  import time
  from unittest.mock import patch
  import pytest
  import torch
  from torch.cuda import Event
  import torch.multiprocessing
  import torch.nn
  from fairscale.fair_dev.testing.testing import dist_init, get_cycles_per_ms, skip_if_single_gpu, teardown, temp_files_ctx
  from fairscale.internal import torch_version
  from fairscale.nn import enable_wrap, wrap
  from fairscale.nn.data_parallel import FullyShardedDataParallel
label_desc: Use image node_id 3 for calling a global function with example usage: test_forward_overlap(world_size, flatten, mixed) without return types