code: def _run_fsdp_worker(
      cls,
      gpu_id: int,
      sync_file: str,
      config,
      world_size: int,
      bench_config: BenchConfig,
  ):
      with_memory_tracking = bench_config.track_memory
      with_profiling = bench_config.enable_profiler
  
      init_distributed_on_file(
          world_size=world_size, gpu_id=gpu_id, sync_file=sync_file
      )
      if with_memory_tracking and gpu_id == 0:
          config.MODEL.FSDP_CONFIG._TRACK_COMMUNICATIONS = True
  
      model = build_model(config.MODEL, config.OPTIMIZER).cuda(gpu_id)
      model = fsdp_wrapper(model, **config.MODEL.FSDP_CONFIG)
  
      criterion = nn.CrossEntropyLoss()
      if gpu_id == 0:
          print(model)
  
      batch_size = bench_config.batch_size
      device = torch.device(gpu_id)
      batch = torch.zeros(size=(batch_size, 3, 224, 224), device=device)
      targets = torch.tensor(
          [0] * batch_size, dtype=torch.int64, device=device
      )
  
      autocast_context = (
          torch.cuda.amp.autocast()
          if config.MODEL.AMP_PARAMS.USE_AMP
          else contextlib.suppress()
      )
  
      def forward_backward():
          with autocast_context:
              outputs = model(batch)
              loss = criterion(outputs[0][0], targets)
              for output in outputs[1:]:
                  loss += criterion(output, targets)
          loss.backward()  # Not recommended to put in auto-cast
  
      # First forward-backward passes to warm up the system
      for _ in range(2):
          forward_backward()
  
      if with_memory_tracking and gpu_id == 0:
          memory_tracker = LayerwiseMemoryTracker()
          memory_tracker.monitor(model)
      else:
          memory_tracker = None
  
      if with_profiling and gpu_id == 0:
          runtime_profiler = create_runtime_profiler(
              enabled=True,
              use_cpu=False,
              use_cuda=True,
              wait=0,
              warmup=0,
              active=1,
              legacy_profiler=False,
          )
      else:
          runtime_profiler = None
  
      with with_timing(f"forward/backward (rank {gpu_id})"):
          if runtime_profiler:
              runtime_profiler.__enter__()
          forward_backward()
          if runtime_profiler:
              runtime_profiler.__exit__(None, None, None)
  
      if gpu_id == 0:
          cls._print_memory_used()
  
      # Dump content of the profiler
      if runtime_profiler:
          runtime_profiler.dump(bench_config.output_path, rank=0)
  
      # Print a summary of the tracker
      if memory_tracker:
          memory_tracker.stop()
          image = memory_tracker.show_plots(capture=True)
          image.save(f"{bench_config.output_path}/memory_tracking.jpg")
          memory_tracker.save_traces(
              f"{bench_config.output_path}/memory_tracking.json"
          )
  
file_path: vissl/tests/bench_vit_fsdp.py
imports: import argparse
  import contextlib
  from dataclasses import dataclass
  import torch
  import torch.multiprocessing
  import torch.nn
  from vissl.models import build_model
  from vissl.utils.fsdp_utils import fsdp_wrapper
  from vissl.utils.hydra_config import compose_hydra_configuration, convert_to_attrdict
  from vissl.utils.layer_memory_tracking import LayerwiseMemoryTracker
  from vissl.utils.profiler import create_runtime_profiler
  from vissl.utils.test_utils import in_temporary_directory, init_distributed_on_file, with_temp_files, with_timing
label_desc: Use image node_id 3 for calling the BenchVitFSDP obj's underlying member method code with example usage: obj._run_fsdp_worker(cls, gpu_id, sync_file, config, world_size, bench_config) without return types