code: def profile(opt):
      if opt["torch"] or opt["torch_cuda"]:
          with torch.autograd.profiler.profile(
              use_cuda=opt["torch_cuda"]
          ) as prof:
              TrainLoop(opt).train()
  
          key = "cpu_time_total" if opt["torch"] else "cuda_time_total"
          print(prof.key_averages().table(sort_by=key, row_limit=25))
  
          return prof
      else:
          pr = cProfile.Profile()
          pr.enable()
          TrainLoop(opt).train()
          pr.disable()
          s = io.StringIO()
          sortby = "cumulative"
          ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
          ps.print_stats()
          print(s.getvalue())
  
file_path: ParlAI/parlai/scripts/profile_train.py
imports: from parlai.core.params import ParlaiParser
  from parlai.core.script import ParlaiScript, register_script
  from parlai.scripts.train_model import setup_args
  from parlai.scripts.train_model import TrainLoop
  import parlai.utils.logging
  import cProfile
  import io
  import pstats
label_desc: Use image node_id 2 for calling a global function with example usage: profile(opt) and returns: prof