code: def torch_profile(
      export_chrome_trace=True, csv_data=None, write_csv=None
  ):
      use_cuda = torch.cuda.is_available()
  
      activities = [ProfilerActivity.CPU]
      if use_cuda:
          activities.append(ProfilerActivity.CUDA)
  
      if export_chrome_trace:
          p_trace_handler = trace_handler
      else:
          p_trace_handler = print_time_total
  
      p = profile(activities=activities, on_trace_ready=p_trace_handler)
  
      with p:
          yield
          p.step()
  
      if csv_data is not None and write_csv == "prof":
          if use_cuda:
              profile_sort = "self_cuda_time_total"
          else:
              profile_sort = "self_cpu_time_total"
          events = EventList(
              sorted(
                  p.key_averages(),
                  key=lambda evt: getattr(evt, profile_sort),
                  reverse=True,
              ),
              use_cuda=use_cuda,
          )
  
          save_profile_data(csv_data, events, use_cuda)
  
imports: import os
  import pathlib
  import time
  from contextlib import ContextDecorator, contextmanager
  from dataclasses import dataclass
  from typing import Any, List, Tuple, Union
  import torch
  from torch.autograd.profiler import EventList
  from torch.profiler import ProfilerActivity, profile
  from torch_geometric.profile.utils import byte_to_megabyte, get_gpu_memory_from_ipex, get_gpu_memory_from_nvidia_smi
label_desc: Use image node_id 7 for calling a global function with example usage: torch_profile(export_chrome_trace, csv_data, write_csv) without return types