code: def callback_with_model(
      callback: Callable[[Any, AsyncPipe], None], ctx: Any
  ) -> None:
      try:
          group = (
              get_pipeline_parallel_group()
          )  # FIXME(tom) handle dynamic group
          set_device_based_on_group(group)
  
          with PipeModel.lock:
              callback(ctx, PipeModel)
      except Exception as e:
          print(f"callback_with_model got {e}")
  
imports: from threading import Event, Lock, Thread
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast
  import torch
  from torch import nn
  from torch.distributed import ProcessGroup, rpc
  from torch.distributed.distributed_c10d import _get_global_rank
  from fairscale.nn.model_parallel.initialize import get_pipeline_parallel_group
  from .async_pipe import AsyncPipe
  from .types import EVENT_LOOP_QUEUE, PipeMessage, TensorOrTensors
label_desc: Use image node_id 5 for calling a global function with example usage: callback_with_model(callback, ctx) without return types