code: def profile_module(
      module: torch.nn.Module, device: str = "meta"
  ) -> Callable:
      """
      Wrap a `call_module` node or `torch.nn` in order to
      record the memory cost and FLOPs of the execution.
  
      Warnings:
          You may only use tensors with `device=meta` for this wrapped function.
          Only original `torch.nn` are available.
  
      Example:
          >>> input = torch.rand(4, 3, 224, 224, device='meta')
          >>> mod = torch.nn.Conv2d(3, 128, 3)
          >>> output, meta_info = profile_module(mod)(input)
      """
  
      def f(
          *args: Tuple[Argument, ...], **kwargs: Dict[str, Any]
      ) -> Any:
          # calculate parameter size
          param_size = parameter_size(module)
  
          # If there is an argument that this `call_module` is inplace, we should
          # still run the profiling but discard some results regarding `module`.
          global do_not_cache
  
          inplace = getattr(module, "inplace", False)
          if type(module) in OUTPUT_SAVED_MOD:
              do_not_cache = True
          if inplace:
              do_not_cache = True
              module.inplace = False
          if device == "meta":
              out, meta = _profile_meta(func, *args, **kwargs)
          else:
              out, meta = _profile_concrete(func, *args, **kwargs)
          if inplace:
              module.inplace = True
              meta.bwd_mem_tmp = 0
              meta.bwd_mem_out = 0
          do_not_cache = False
  
          # grad for param will not be counted
          meta.bwd_mem_out -= param_size
          return out, meta
  
      f.__name__ = module.__class__.__name__
      func = module.forward
      return f
  
imports: import time
  from functools import partial
  from typing import Any, Callable, Dict, Tuple
  import torch
  from torch.fx import Graph, Node
  from torch.fx.node import Argument, Target
  from torch.nn.parameter import Parameter
  from torch.utils._pytree import tree_map
  from .._compatibility import compatibility
  from .constants import ALIAS_ATEN, OUTPUT_SAVED_MOD, OUTPUT_SAVED_OPS
  from .dataflow import GraphInfo, Phase, autograd_graph_analysis, is_phase
  from .memory_utils import activation_size, parameter_size
  from .opcount import flop_mapping
  from .tensor import MetaTensor
label_desc: Use image node_id 8 for calling a global function with example usage: profile_module(module, device) and returns: f, out,  meta