code: def force_lazy_device(model: fx.GraphModule):
      """
      Factory methods in a Fx graph may create tensors for a specific eager devices.
      If we take no actions, those eager tensors will be mixed with lazy tensors and
      cause crash. This method overwrite those eager device to lazy device.
      """
  
      def tolazydevice(dev):
          if isinstance(dev, torch.device):
              return torch.device("lazy", index=dev.index)
          return dev
  
      def hasDeviceArg(args, kwargs):
          return any(
              isinstance(arg, torch.device)
              for arg in itertools.chain(args, kwargs.values())
          )
  
      for nd in model.graph.nodes:
          nd.args = tuple(tolazydevice(arg) for arg in nd.args)
          nd.kwargs = {k: tolazydevice(v) for k, v in nd.kwargs.items()}
  
          # For torchbench like yolov3, hf_Bart, dynamo generates Fx graph that return
          # eager tensors on the default device
          # (check https://gist.github.com/shunting314/eabdf6c769c59bc384469717b8f9bb7f for yolove,
          # and https://gist.github.com/shunting314/8d5e2d9348a3258959d3954186c48814 for hf_Bart).
          # To force those tensors on the lazy device, we can not simply override
          # the device argument since there is no explicit device argument.
          # What we are doing here is, for the list of covered tensor factory methods
          # we add a lazy device argument explicity.
          #
          # TODO: This solution is no ideal since we may miss some factory methods. In future
          # when we support lazy mode, this method can be replaced by that.
          if (
              nd.target in tensor_factory_functions
              and not hasDeviceArg(nd.args, nd.kwargs)
          ):
              kwargs = dict(
                  nd.kwargs
              )  # nd.kwargs is immutable. make a mutable copy.
              kwargs["device"] = torch.device("lazy")
              nd.kwargs = kwargs
  
      model.recompile()
  
file_path: pytorch/torch/_lazy/extract_compiled_graph.py
imports: import copy
  import dataclasses
  import itertools
  import os
  from typing import Any, Callable, Dict, List
  import torch
  import torch._lazy
  import torch._lazy.metrics
  from torch import fx
  from torch._lazy import computation, debug
  from torch._lazy.tensor_factory_functions import tensor_factory_functions
label_desc: Use image node_id 1 for calling a global function with example usage: force_lazy_device(model) and returns: dev, any, torch