code: def _infer_ep_from_device(*args) -> Tuple[str, ...]:
      """Return the first valid device (i.e., GPU or CPU) in argument list."""
      eps = []
      for arg in args:
          if hasattr(arg, "device"):
              device = arg.device
              if device.type == "cuda":
                  eps.append("CUDAExecutionProvider")
              elif device.type == "cpu":
                  eps.append("CPUExecutionProvider")
      return tuple(eps)
  
imports: import dataclasses
  import importlib
  import logging
  from typing import Any, Dict, Final, List, Mapping, Optional, Sequence, Set, Tuple, Union
  from typing_extensions import TypeAlias
  import torch
  import torch._C
  import torch._ops
  import torch._prims.executor
  import torch.fx
  from torch._subclasses.fake_tensor import FakeTensor
  from torch.fx._compatibility import compatibility
  from torch.fx.passes.fake_tensor_prop import FakeTensorProp
  from torch.fx.passes.operator_support import OperatorSupport
  from torch.fx.passes.tools_common import CALLABLE_NODE_OPS
  from torch.utils import _pytree
label_desc: Use image node_id 7 for calling a global function with example usage: _infer_ep_from_device() and returns: tuple