code: def _extract_graph_module_inputs(
      graph_module: torch.fx.GraphModule,
  ) -> Tuple[Any, ...]:
      placeholders = []
      for node in graph_module.graph.nodes:
          if node.op == "placeholder":
              if hasattr(node, "meta") and "val" in node.meta:
                  assert isinstance(node.meta["val"], torch.Tensor)
              placeholders.append(node)
      return tuple(placeholders)
  
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 8 for calling a global function with example usage: _extract_graph_module_inputs(graph_module) and returns: tuple