code: def _adjust_scalar_from_onnx_to_fx(
      tensor: torch.Tensor,
      prim_value: Union[
          torch.Tensor,
          torch.SymInt,
          int,
          torch.SymFloat,
          float,
          torch.SymBool,
          bool,
      ],
  ) -> Union[torch.Tensor, int, float, bool,]:
      """Helper function to wrap ORT-produced torch.Tensor as PyTorch variables"""
      assert isinstance(
          tensor, torch.Tensor
      ), "ORT's output must be tensor."
      if isinstance(
          prim_value,
          (
              torch.SymInt,
              int,
              torch.SymFloat,
              float,
              torch.SymBool,
              bool,
          ),
      ):
          # Convert tensor back to scalar to match Dynamo's expectation.
          return tensor.item()
      return tensor
  
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 16 for calling a global function with example usage: _adjust_scalar_from_onnx_to_fx(tensor, prim_value) and returns: tensor, tensor