code: def is_onnxrt_backend_supported() -> bool:
      """Returns ``True`` if ONNX Runtime dependencies are installed and usable
      to support TorchDynamo backend integration; ``False`` otherwise.
  
      Example::
  
          # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ONNX)
          >>> import torch
          >>> if torch.onnx.is_onnxrt_backend_supported():
          ...     @torch.compile(backend="onnxrt")
          ...     def f(x):
          ...             return x * x
          ...     print(f(torch.randn(10)))
          ... else:
          ...     print("pip install onnx onnxscript onnxruntime")
          ...
      """
      return _SUPPORT_ONNXRT
  
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 1 for calling a global function with example usage: is_onnxrt_backend_supported() and returns: _SUPPORT_ONNXRT