class_docstr: Operator support for ONNXRuntime backend.
  
  It has two-level of support decision. One is via support_dict and the other one
  is via extra_support_dict. The logic of using support_dict is implemented in
  OrtOperatorSupport and extra_support_dict is used by OperatorSupport.is_node_supported.
class_docstr_tok: ["Operator","support","for","ONNXRuntime","backend",".","It","has","two-level","of","support","decision",".","One","is","via","support_dict","and","the","other","one","is","via","extra_support_dict",".","The","logic","of","using","support_dict","is","implemented","in","OrtOperatorSupport","and","extra_support_dict","is","used","by","OperatorSupport.is_node_supported","."]
code: def __init__(
      self, support_dict: Set[Any], extra_support_dict: Dict[str, Any]
  ):
      # Use extra_support_dict[op_name] = None to indicate
      # we support op_name with all input types. Otherwise,
      # see support_dict (type: SupportDict) in operator_support.py
      # for specifying supported types.
      super().__init__(extra_support_dict)
      self._onnx_support_dict = support_dict
  
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 to create a new OrtOperatorSupport object from inherited base classes: OperatorSupport with example: obj = OrtOperatorSupport(support_dict, extra_support_dict)