class_docstr: A backend compiles (sub-)graphs in torch.fx.GraphModule to onnxruntime.InferenceSession calls.
  
  The compiler entry point is OrtBackend.compile, which
      1. partitions the original graph into supported sub-graphs (type: torch.fx.GraphModule) and unsupported
         sub-graphs.
      2. For each supported sub-graph, it replaces its _wrapped_call function with _ort_accelerated_call.
      3. Inside _ort_accelerated_call, it creates onnxruntime.InferenceSession and calls it to execute the sub-graph.
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docstr: If ``OrtBackendOptions.use_aot_autograd`` is ``True``, the `auto_autograd` compiler
  will be invoked, wrapping this ``OrtBackend`` instance's ``compile`` method. Otherwise,
  the ``compile`` method is invoked directly.
code: def __call__(
      self, graph_module: torch.fx.GraphModule, args
  ) -> torch.fx.GraphModule:
      """If ``OrtBackendOptions.use_aot_autograd`` is ``True``, the `auto_autograd` compiler
      will be invoked, wrapping this ``OrtBackend`` instance's ``compile`` method. Otherwise,
      the ``compile`` method is invoked directly."""
      if self._options.use_aot_autograd:
          from functorch.compile import (
              min_cut_rematerialization_partition,
          )
  
          from torch._dynamo.backends.common import aot_autograd
  
          return aot_autograd(
              fw_compiler=self.compile,
              partition_fn=min_cut_rematerialization_partition,
              decompositions=self._resolved_onnx_exporter_options.decomposition_table,
          )(graph_module, args)
  
      return self.compile(graph_module, args)
  
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 5 for calling the OrtBackend obj's underlying member method code with example usage: obj.__call__(graph_module, args) and returns: self, aot_autograd