class_docstr: Backend is the entity that will take an ONNX model with inputs,
  perform a computation, and then return the output.
  
  For one-off execution, users can use run_node and run_model to obtain results quickly.
  
  For repeated execution, users should use prepare, in which the Backend
  does all of the preparation work for executing the model repeatedly
  (e.g., loading initializers), and returns a BackendRep handle.
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docstr: Simple run one operator and return the results.
  
  Args:
      node: The node proto.
      inputs: Inputs to the node.
      device: The device to run on.
      outputs_info: a list of tuples, which contains the element type and
          shape of each output. First element of the tuple is the dtype, and
          the second element is the shape. More use case can be found in
          https://github.com/onnx/onnx/blob/main/onnx/backend/test/runner/__init__.py
      kwargs: Other keyword arguments.
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code: def run_node(
      cls,
      node: NodeProto,
      inputs: Any,
      device: str = "CPU",
      outputs_info: Optional[
          Sequence[Tuple[numpy.dtype, Tuple[int, ...]]]
      ] = None,
      **kwargs: Dict[str, Any],
  ) -> Optional[Tuple[Any, ...]]:
      """Simple run one operator and return the results.
  
      Args:
          node: The node proto.
          inputs: Inputs to the node.
          device: The device to run on.
          outputs_info: a list of tuples, which contains the element type and
              shape of each output. First element of the tuple is the dtype, and
              the second element is the shape. More use case can be found in
              https://github.com/onnx/onnx/blob/main/onnx/backend/test/runner/__init__.py
          kwargs: Other keyword arguments.
      """
      # TODO Remove Optional from return type
      if "opset_version" in kwargs:
          special_context = c_checker.CheckerContext()
          special_context.ir_version = IR_VERSION
          special_context.opset_imports = {"": kwargs["opset_version"]}  # type: ignore
          onnx.checker.check_node(node, special_context)
      else:
          onnx.checker.check_node(node)
  
      return None
  
file_path: onnx/onnx/backend/base.py
imports: from collections import namedtuple
  from typing import Any, Dict, NewType, Optional, Sequence, Tuple, Type
  import numpy
  import onnx.checker
  import onnx.onnx_cpp2py_export.checker
  from onnx import IR_VERSION, ModelProto, NodeProto
label_desc: Use image node_id 4 for calling the Backend obj's underlying member method code with example usage: obj.run_node(cls, node, inputs, device, outputs_info) and returns: None