parameters: (cls,
                   node,  # type: NodeProto
                   inputs,  # type: Any
                   device='CPU',  # type: Text
                   outputs_info=None,  # type: Optional[Sequence[Tuple[numpy.dtype, Tuple[int, ...]]]]
                   **kwargs  # type: Dict[Text, Any]
                   )
return_statement: return None
docstring: Simple run one operator and return the results.
          Args:
              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/master/onnx/backend/test/runner/__init__.py
docstring_summary: Simple run one operator and return the results.
          Args:
              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/master/onnx/backend/test/runner/__init__.py
docstring_tokens: Simple run one operator and return the results . Args : 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 / master / onnx / backend / test / runner / __init__ . py
function: def run_node(cls,
                   node,  # type: NodeProto
                   inputs,  # type: Any
                   device='CPU',  # type: Text
                   outputs_info=None,  # type: Optional[Sequence[Tuple[numpy.dtype, Tuple[int, ...]]]]
                   **kwargs  # type: Dict[Text, Any]
                   ):  # type: (...) -> Optional[Tuple[Any, ...]]
          '''Simple run one operator and return the results.
          Args:
              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/master/onnx/backend/test/runner/__init__.py
          '''
          # 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
function_tokens: def run_node ( cls , node , # type: NodeProto inputs , # type: Any device = 'CPU' , # type: Text outputs_info = None , # type: Optional[Sequence[Tuple[numpy.dtype, Tuple[int, ...]]]] * * kwargs # type: Dict[Text, Any] ) : # type: (...) -> Optional[Tuple[Any, ...]] # 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