class_docstr: Ancestor to all operators in this subfolder.
  
  Args:
      onnx_node: `onnx` node
      run_params: additional parameters such as `verbose`, `opsets`
          (it can be more than one if the operator has a subgraph),
          `log` for a logging function
      schema: operator schema
class_docstr_tok: ["Ancestor","to","all","operators","in","this","subfolder",".","Args",":","onnx_node",":","`","onnx","`","node","run_params",":","additional","parameters","such","as","`","verbose","`",",","`","opsets","`","(","it","can","be","more","than","one","if","the","operator","has","a","subgraph",")",",","`","log","`","for","a","logging","function","schema",":","operator","schema"]
docstr: Returns node attribute `op_type`.
docstr_tok: ["Returns","node","attribute","`","op_type","`","."]
code: def op_type(self) -> str:
      """Returns node attribute `op_type`."""
      return self.onnx_node.op_type
  
imports: from __future__ import annotations
  import abc
  from typing import Any, ClassVar, Iterable
  import numpy
  from onnx import TensorProto
  from onnx.defs import get_all_schemas_with_history, get_schema, onnx_opset_version
  from onnx.helper import make_node, make_tensor_type_proto, np_dtype_to_tensor_dtype
  from onnx.numpy_helper import to_array, unpack_int4
  from onnx.onnx_pb import AttributeProto, GraphProto, NodeProto, TypeProto
  from onnx.reference.custom_element_types import bfloat16, float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, uint4
label_desc: Use image node_id 9 for calling the OpRun obj's underlying member method code with example usage: obj.op_type() and returns: self