code: def _parse_onnx_single_operator(topology, operator):
      """
      This function handles the parsing of all ONNX operators.
  
      Args:
          topology: The ``onnxconverter_common.topology.Topology`` where the model will be added
          model: An ONNX operator
      """
  
      # Identify nodes can just be skipped.
      if operator.op_type == "Identity":
          if operator.origin.input[0] != operator.origin.output[0]:
              variable = topology.variables[operator.origin.input[0]]
              variable.raw_name = operator.origin.output[0]
              variable.onnx_name = operator.origin.output[0]
          return
  
      # Add the operator in the topology.
      alias = get_onnxml_api_operator_name(operator.op_type)
      if alias is None:
          alias = "ONNXML" + operator.op_type
      this_operator = topology.declare_logical_operator(alias, operator)
  
      # Register the operator's inputs.
      input_names = list(operator.origin.input)
      this_operator.inputs = [
          topology.variables[in_]
          for in_ in input_names
          if in_ in topology.variables
      ]
  
      # Register the operator's outpurs.
      output_names = list(operator.output.keys())
      output_names.sort()
      for output in output_names:
          variable = topology.declare_logical_variable(output)
          this_operator.outputs.append(variable)
  
imports: from collections import OrderedDict
  from copy import deepcopy
  import pprint
  from uuid import uuid4
  import numpy
  from onnxconverter_common.optimizer import LinkedNode, _topological_sort
  from sklearn import pipeline
  from sklearn.compose import ColumnTransformer
  from sklearn.ensemble import BaggingClassifier, BaggingRegressor
  from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
  from sklearn.multioutput import MultiOutputRegressor, RegressorChain
  from sklearn.preprocessing import OneHotEncoder
  from .containers import CommonSklearnModelContainer, CommonONNXModelContainer, CommonSparkMLModelContainer
  from ._topology import Topology
  from ._utils import sklearn_installed, sparkml_installed
  from .operator_converters import constants
  from .supported import get_sklearn_api_operator_name, get_onnxml_api_operator_name, get_sparkml_api_operator_name
label_desc: Use image node_id 20 for calling a global function with example usage: _parse_onnx_single_operator(topology, operator) without return types