code: def _parse_sklearn_api(topology, model, inputs):
      """
      This is a delegate function adding the model to the input topology.
      It does nothing but invokes the correct parsing function according to the input model's type.
  
      Args:
          topology: The ``hummingbitd.ml._topology.Topology`` object where the model will be added
          model: A scikit-learn model object
          inputs: A list of `onnxconverter_common.topology.Variable`s
  
      Returns:
          The output `onnxconverter_common.topology.Variable`s produced by the input model
      """
      tmodel = type(model)
      if tmodel in sklearn_api_parsers_map:
          outputs = sklearn_api_parsers_map[tmodel](
              topology, model, inputs
          )
      else:
          outputs = _parse_sklearn_single_model(topology, model, inputs)
  
      return outputs
  
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 6 for calling a global function with example usage: _parse_sklearn_api(topology, model, inputs) and returns: outputs