code: def _build_sklearn_api_parsers_map():
      # Parsers for edge cases are going here.
      map_parser = {
          ColumnTransformer: _parse_sklearn_column_transformer,
          GridSearchCV: _parse_sklearn_model_selection,
          MultiOutputRegressor: _parse_sklearn_multi_output_regressor,
          pipeline.Pipeline: _parse_sklearn_pipeline,
          pipeline.FeatureUnion: _parse_sklearn_feature_union,
          RandomizedSearchCV: _parse_sklearn_model_selection,
          RegressorChain: _parse_sklearn_regressor_chain,
          BaggingClassifier: _parse_sklearn_bagging,
          BaggingRegressor: _parse_sklearn_bagging,  # This may introduce some rounding error. TODO better implementation.
          # More parsers will go here
      }
  
      if StackingClassifier is not None:
          map_parser[StackingClassifier] = _parse_sklearn_stacking
          map_parser[StackingRegressor] = _parse_sklearn_stacking
  
      return map_parser
  
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 17 for calling a global function with example usage: _build_sklearn_api_parsers_map() and returns: map_parser