code: def _get_converter_module(sk_obj):
      """
      Returns the module holding the conversion functions for a
      particular model).
      """
      try:
          cv_idx = _converter_lookup[sk_obj.__class__]
      except KeyError:
          raise ValueError(
              "Transformer '%s' not supported; supported transformers are %s."
              % (
                  repr(sk_obj),
                  ",".join(k.__name__ for k in _converter_module_list),
              )
          )
  
      return _converter_module_list[cv_idx]
  
imports: from ...models import _feature_management
  from ...models import datatypes
  from ...models.feature_vectorizer import create_feature_vectorizer
  from ...models.pipeline import Pipeline, PipelineRegressor, PipelineClassifier
  from ..._deps import _HAS_SKLEARN
  from collections import namedtuple
  import numpy
  from six import string_types
  from .None import _one_hot_encoder
  from .None import _dict_vectorizer
  from .None import _NuSVC
  from .None import _NuSVR
  from .None import _SVC
  from .None import _SVR
  from .None import _LinearSVC
  from .None import _LinearSVR
  from .None import _linear_regression
  from .None import _decision_tree_classifier
  from .None import _decision_tree_regressor
  from .None import _gradient_boosting_classifier
  from .None import _gradient_boosting_regressor
  from .None import _logistic_regression
  from .None import _normalizer
  from .None import _imputer
  from .None import _random_forest_classifier
  from .None import _random_forest_regressor
  from .None import _standard_scaler
  from .None import _k_neighbors_classifier
label_desc: Use image node_id 2 for calling a global function with example usage: _get_converter_module(sk_obj) and returns: _converter_module_list