code: def _test_module(m):
      assert m.model_type in [
          "transformer",
          "regressor",
          "classifier",
      ], m.__name__
      if m.model_type == "transformer":
          assert hasattr(m, "update_dimension"), m.__name__
      if m.model_type == "classifier":
          assert hasattr(m, "supports_output_scores"), m.__name__
          assert hasattr(m, "get_output_classes"), m.__name__
      assert hasattr(m, "sklearn_class"), m.__name__
      assert hasattr(m, "get_input_dimension"), m.__name__
  
      return True
  
file_path: coremltools/coremltools/converters/sklearn/_converter_internal.py
imports: from ..._deps import _HAS_SKLEARN
  from ...models import _feature_management
  from ...models import datatypes
  from ...models.feature_vectorizer import create_feature_vectorizer
  from ...models.pipeline import Pipeline, PipelineClassifier, PipelineRegressor
  from collections import namedtuple
  from .None import _SVC, _SVR, _decision_tree_classifier, _decision_tree_regressor, _dict_vectorizer, _gradient_boosting_classifier, _gradient_boosting_regressor, _imputer, _k_neighbors_classifier, _linear_regression, _LinearSVC, _LinearSVR, _logistic_regression, _normalizer, _NuSVC, _NuSVR, _one_hot_encoder, _random_forest_classifier, _random_forest_regressor, _standard_scaler, _ridge_regression
label_desc: Use image node_id 1 for calling a global function with example usage: _test_module(m) and returns: True