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
  
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 1 for calling a global function with example usage: _test_module(m) and returns: True