code: def create_models_classification(X_train, y_train):
      """Create a list of models for classification.
  
      :param X_train: The training data.
      :type X_train: numpy.ndarray or pandas.DataFrame
      :param y_train: The training labels.
      :type y_train: numpy.ndarray or pandas.DataFrame
      :return: A list of models.
      :rtype: list
      """
      svm_model = create_sklearn_svm_classifier(X_train, y_train)
      log_reg_model = create_sklearn_logistic_regressor(
          X_train, y_train
      )
      xgboost_model = create_xgboost_classifier(X_train, y_train)
      lgbm_model = create_lightgbm_classifier(X_train, y_train)
      rf_model = create_sklearn_random_forest_classifier(
          X_train, y_train
      )
  
      return [
          svm_model,
          log_reg_model,
          xgboost_model,
          lgbm_model,
          rf_model,
      ]
  
imports: from rai_test_utils.models.lightgbm import create_lightgbm_classifier
  from rai_test_utils.models.sklearn import create_sklearn_logistic_regressor, create_sklearn_random_forest_classifier, create_sklearn_random_forest_regressor, create_sklearn_svm_classifier
  from rai_test_utils.models.torch import get_object_detection_fridge_model
  from rai_test_utils.models.xgboost import create_xgboost_classifier
label_desc: Use image node_id 1 for calling a global function with example usage: create_models_classification(X_train, y_train) and returns: list