code: def test_model_uses_user_provided_training_params(model_name=None):
      np.random.seed(0)
  
      (
          df_titanic_train,
          df_titanic_test,
      ) = utils.get_titanic_binary_classification_dataset()
  
      column_descriptions = {
          "survived": "output",
          "sex": "categorical",
          "embarked": "categorical",
          "pclass": "categorical",
      }
  
      ml_predictor = Predictor(
          type_of_estimator="classifier",
          column_descriptions=column_descriptions,
      )
  
      try:
          ml_predictor.train(
              df_titanic_train,
              model_names="RidgeClassifier",
              training_params={"this_param_is_not_valid": True},
          )
          assert False
      except ValueError as e:
          assert True
  
file_path: auto_ml/tests/core_tests/basic_tests.py
imports: import datetime
  import os
  import random
  import sys
  from auto_ml import Predictor
  from auto_ml.utils_models import load_ml_model
  from nose.tools import assert_equal, assert_not_equal, with_setup
  from sklearn.metrics import accuracy_score
  import dill
  import numpy
  import utils_testing
label_desc: Use image node_id 3 for calling a global function with example usage: test_model_uses_user_provided_training_params(model_name) without return types