code: def test_given_categorical_training_data_with_many_categories_when_fit_regression_model_then_returns_reasonably_accurate_predictions():
      def _generate_data():
          X = np.column_stack(
              [
                  np.random.choice(20, 10000, replace=True).astype(str),
                  np.random.normal(0, 1, (10000, 2)).astype(object),
              ]
          ).astype(object)
          Y = [
              X[i, 1] * X[i, 1 + int(X[i, 0]) % 2]
              for i in range(X.shape[0])
          ]
  
          return X, np.array(Y)
  
      X_training, Y_training = _generate_data()
      X_test, Y_test = _generate_data()
      mdl = create_random_forest_regressor()
      mdl.fit(X_training, Y_training)
  
      assert (
          np.mean((mdl.predict(X_test).reshape(-1) - Y_test) ** 2) < 0.1
      )
  
imports: import numpy
  from _pytest.python_api import approx
  from flaky import flaky
  from dowhy.gcm.ml.regression import create_polynom_regressor, create_random_forest_regressor
label_desc: Use image node_id 3 for calling a global function with example usage: test_given_categorical_training_data_with_many_categories_when_fit_regression_model_then_returns_reasonably_accurate_predictions() and returns: X,  np