class_docstr: Class to test the AutoML flow
      
class_docstr_tok: ["Class","to","test","the","AutoML","flow"]
docstr: Tests passing models as a list instead of a string
docstr_tok: ["Tests","passing","models","as","a","list","instead","of","a","string"]
code: def test_passing_list_instead_of_str(self):
      """
      Tests passing models as a list instead of a string
      """
  
      print("\n\n" + "*" * 50)
      print("Performing Unit Test: 'test_passing_list_instead_of_str'")
      print("*" * 50 + "\n\n")
  
      # import numpy as np  # type: ignore
      # from auto_ts import auto_timeseries as ATS
  
      automl_model = ATS(
          score_type="rmse",
          forecast_period=self.forecast_period,
          time_interval="Month",
          non_seasonal_pdq=None,
          seasonality=False,
          seasonal_period=12,
          model_type=["auto_SARIMAX", "ML"],
          verbose=0,
      )
      automl_model.fit(
          traindata=self.train_multivar,
          ts_column=[self.ts_column],
          target=[self.target],
          cv=None,
          sep=self.sep,
      )
  
      leaderboard_models = np.array(["ML", "auto_SARIMAX"])
      np.testing.assert_array_equal(
          automl_model.get_leaderboard()["name"].values,
          leaderboard_models,
      )
  
imports: import sys
  import os
  import unittest
  import math
  import numpy
  import pandas
  from pandas.testing import assert_series_equal
  from pandas.testing import assert_frame_equal
  from prophet.forecaster import Prophet
  from statsmodels.tsa.statespace.sarimax import SARIMAXResultsWrapper
  from sklearn.base import BaseEstimator
  from auto_ts import auto_timeseries
label_desc: Use image node_id 7 for calling the TestAutoTS obj's underlying member method code with example usage: obj.test_passing_list_instead_of_str() without return types