code: def test_make_univariate_time_series(df):
      """Tests make_univariate_time_series function"""
      forecast = UnivariateForecast(
          df, train_end_date=datetime.datetime(2018, 1, 2)
      )
      ts = UnivariateTimeSeries()
      ts.load_data(
          pd.DataFrame(
              {
                  cst.TIME_COL: df[cst.TIME_COL],
                  cst.VALUE_COL: df[cst.PREDICTED_COL],
              }
          ),
          cst.TIME_COL,
          cst.VALUE_COL,
      )
      assert forecast.make_univariate_time_series().df.equals(ts.df)
  
imports: import datetime
  import math
  import sys
  from functools import partial
  import numpy
  import pandas
  import pytest
  from pandas.testing import assert_frame_equal
  from pandas.testing import assert_series_equal
  from sklearn.pipeline import Pipeline
  from greykite.common import constants
  from greykite.common.evaluation import ElementwiseEvaluationMetricEnum
  from greykite.common.evaluation import EvaluationMetricEnum
  from greykite.common.python_utils import assert_equal
  from greykite.common.testing_utils import gen_sliced_df
  from greykite.framework.input.univariate_time_series import UnivariateTimeSeries
  from greykite.framework.output.univariate_forecast import UnivariateForecast
  from greykite.framework.pipeline.utils import get_forecast
  from greykite.sklearn.estimator.prophet_estimator import ProphetEstimator
  from greykite.sklearn.estimator.silverkite_estimator import SilverkiteEstimator
  from greykite.sklearn.estimator.testing_utils import params_component_breakdowns
label_desc: Use image node_id 17 for calling a global function with example usage: test_make_univariate_time_series(df) without return types