code: def test_custom_priors(self, daily_univariate_ts, backend):
      holidays = pd.DataFrame(
          {
              "ds": pd.to_datetime(["2017-01-02"]),
              "holiday": ["special_day"],
              "prior_scale": [4.0],
          }
      )
      m = Prophet(
          holidays=holidays,
          yearly_seasonality=False,
          seasonality_mode="multiplicative",
          stan_backend=backend,
      )
      m.add_seasonality(
          name="monthly",
          period=30,
          fourier_order=5,
          prior_scale=2.0,
          mode="additive",
      )
      m.fit(daily_univariate_ts)
      assert m.seasonalities["monthly"]["mode"] == "additive"
      assert m.seasonalities["weekly"]["mode"] == "multiplicative"
      (
          seasonal_features,
          prior_scales,
          component_cols,
          modes,
      ) = m.make_all_seasonality_features(m.history)
      assert sum(component_cols["monthly"]) == 10
      assert sum(component_cols["special_day"]) == 1
      assert sum(component_cols["weekly"]) == 6
      assert sum(component_cols["additive_terms"]) == 10
      assert sum(component_cols["multiplicative_terms"]) == 7
  
      if seasonal_features.columns[0] == "monthly_delim_1":
          true = [2.0] * 10 + [10.0] * 6 + [4.0]
          assert sum(component_cols["monthly"][:10]) == 10
          assert sum(component_cols["weekly"][10:16]) == 6
      else:
          true = [10.0] * 6 + [2.0] * 10 + [4.0]
          assert sum(component_cols["weekly"][:6]) == 6
          assert sum(component_cols["monthly"][6:16]) == 10
      assert prior_scales == true
  
imports: import numpy
  import pandas
  import pytest
  from prophet import Prophet
  from prophet.utilities import warm_start_params
label_desc: Use image node_id 4 for calling the TestProphetCustomSeasonalComponent obj's underlying member method code with example usage: obj.test_custom_priors(daily_univariate_ts, backend) without return types