code: def test_ktrlite_single_seas(make_daily_data, seasonality_fs_order):
      train_df, _, _ = make_daily_data
  
      ktrlite = KTRLite(
          response_col="response",
          date_col="date",
          seasonality=[365.25],
          seasonality_fs_order=seasonality_fs_order,
          estimator="stan-map",
          n_bootstrap_draws=-1,
      )
  
      ktrlite.fit(train_df)
      predict_df = ktrlite.predict(train_df)
  
      expected_columns = ["date", "prediction"]
      expected_shape = (train_df.shape[0], len(expected_columns))
      expected_num_parameters = (
          len(ktrlite._model.get_model_param_names()) + 1
      )
  
      assert predict_df.shape == expected_shape
      assert predict_df.columns.tolist() == expected_columns
      assert len(ktrlite._posterior_samples) == expected_num_parameters
      smape_val = smape(
          train_df["response"].values, predict_df["prediction"].values
      )
      assert smape_val <= SMAPE_TOLERANCE
  
file_path: orbit/tests/orbit/models/test_ktrlite.py
imports: import pytest
  import numpy
  import pandas
  from orbit.models import KTRLite
  from orbit.diagnostics.metrics import smape
label_desc: Use image node_id 1 for calling a global function with example usage: test_ktrlite_single_seas(make_daily_data, seasonality_fs_order) without return types