code: def test_ktrlite_predict_decompose(make_daily_data):
      train_df, test_df, coef = make_daily_data
  
      ktrlite = KTRLite(
          response_col="response",
          date_col="date",
          seasonality=[7, 365.25],
          seasonality_fs_order=[2, 5],
          estimator="stan-map",
          n_bootstrap_draws=1e4,
      )
  
      ktrlite.fit(train_df)
      predict_df = ktrlite.predict(test_df, decompose=True)
  
      expected_columns = [
          "date",
          "prediction_5",
          "prediction",
          "prediction_95",
          "trend_5",
          "trend",
          "trend_95",
          "seasonality_7_5",
          "seasonality_7",
          "seasonality_7_95",
          "seasonality_365.25_5",
          "seasonality_365.25",
          "seasonality_365.25_95",
      ]
      expected_shape = (364, 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
  
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 7 for calling a global function with example usage: test_ktrlite_predict_decompose(make_daily_data) without return types