code: def test_when_npts_fit_with_default_seasonal_features_then_predictions_match_gluonts(
      freq,
  ):
      from gluonts.model.npts import NPTSPredictor
  
      item_id = "A"
      prediction_length = 9
      data = get_data_frame_with_item_index(
          [item_id], freq=freq, data_length=100
      )
  
      npts_ag = NPTSModel(
          freq=freq,
          prediction_length=prediction_length,
          hyperparameters={"n_jobs": 1, "use_fallback_model": False},
      )
      npts_gts = NPTSPredictor(prediction_length=prediction_length)
      npts_ag.fit(train_data=data)
  
      np.random.seed(123)
      pred_ag = npts_ag.predict(data)
  
      np.random.seed(123)
      ts = data.loc[item_id]["target"]
      ts.index = ts.index.to_period(freq=freq)
      pred_gts = npts_gts.predict_time_series(ts, num_samples=100)
  
      assert (pred_gts.mean == pred_ag["mean"]).all()
      for q in npts_ag.quantile_levels:
          assert (pred_gts.quantile(str(q)) == pred_ag[str(q)]).all()
  
imports: import logging
  from typing import Dict
  import numpy
  import pandas
  import pytest
  from autogluon.timeseries import TimeSeriesDataFrame
  from autogluon.timeseries.models.local import ADIDAModel, AutoARIMAModel, AutoCESModel, AutoETSModel, AverageModel, CrostonClassicModel, CrostonOptimizedModel, CrostonSBAModel, DynamicOptimizedThetaModel, ETSModel, IMAPAModel, NaiveModel, NPTSModel, SeasonalAverageModel, SeasonalNaiveModel, ThetaModel, ZeroModel
  from autogluon.timeseries.models.local.statsforecast import AbstractConformalizedStatsForecastModel
  from ..common import DUMMY_TS_DATAFRAME, DUMMY_VARIABLE_LENGTH_TS_DATAFRAME, dict_equal_primitive, get_data_frame_with_item_index
label_desc: Use image node_id 14 for calling a global function with example usage: test_when_npts_fit_with_default_seasonal_features_then_predictions_match_gluonts(freq) without return types