code: def test_seasons():
      log.info("testing: Seasonality: additive")
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      # Additive
      m = NeuralProphet(
          yearly_seasonality=8,
          weekly_seasonality=4,
          seasonality_mode="additive",
          seasonality_reg=1,
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      m.fit(df, freq="D")
      future = m.make_future_dataframe(
          df, n_historic_predictions=365, periods=365
      )
      forecast = m.predict(df=future)
      if PLOT:
          m.plot(forecast)
          # m.plot_components(forecast)
          m.plot_parameters()
          plt.show()
      log.info("testing: Seasonality: multiplicative")
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      # Multiplicative
      m = NeuralProphet(
          yearly_seasonality=8,
          weekly_seasonality=4,
          seasonality_mode="multiplicative",
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      m.fit(df, freq="D")
      future = m.make_future_dataframe(
          df, n_historic_predictions=365, periods=365
      )
      forecast = m.predict(df=future)
  
imports: import logging
  import math
  import os
  import pathlib
  import matplotlib.pyplot
  import numpy
  import pandas
  import pytest
  import torch
  from neuralprophet import NeuralProphet, df_utils, set_random_seed
  from neuralprophet.data.process import _handle_missing_data, _validate_column_name
label_desc: Use image node_id 8 for calling a global function with example usage: test_seasons() without return types