code: def test_custom_changepoints():
      log.info("testing: Custom Changepoints")
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      dates = df["ds"][range(1, len(df) - 1, int(len(df) / 5.0))]
      dates_list = [str(d) for d in dates]
      dates_array = pd.to_datetime(dates_list).values
      for cp in [dates_list, dates_array]:
          m = NeuralProphet(
              changepoints=cp,
              yearly_seasonality=False,
              weekly_seasonality=False,
              daily_seasonality=False,
              epochs=EPOCHS,
              batch_size=BATCH_SIZE,
              learning_rate=LR,
          )
          # print(m.config_trend)
          m.fit(df, freq="D")
          future = m.make_future_dataframe(
              df, periods=60, n_historic_predictions=60
          )
          m.predict(df=future)
          if PLOT:
              # m.plot(forecast)
              # m.plot_components(forecast)
              m.plot_parameters()
              plt.show()
  
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 5 for calling a global function with example usage: test_custom_changepoints() without return types