code: def test_no_changepoints():
      log.info("testing: Trend")
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      m = NeuralProphet(
          growth="linear",
          n_changepoints=0,
          trend_reg_threshold=False,
          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
      )
      forecast = 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 6 for calling a global function with example usage: test_no_changepoints() without return types