code: def test_reg_delay():
      df = pd.read_csv(PEYTON_FILE, nrows=102)[:100]
      m = NeuralProphet(
          epochs=10,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      m.fit(df, freq="D")
      c = m.config_train
      for w, e, i in [
          (0, 0, 1),
          (0, 3, 0),
          (0, 5, 0),
          # (0.002739052315863355, 5, 0.1),
          (0.5, 6, 0.5),
          # (0.9972609476841366, 7, 0.9),
          (1, 7, 1),
          (1, 8, 0),
      ]:
          weight = c.get_reg_delay_weight(
              e, i, reg_start_pct=0.5, reg_full_pct=0.8
          )
          assert weight == w
  
imports: import logging
  import os
  import pathlib
  import matplotlib.pyplot
  import numpy
  import pandas
  import pytest
  from torch.utils.data import DataLoader
  from neuralprophet import NeuralProphet, configure, df_utils, time_dataset
  from neuralprophet.data.process import _create_dataset, _handle_missing_data
  from neuralprophet.data.transform import _normalize
label_desc: Use image node_id 9 for calling a global function with example usage: test_reg_delay() without return types