code: def test_handle_negative_values_replace():
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      # Insert a negative value
      df.loc[0, "y"] = -1
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_lags=3,
          impute_missing=False,
          drop_missing=False,
      )
      df_ = m.handle_negative_values(df, handle=0.0)
      assert df_.loc[0, "y"] == 0.0
  
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 23 for calling a global function with example usage: test_handle_negative_values_replace() without return types