code: def test_wrapper_components():
      log.info("testing: Wrapper components")
      df = pd.read_csv(AIR_FILE, nrows=NROWS)
      df["regressor"] = df["y"].rolling(7, min_periods=1).mean()
      regressors_df_future = pd.DataFrame(
          data={"regressor": df["regressor"][-50:]}
      )
  
      m = Prophet(
          seasonality_mode="multiplicative",
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
  
      m.add_seasonality(
          "quarterly", period=91.25, fourier_order=8, mode="additive"
      )
      m.add_regressor("regressor", mode="additive")
      m.fit(df)
  
      future = m.make_future_dataframe(
          periods=50, regressors_df=regressors_df_future
      )
      m.predict(future)
  
file_path: neural_prophet/tests/test_wrapper.py
imports: import logging
  import os
  import pathlib
  import pandas
  from neuralprophet import TorchProphet
label_desc: Use image node_id 2 for calling a global function with example usage: test_wrapper_components() without return types