code: def test_auto_normalization():
      length = 100
      days = pd.date_range(start="2017-01-01", periods=length)
      y = np.ones(length)
      y[1] = 0
      y[2] = 2
      y[3] = 3.3
      df = pd.DataFrame({"ds": days, "y": y})
      df["future_constant"] = 1.0
      df["future_dynamic"] = df["y"] * 2
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          n_forecasts=5,
          normalize="auto",
      )
      m = m.add_future_regressor("future_constant")
      m = m.add_future_regressor("future_dynamic")
      _ = m.fit(df, freq="D")
  
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 33 for calling a global function with example usage: test_auto_normalization() without return types