code: def ft_stl(data: pd.Series, freq: Optional[int] = None) -> pd.Series:
      """Calculates features related to STL.
  
      Returns:
          pd.Series: Series with 4 values: trend, seasonality, acf1(error) and acf10(error).
      """
      if not freq:
          inferred = data.index.inferred_freq
          freq = freq_to_period(inferred) if inferred else -1
  
      if len(data) < 2 * freq:
          warnings.warn("Need 2 full periods of data", UserWarning)
          return pd.Series(
              {
                  "trend": np.nan,
                  "seasonality": np.nan,
                  "acf1(error)": np.nan,
                  "acf10(error)": np.nan,
              }
          )
  
      if freq > 1:
          seasonal = 13
          seasonal_jump = np.ceil(seasonal / 10).astype(int)
          seasonal_deg = 0
          trend = np.ceil(1.5 * freq / (1 - 1.5 / seasonal)).astype(int)
          trend += 1 if trend % 2 == 0 else 0
          trend = max(3, int(trend))
          trend_jump = np.ceil(trend / 10).astype(int)
          trend_deg = 1
          low_pass = freq + 1
          low_pass += 1 if low_pass % 2 == 0 else 0
          low_pass = max(3, int(low_pass))
          low_pass_jump = np.ceil(low_pass / 10).astype(int)
          low_pass_deg = trend_deg
          robust = False
  
          stl = STL(
              data.interpolate(limit_direction="both"),
              freq,
              seasonal=seasonal,
              trend=trend,
              low_pass=low_pass,
              seasonal_deg=seasonal_deg,
              trend_deg=trend_deg,
              low_pass_deg=low_pass_deg,
              robust=robust,
              seasonal_jump=seasonal_jump,
              trend_jump=trend_jump,
              low_pass_jump=low_pass_jump,
          ).fit()
  
          trend, season, resid = stl.trend, stl.seasonal, stl.resid
      else:
          trend = _ss.supsmu(data).supsmu
          resid = data - trend
          season = [np.nan] * len(data)
  
      var_resid = np.var(resid, ddof=1)
      var_deseason = np.var(resid + trend, ddof=1)
      var_detrend = np.var(resid + season, ddof=1)
  
      c_trend = max(0, 1 - var_resid / var_deseason)
      c_season = max(0, 1 - var_resid / var_detrend)
  
      nlags = min(int(10 * np.log10(len(resid))), len(resid) - 1)
      resid_acf = acf(resid, nlags=nlags, fft=True)[1:11]
      c_eacf1 = resid_acf[0]
      c_eacf10 = (resid_acf**2).sum()
  
      return pd.Series(
          {
              "trend": c_trend,
              "seasonality": c_season,
              "acf1(error)": c_eacf1,
              "acf10(error)": c_eacf10,
          }
      )
  
imports: from typing import Optional, Callable, Union, Tuple
  from functools import partial
  import warnings
  import numpy
  import pandas
  from scipy.signal import welch
  from statsmodels.tsa.seasonal import STL
  from statsmodels.tsa.stattools import acf, pacf, kpss, adfuller
  from statsmodels.tsa.tsatools import freq_to_period
  from .None import _supsmu
  from tslumen.profile.base import ProfilingFunction
label_desc: Use image node_id 3 for calling a global function with example usage: ft_stl(data, freq) and returns: pd, pd