code: def supsmu(
      data: pd.Series,
      alpha: Optional[float] = None,
      period: Optional[float] = None,
      primary_spans: Tuple[float, ...] = (0.05, 0.2, 0.5),
      middle_span: float = 0.2,
      final_span: float = 0.05,
  ) -> pd.DataFrame:
      """SuperSmoother for nonparametric smoothing of scatter plots.
  
      Args:
          data (pd.Series): Time series.
          alpha (float): Bass enhancement / smoothing level (0 < alpha < 10).
          period (float): Data periodicity.
          primary_spans (Tuple[float]): Primary span values used for the smooth (between 0 and 1).
          middle_span (float): Middle span value.
          final_span (float): Final span value.
  
      Returns:
          pd.DataFrame: DataFrame with original and smooth series, labelled 'original' and 'supsmu'
          respectively.
  
      See Also:
          http://doi.org/10.5281/zenodo.14475
      """
      return _ss.supsmu(
          data=data,
          alpha=alpha,
          period=period,
          primary_spans=primary_spans,
          middle_span=middle_span,
          final_span=final_span,
      )
  
file_path: tslumen/tslumen/profile/smooth.py
imports: from typing import Optional, Tuple
  import numpy
  import pandas
  from statsmodels.nonparametric import smoothers_lowess
  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: supsmu(data, alpha, period, primary_spans, middle_span, final_span) and returns: _ss