class_docstr: Mixin class for Maximum Penalized Likelihood
  
  Parameters
  ----------
  args and kwds for the model super class
  penal : None or instance of Penalized function class
      If penal is None, then NonePenalty is used.
  pen_weight : float or None
      factor for weighting the penalization term.
      If None, then pen_weight is set to nobs.
  
  
  TODO: missing **kwds or explicit keywords
  
  TODO: do we adjust the inherited docstrings?
  We would need templating to add the penalization parameters
code: def _handle_scale(self, params, scale=None, **kwds):
      if scale is None:
          # special handling for GLM
          if hasattr(self, "scaletype"):
              mu = self.predict(params)
              scale = self.estimate_scale(mu)
          else:
              scale = 1
  
      return scale
  
file_path: statsmodels/statsmodels/base/_penalized.py
imports: import numpy
  from ._penalties import NonePenalty
  from statsmodels.tools.numdiff import approx_fprime_cs, approx_fprime
label_desc: Use image node_id 2 for calling the PenalizedMixin obj's underlying member method code with example usage: obj._handle_scale(params, scale) and returns: scale