code: def _to_class_kwargs(kwargs, robust=False):
      endog = kwargs["y"]
      np = kwargs["np"]
      ns = kwargs["ns"]
      nt = kwargs["nt"]
      nl = kwargs["nl"]
      isdeg = kwargs["isdeg"]
      itdeg = kwargs["itdeg"]
      ildeg = kwargs["ildeg"]
      nsjump = kwargs["nsjump"]
      ntjump = kwargs["ntjump"]
      nljump = kwargs["nljump"]
      outer_iter = kwargs["no"]
      inner_iter = kwargs["ni"]
      class_kwargs = dict(
          endog=endog,
          period=np,
          seasonal=ns,
          trend=nt,
          low_pass=nl,
          seasonal_deg=isdeg,
          trend_deg=itdeg,
          low_pass_deg=ildeg,
          robust=robust,
          seasonal_jump=nsjump,
          trend_jump=ntjump,
          low_pass_jump=nljump,
      )
      return class_kwargs, outer_iter, inner_iter
  
imports: from statsmodels.compat.pandas import MONTH_END
  import os
  import pickle
  import numpy
  from numpy.testing import assert_allclose
  import pandas
  import pytest
  from statsmodels.datasets import co2
  from statsmodels.tsa.seasonal import STL, DecomposeResult
label_desc: Use image node_id 5 for calling a global function with example usage: _to_class_kwargs(kwargs, robust) and returns: class_kwargs,  outer_iter,  inner_iter