class_docstr: WLS with heteroscedasticity that depends on explanatory variables
  
  note: mixing GLS sigma and weights for heteroscedasticity might not make
  sense
  
  I think rewriting following the pattern of GLSAR is better
  stopping criteria: improve in GLSAR also, e.g. change in rho
class_docstr_tok: ["WLS","with","heteroscedasticity","that","depends","on","explanatory","variables","note",":","mixing","GLS","sigma","and","weights","for","heteroscedasticity","might","not","make","sense","I","think","rewriting","following","the","pattern","of","GLSAR","is","better","stopping","criteria",":","improve","in","GLSAR","also",",","e.g",".","change","in","rho"]
code: def __init__(self, endog, exog, exog_var, sigma=None):
      self.exog_var = atleast_2dcols(exog_var)
      super(self.__class__, self).__init__(endog, exog, sigma=sigma)
  
file_path: statsmodels/statsmodels/regression/feasible_gls.py
imports: import numpy
  from statsmodels.regression.linear_model import OLS, GLS, WLS
label_desc: Use image node_id 1 to create a new GLSHet2 object from inherited base classes: GLS with example: obj = GLSHet2(endog, exog, exog_var, sigma)