problme with pythoinns PDF for new ROOT/cppyy

tst38.py

import ROOT, math

print ( 'ROOT version %s' % ROOT.gROOT.GetVersion() )

ROOT.gInterpreter.Declare('''
#include "RooAbsPdf.h"

class PyPdf  : public RooAbsPdf
{
public:
// ======================================================================
ClassDefOverride(PyPdf, 2 ) ;
// ======================================================================
public :
// ======================================================================
PyPdf ( const char*  name  ,
const char*  title   ,
RooArgList&  varlist  )
: RooAbsPdf ( name  , title )
, m_varlist  ( "!varlist" , "All variables(list)" , this )
{
m_varlist.addTyped<RooAbsReal>( varlist ) ;
}
// copy  constructor
PyPdf ( const PyPdf& right , const char* name = nullptr )
: RooAbsPdf ( right , name  )
, m_varlist ( "!varlist" , this , right.m_varlist )
{}
// virtual destructor
virtual ~PyPdf(){} ;
/// clone method
PyPdf* clone ( const char* name ) const override
{ return new PyPdf ( *this , name ) ; }
///
const RooArgList& varlist () const { return m_varlist ; }
//
// the actual evaluation of function (will be redefiend ni python!
Double_t evaluate() const override
{ return 1 ; }
// 
protected:
/// all variables as list of variables
RooListProxy m_varlist {} ; // all variables as list of variables
};
ClassImp(PyPdf);
''')


class MyPdf(ROOT.PyPdf) :
    
    def  __init__ ( self , name  , title , x , mean , sigma ) :

        vlst = ROOT.RooArgList()
        vlst.add ( x     )
        vlst.add ( mean  )
        vlst.add ( sigma )
        super(MyPdf,self).__init__ ( name , title, vlst )

    def  evaluate ( self ) :

        vars = self.varlist()
        x     = vars[0].getVal() 
        mean  = vars[1].getVal()
        sigma = vars[2].getVal() 

        dx = ( x - mean ) / sigma 
        return math.exp ( -0.5 * dx  * dx )
    
    def clone ( self, newname ) :

        if not newname :
            newname = self.GetName () + '_clone'
            
        vars = self.varlist()
        x     = vars[0] 
        mean  = vars[1] 
        sigma = vars[2]
        
        cl = MyPdf ( newname , self.GetTitle() ,
                     x, mean , sigma )

        ROOT.SetOwnership ( cl , False ) ## NB!
        
        return cl
    
        
xvar    = ROOT.RooRealVar  ( 'x'     , 'observable'      , 0 , 10 ) 
mean    = ROOT.RooRealVar  ( 'mean'  , 'gaussian mean'   , 5 , 4, 6 ) 
sigma   = ROOT.RooRealVar  ( 'sigma' , 'gaussian sigma'  , 1.5 , 1 , 2 )

gauss   = ROOT.RooGaussian ( 'gauss' , 'gauss' , xvar, mean , sigma )

varset  = ROOT.RooArgSet   ( xvar )
dataset = gauss.generate   ( varset , ROOT.RooFit.NumEvents ( 1000 ) )

pars = ROOT.RooFit.Save() , ROOT.RooFit.Verbose ( False ) , ROOT.RooFit.PrintLevel ( -1 ) 
print ( 'Fit  with native C++ RooGaussian' )

r_cpp = gauss.fitTo ( dataset , *pars )
r_cpp.Print ( 'vvv') 

pypdf = MyPdf ( 'pypdf' , 'my python pdf' , xvar , mean , sigma )

print ( 'Fit  with python Gaussian' ) 
r_py = pypdf.fitTo ( dataset , *pars )
r_py.Print ( 'vvv') 

print ( 'after the fit  with python Gaussian' )