Get an estimate of the SM parameters at the input scale by running
        them from the EW scale using constant values for the Wilson coefficients
        (corresponding to their leading log approximated values at the EW
        scale).

        Note that this is not guaranteed to work and will fail if some of the
        Wilson coefficients (the ones affecting the extraction of SM parameters)
        are large.

def _get_sm_scale_in(self, scale_sm=91.1876):
        """Get an estimate of the SM parameters at the input scale by running
        them from the EW scale using constant values for the Wilson coefficients
        (corresponding to their leading log approximated values at the EW
        scale).

        Note that this is not guaranteed to work and will fail if some of the
        Wilson coefficients (the ones affecting the extraction of SM parameters)
        are large."""
        # intialize a copy of ourselves
        _smeft = SMEFT()
        _smeft.set_initial(self.C_in, self.scale_in, self.scale_high)
        # Step 1: run the SM up, using the WCs at scale_input as (constant) estimate
        _smeft.C_in.update(self._run_sm_scale_in(self.C_in, scale_sm=scale_sm))
        # Step 2: run the WCs down in LL approximation
        C_out = _smeft.rgevolve_leadinglog(scale_sm)
        # Step 3: run the SM up again, this time using the WCs at scale_sm as (constant) estimate
        return self._run_sm_scale_in(C_out, scale_sm=scale_sm)