(self)
return self._gradient_solve
Parameters to use for the gradient pass when an implicit gradient is computed.
        If `None`, a duplicate of this `Solve` is created for the gradient solve.

        In any case, the gradient solve information will be stored in `gradient_solve.result`.
Parameters to use for the gradient pass when an implicit gradient is computed.
        If `None`, a duplicate of this `Solve` is created for the gradient solve.
Parameters to use for the gradient pass when an implicit gradient is computed . If None a duplicate of this Solve is created for the gradient solve .
def gradient_solve(self) -> 'Solve[Y, X]':
        """
        Parameters to use for the gradient pass when an implicit gradient is computed.
        If `None`, a duplicate of this `Solve` is created for the gradient solve.

        In any case, the gradient solve information will be stored in `gradient_solve.result`.
        """
        if self._gradient_solve is None:
            self._gradient_solve = Solve(self.method, self.relative_tolerance, self.absolute_tolerance, self.max_iterations, None, self.suppress)
        return self._gradient_solve
def gradient_solve ( self ) -> 'Solve[Y, X]' : if self . _gradient_solve is None : self . _gradient_solve = Solve ( self . method , self . relative_tolerance , self . absolute_tolerance , self . max_iterations , None , self . suppress ) return self . _gradient_solve