<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def gradient_local(self, f, index):
 """ Return the gradient at a specified node. This routine employs a local method, in which values depend only on nearby data points, to compute an estimated gradient at a node. gradient_local() is more efficient than gradient() only if it is unnecessary to compute gradients at all of the nodes. Both routines have similar accuracy. Parameters """

        if f.size != self.npoints:
            raise ValueError('f should be the same size as mesh')

        f = self._shuffle_field(f)


        gradX, gradY, l = _srfpack.gradl(index + 1, self._x, self._y, f,\
                                         self.lst, self.lptr, self.lend)

        return gradX, gradY