code: def _compute_dy_dtau(self, tau, b, r2l2):
          r
          if len(b) == 0:
              return self._compute_y(tau)
          elif len(b) == 1:
              return 4.0 * self.nu * tau[:, b[0]] / (self.params[2 + b[0]])**2.0
          elif (len(b) == 2) and (b[0] == b[1]):
              return 4.0 * self.nu / (self.params[2 + b[0]])**2.0
          else:
              return scipy.zeros_like(r2l2)
docs: r"""Evaluate the derivative of the inner argument of the Matern kernel.
          
          Take the derivative of
          
          .. math::
          
              y = 2 \nu \sum_i(\tau_i^2 / l_i^2)
          
          Parameters
          ----------
          tau : :py:class:`Matrix`, (`M`, `D`)
              `M` inputs with dimension `D`.
          b : :py:class:`Array`, (`P`,)
              Block specifying derivatives to be evaluated.
          r2l2 : :py:class:`Array`, (`M`,)
              Precomputed anisotropically scaled distance.
          
          Returns
          -------
          dy_dtau: :py:class:`Array`, (`M`,)
              Specified derivative at specified locations.