code: def grad(self, X, lenscale=None):
          r
          N, D = X.shape
          lenscale = self._check_dim(D, lenscale)[:, np.newaxis]
  
          WX = np.dot(X, self.W / lenscale)
          sinWX = - np.sin(WX)
          cosWX = np.cos(WX)
  
          dPhi = []
          for i, l in enumerate(lenscale):
              dWX = np.outer(X[:, i], - self.W[i, :] / l**2)
              dPhi.append(np.hstack((dWX * sinWX, dWX * cosWX)) /
                          np.sqrt(self.n))
  
          return np.dstack(dPhi) if len(lenscale) != 1 else dPhi[0]
docs: r"""
          Get the gradients of this basis w.r.t.\ the length scales.
  
          Parameters
          ----------
          X: ndarray
              (N, d) array of observations where N is the number of samples, and
              d is the dimensionality of X.
          lenscale: scalar or ndarray, optional
              scalar or array of shape (d,) length scales (one for each dimension
              of X). If not input, this uses the value of the initial length
              scale.
  
          Returns
          -------
          ndarray:
              of shape (N, 2*nbases[, d]) where d is number of lenscales (if not
              ARD, i.e. scalar lenscale, this is just a 2D array). This is
              :math:`\partial \Phi(\mathbf{X}) / \partial \mathbf{l}`