diff --git a/GPy/kern/_src/coregionalize.py b/GPy/kern/_src/coregionalize.py
index 0d99ce21..74cd2a1d 100644
--- a/GPy/kern/_src/coregionalize.py
+++ b/GPy/kern/_src/coregionalize.py
@@ -135,3 +135,7 @@ class Coregionalize(Kern):
 
     def gradients_X(self, dL_dK, X, X2=None):
         return np.zeros(X.shape)
+
+    def gradients_X_diag(self, dL_dKdiag, X):
+        return np.zeros(X.shape)
+
diff --git a/GPy/kern/_src/stationary.py b/GPy/kern/_src/stationary.py
index aaa534ac..7cc2e695 100644
--- a/GPy/kern/_src/stationary.py
+++ b/GPy/kern/_src/stationary.py
@@ -49,9 +49,6 @@ class Stationary(Kern):
         self.variance.gradient = np.sum(dL_dKdiag)
         self.lengthscale.gradient = 0.
 
-    def gradients_X_diag(self, dL_dKdiag, X):
-        return np.zeros(X.shape)
-
     def update_gradients_full(self, dL_dK, X, X2=None):
         K = self.K(X, X2)
         self.variance.gradient = np.sum(K * dL_dK)/self.variance
@@ -82,6 +79,9 @@ class Stationary(Kern):
             ret *= 2.
         return ret
 
+    def gradients_X_diag(self, dL_dKdiag, X):
+        return np.zeros(X.shape)
+
 
 
 
@@ -104,16 +104,6 @@ class Matern32(Stationary):
 
        k(r) = \\sigma^2 (1 + \\sqrt{3} r) \exp(- \sqrt{3} r) \\ \\ \\ \\  \\text{ where  } r = \sqrt{\sum_{i=1}^input_dim \\frac{(x_i-y_i)^2}{\ell_i^2} }
 
-    :param input_dim: the number of input dimensions
-    :type input_dim: int
-    :param variance: the variance :math:`\sigma^2`
-    :type variance: float
-    :param lengthscale: the vector of lengthscale :math:`\ell_i`
-    :type lengthscale: array or list of the appropriate size (or float if there is only one lengthscale parameter)
-    :param ARD: Auto Relevance Determination. If equal to "False", the kernel is isotropic (ie. one single lengthscale parameter \ell), otherwise there is one lengthscale parameter per dimension.
-    :type ARD: Boolean
-    :rtype: kernel object
-
     """
 
     def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, name='Mat32'):