diff --git a/GPy/kern/src/kernel_slice_operations.py b/GPy/kern/src/kernel_slice_operations.py
index 104bed48..921ac518 100644
--- a/GPy/kern/src/kernel_slice_operations.py
+++ b/GPy/kern/src/kernel_slice_operations.py
@@ -69,6 +69,8 @@ class _Slice_wrap(object):
                 ret[:, self.k._all_dims_active] = return_val
             elif len(self.shape) == 3:
                 ret[:, :, self.k._all_dims_active] = return_val
+            elif len(self.shape) == 4:
+                ret[:, :, :, self.k._all_dims_active] = return_val
             return ret
         return return_val
 
@@ -120,12 +122,12 @@ def _slice_gradients_XX(f):
         else:
             N, M = X.shape[0], X2.shape[0]
         if cov: # full covariance
-            with _Slice_wrap(self, X, X2, ret_shape=(N, M, X.shape[1], X.shape[1])) as s:
             #with _Slice_wrap(self, X, X2, ret_shape=None) as s:
+            with _Slice_wrap(self, X, X2, ret_shape=(N, M, X.shape[1], X.shape[1])) as s:
                 ret = s.handle_return_array(f(self, dL_dK, s.X, s.X2, cov))
         else: # diagonal covariance
-            with _Slice_wrap(self, X, X2, ret_shape=(N, M, X.shape[1])) as s:
             #with _Slice_wrap(self, X, X2, ret_shape=None) as s:
+            with _Slice_wrap(self, X, X2, ret_shape=(N, M, X.shape[1])) as s:
                 ret = s.handle_return_array(f(self, dL_dK, s.X, s.X2, cov))
         return ret
     return wrap
diff --git a/GPy/kern/src/static.py b/GPy/kern/src/static.py
index a56d1903..1745dc23 100644
--- a/GPy/kern/src/static.py
+++ b/GPy/kern/src/static.py
@@ -27,7 +27,10 @@ class Static(Kern):
     def gradients_XX(self, dL_dK, X, X2=None, cov=True):
         if X2 is None:
             X2 = X
-        return np.zeros((X.shape[0], X2.shape[0], X.shape[1]), dtype=np.float64)
+        if cov:
+            return np.zeros((X.shape[0], X2.shape[0], X.shape[1],X.shape[1]), dtype=np.float64)
+        else:
+            return np.zeros((X.shape[0], X2.shape[0], X.shape[1]), dtype=np.float64)
     def gradients_XX_diag(self, dL_dKdiag, X):
         return np.zeros(X.shape)
 
diff --git a/GPy/testing/kernel_tests.py b/GPy/testing/kernel_tests.py
index a8af50aa..262b7d45 100644
--- a/GPy/testing/kernel_tests.py
+++ b/GPy/testing/kernel_tests.py
@@ -108,8 +108,8 @@ class Kern_check_d2K_dXdX(Kern_check_model):
     """This class allows gradient checks for the secondderivative of a kernel with respect to X. """
     def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
         Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
-        #self.X = Param('X',X)
-        #self.link_parameter(self.X)
+        self.X = Param('X',X)
+        self.link_parameter(self.X)
 
     def log_likelihood(self):
         return np.sum(self.kernel.gradients_X(self.dL_dK,self.X, self.X2))
@@ -117,6 +117,7 @@ class Kern_check_d2K_dXdX(Kern_check_model):
     def parameters_changed(self):
         self.X.gradient[:] =  self.kernel.gradients_XX(self.dL_dK, self.X, self.X2)
 
+
 # class Kern_check_d2Kdiag_dXdX(Kern_check_model):
 #     """This class allows gradient checks for the secondderivative of a kernel diagonal with respect to X. """