diff --git a/GPy/_models/gradient_checker.py b/GPy/_models/gradient_checker.py
index 64b8b2fb..dfd0640f 100644
--- a/GPy/_models/gradient_checker.py
+++ b/GPy/_models/gradient_checker.py
@@ -28,38 +28,37 @@ class GradientChecker(Model):
         :param df: Gradient of function to check
         :param x0:
             Initial guess for inputs x (if it has a shape (a,b) this will be reflected in the parameter names).
-            Can be a list of arrays, if takes a list of arrays. This list will be passed
+            Can be a list of arrays, if f takes a list of arrays. This list will be passed
             to f and df in the same order as given here.
-            If only one argument, make sure not to pass a list!!!
-
+            If f takes only one argument, make sure not to pass a list for x0!!!
         :type x0: [array-like] | array-like | float | int
-        :param names:
+        :param list names:
             Names to print, when performing gradcheck. If a list was passed to x0
             a list of names with the same length is expected.
-        :param args: Arguments passed as f(x, *args, **kwargs) and df(x, *args, **kwargs)
+        :param args kwargs: Arguments passed as f(x, *args, **kwargs) and df(x, *args, **kwargs)
 
         Examples:
         ---------
-            from GPy.models import GradientChecker
-            N, M, Q = 10, 5, 3
+        from GPy.models import GradientChecker
+        N, M, Q = 10, 5, 3
 
-            Sinusoid:
+        Sinusoid:
 
-                X = numpy.random.rand(N, Q)
-                grad = GradientChecker(numpy.sin,numpy.cos,X,'x')
-                grad.checkgrad(verbose=1)
+            X = numpy.random.rand(N, Q)
+            grad = GradientChecker(numpy.sin,numpy.cos,X,'sin_in')
+            grad.checkgrad(verbose=1)
 
-            Using GPy:
+        Using GPy:
 
-                X, Z = numpy.random.randn(N,Q), numpy.random.randn(M,Q)
-                kern = GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True)
-                grad = GradientChecker(kern.K,
-                                       lambda x: 2*kern.dK_dX(numpy.ones((1,1)), x),
-                                       x0 = X.copy(),
-                                       names='X')
-                grad.checkgrad(verbose=1)
-                grad.randomize()
-                grad.checkgrad(verbose=1)
+            X, Z = numpy.random.randn(N,Q), numpy.random.randn(M,Q)
+            kern = GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True)
+            grad = GradientChecker(kern.K,
+                                   lambda x: kern.dK_dX(numpy.ones((1,1)), x),
+                                   x0 = X.copy(),
+                                   names=['X_input'])
+            grad.checkgrad(verbose=1)
+            grad.randomize()
+            grad.checkgrad(verbose=1)
         """
         Model.__init__(self)
         if isinstance(x0, (list, tuple)) and names is None:
diff --git a/GPy/kern/kern.py b/GPy/kern/kern.py
index f51c3c13..2c56f47a 100644
--- a/GPy/kern/kern.py
+++ b/GPy/kern/kern.py
@@ -737,15 +737,16 @@ class kern(Parameterized):
         else:
             raise NotImplementedError, "Cannot plot a kernel with more than two input dimensions"
 
-from GPy.core.model import Model
-
+from ..core.model import Model
 class Kern_check_model(Model):
     """This is a dummy model class used as a base class for checking that the gradients of a given kernel are implemented correctly. It enables checkgradient() to be called independently on a kernel."""
     def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
         num_samples = 20
         num_samples2 = 10
         if kernel==None:
+            import GPy
             kernel = GPy.kern.rbf(1)
+            del GPy
         if X==None:
             X = np.random.normal(size=(num_samples, kernel.input_dim))
         if dL_dK==None:
@@ -760,7 +761,7 @@ class Kern_check_model(Model):
         self.dL_dK = dL_dK
         #self.constrained_indices=[]
         #self.constraints=[]
-        Model.__init__(self)
+        super(Kern_check_model, self).__init__()
 
     def is_positive_definite(self):
         v = np.linalg.eig(self.kernel.K(self.X))[0]
@@ -863,7 +864,6 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False, X_positive=
         if output_ind is not None:
             assert(output_ind<kern.input_dim)
             X[:, output_ind] = np.random.randint(low=0,high=kern.parts[0].output_dim, size=X.shape[0])
-            import ipdb; ipdb.set_trace()
     if X2==None:
         X2 = np.random.randn(20, kern.input_dim)
         if X_positive:
@@ -964,3 +964,4 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False, X_positive=
         return False
 
     return pass_checks
+del Model
\ No newline at end of file