diff --git a/examples/ode/damping.py b/examples/ode/damping.py
index 61bcc7f..e714dbe 100755
--- a/examples/ode/damping.py
+++ b/examples/ode/damping.py
@@ -35,14 +35,11 @@
 sys.path.append("../..")
 from pyoptmat import ode, utility, neuralode
 
-if torch.cuda.is_available():
-    dev = "cuda:0"
-else:
-    dev = "cpu"
+dev = "cpu"
 device = torch.device(dev)
 
 # I'm assuming single precision will be fine for this but for now use doubles
-torch.set_default_tensor_type(torch.DoubleTensor)
+torch.set_default_dtype(torch.float64)
 
 
 class MassDamperSpring(torch.nn.Module):
diff --git a/examples/structural-material-models/kocks-mecking-differentiable.py b/examples/structural-material-models/kocks-mecking-differentiable.py
index b0d15f4..1712b3f 100755
--- a/examples/structural-material-models/kocks-mecking-differentiable.py
+++ b/examples/structural-material-models/kocks-mecking-differentiable.py
@@ -48,7 +48,6 @@ def calculate_yield(strain, stress, offset=0.2 / 100.0):
     mu = temperature.PolynomialScaling(
         [-2.60610204e-05, 3.61911162e-02, -4.23765368e01, 8.44212545e04]
     )
-    g0 = torch.tensor(0.771)
     k = torch.tensor(1.38064e-20)
     b = torch.tensor(2.019e-7)
     eps0 = torch.tensor(1.0e6)
@@ -81,7 +80,7 @@ def calculate_yield(strain, stress, offset=0.2 / 100.0):
 
     sf = torch.tensor(50.0)
     flowrule = flowrules.SoftKocksMeckingRegimeFlowRule(
-        ri_flowrule, rd_flowrule, g0, mu, b, eps0, k, sf
+        ri_flowrule, rd_flowrule, A, B, C, mu, b, eps0, k, sf
     )
 
     model = models.InelasticModel(E, flowrule)
diff --git a/pyoptmat/flowrules.py b/pyoptmat/flowrules.py
index cd873c9..a422325 100644
--- a/pyoptmat/flowrules.py
+++ b/pyoptmat/flowrules.py
@@ -517,9 +517,7 @@ def g0(self):
         """
         The intercept value
         """
-        return (self.C_scale(self.C) - self.B_scale(self.B)) / self.A_scale(
-            self.A
-        )
+        return (self.C_scale(self.C) - self.B_scale(self.B)) / self.A_scale(self.A)
 
     def df_e(self, T, e):
         """