diff --git a/src/python/detectors/pytorch_miss_call_to_zero_grad/pytorch_miss_call_to_zero_grad.py b/src/python/detectors/pytorch_miss_call_to_zero_grad/pytorch_miss_call_to_zero_grad.py
index e96e0a6..e6e19fd 100644
--- a/src/python/detectors/pytorch_miss_call_to_zero_grad/pytorch_miss_call_to_zero_grad.py
+++ b/src/python/detectors/pytorch_miss_call_to_zero_grad/pytorch_miss_call_to_zero_grad.py
@@ -18,22 +18,6 @@ def pytorch_miss_call_to_zero_grad_noncompliant(
         # zero before doing a backward pass.
         loss.backward()
         optimizer.step()
-
-        avg_loss += loss.item()
-        # train_error += torch.sum((output > 0) != label)
-        true_pos += torch.sum((output >= 0).float() * label)
-        false_pos += torch.sum((output >= 0).float() * (1.0 - label))
-        true_neg += torch.sum((output < 0).float() * (1.0 - label))
-        false_neg += torch.sum((output < 0).float() * label)
-
-        print(f'\rEpoch {i_epoch},\
-        Training {i_batch+1:3d}/{len(dataloader):3d} batch, '
-              f'loss {loss.item():0.6f}    ', end='')
-
-    avg_loss /= len(dataloader)
-    tpr = float(true_pos) / float(true_pos + false_neg)
-    fpr = float(false_pos) / float(false_pos + true_neg)
-    return avg_loss, tpr, fpr
 # {/fact}
 
 
@@ -54,20 +38,4 @@ def pytorch_miss_call_to_zero_grad_compliant(
         optimizer.zero_grad()
         loss.backward()
         optimizer.step()
-
-        avg_loss += loss.item()
-        # train_error += torch.sum((output > 0) != label)
-        true_pos += torch.sum((output >= 0).float() * label)
-        false_pos += torch.sum((output >= 0).float() * (1.0 - label))
-        true_neg += torch.sum((output < 0).float() * (1.0 - label))
-        false_neg += torch.sum((output < 0).float() * label)
-
-        print(f'\rEpoch {i_epoch},\
-        Training {i_batch+1:3d}/{len(dataloader):3d} batch, '
-              f'loss {loss.item():0.6f}    ', end='')
-
-    avg_loss /= len(dataloader)
-    tpr = float(true_pos) / float(true_pos + false_neg)
-    fpr = float(false_pos) / float(false_pos + true_neg)
-    return avg_loss, tpr, fpr
 # {/fact}