diff --git a/norch/__pycache__/tensor.cpython-38.pyc b/norch/__pycache__/tensor.cpython-38.pyc
index 939d371..72d3beb 100644
Binary files a/norch/__pycache__/tensor.cpython-38.pyc and b/norch/__pycache__/tensor.cpython-38.pyc differ
diff --git a/norch/autograd/__pycache__/functions.cpython-38.pyc b/norch/autograd/__pycache__/functions.cpython-38.pyc
index 53f8f0f..a7d3c51 100644
Binary files a/norch/autograd/__pycache__/functions.cpython-38.pyc and b/norch/autograd/__pycache__/functions.cpython-38.pyc differ
diff --git a/norch/autograd/functions.py b/norch/autograd/functions.py
index d0aed64..30e452f 100644
--- a/norch/autograd/functions.py
+++ b/norch/autograd/functions.py
@@ -15,6 +15,7 @@ class AddBroadcastedBackward:
         x, y = self.input
         grad_x = self._reshape_gradient(gradient, x.shape)
         grad_y = self._reshape_gradient(gradient, y.shape)
+
         return [grad_x, grad_y]
     
     def _reshape_gradient(self, gradient, shape):
diff --git a/tests/test_autograd.py b/tests/test_autograd.py
index 8cd105e..1db0569 100644
--- a/tests/test_autograd.py
+++ b/tests/test_autograd.py
@@ -80,12 +80,17 @@ class TestTensorAutograd(unittest.TestCase):
         self.assertTrue(utils.compare_torch(norch_tensor2_grad, torch_tensor2_grad))
 
         ## reversed order broadcasting
+        norch_tensor1 = norch.Tensor([[[1., 2, 3], [4, 5, 6]]], requires_grad=True).to(self.device)  # Shape (1, 2, 3)
+        norch_tensor2 = norch.Tensor([1.5, -1, 0], requires_grad=True).to(self.device)  # Shape (3)
 
         norch_result = (norch_tensor2 + norch_tensor1).sum()
         norch_result.backward()
         norch_tensor1_grad = utils.to_torch(norch_tensor1.grad).to(self.device)
         norch_tensor2_grad = utils.to_torch(norch_tensor2.grad).to(self.device)
 
+        torch_tensor1 = torch.tensor([[[1., 2, 3], [4, 5, 6]]], requires_grad=True).to(self.device)  # Shape (1, 2, 3)
+        torch_tensor2 = torch.tensor([1.5, -1, 0], requires_grad=True).to(self.device)  # Shape (3)
+
         torch_result = (torch_tensor2 + torch_tensor1).sum()
         torch_result.backward()
         torch_tensor1_grad = torch_tensor1.grad
@@ -137,6 +142,8 @@ class TestTensorAutograd(unittest.TestCase):
         self.assertTrue(utils.compare_torch(norch_tensor2_grad, torch_tensor2_grad))
 
         # reversed order broadcasting
+        norch_tensor1 = norch.Tensor([[[1., 2, 3], [4, 5, 6]]], requires_grad=True).to(self.device)  # Shape (1, 2, 3)
+        norch_tensor2 = norch.Tensor([1.5, -1, 0], requires_grad=True).to(self.device)  # Shape (3)
 
         norch_result = (norch_tensor2 - norch_tensor1).sum()
         norch_result.backward()
@@ -145,6 +152,7 @@ class TestTensorAutograd(unittest.TestCase):
 
         torch_tensor1 = torch.tensor([[[1., 2, 3], [4, 5, 6]]], requires_grad=True).to(self.device)  # Shape (1, 2, 3)
         torch_tensor2 = torch.tensor([1.5, -1, 0], requires_grad=True).to(self.device)  # Shape (3)
+        
         torch_result = (torch_tensor2 - torch_tensor1).sum()
         torch_result.backward()
         torch_tensor1_grad = torch_tensor1.grad