diff --git a/tests/test_quantization/test_utils/test_helpers.py b/tests/test_quantization/test_utils/test_helpers.py
index b07d5e36..d89a9c7f 100644
--- a/tests/test_quantization/test_utils/test_helpers.py
+++ b/tests/test_quantization/test_utils/test_helpers.py
@@ -1,38 +1,60 @@
-import torch
-import pytest
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
-from compressed_tensors.quantization.utils import calculate_qparams
+import pytest
+import torch
 from compressed_tensors.quantization import QuantizationArgs, QuantizationStrategy
+from compressed_tensors.quantization.utils import calculate_qparams
 
 
-_IN_DIMS = 5
-_OUT_DIMS = 14
-_GROUP_SIZE = 2
-
 @pytest.mark.parametrize(
     "dim,keepdims,strategy,exp_shape",
     [
-        (tuple(), False, QuantizationStrategy.TENSOR, torch.Size([1,])),
-        (0, True, QuantizationStrategy.CHANNEL, torch.Size([_OUT_DIMS, 1])),
-        (tuple(), True, QuantizationStrategy.GROUP, torch.Size([_OUT_DIMS // _GROUP_SIZE , 1])),
-        (tuple(), False, QuantizationStrategy.BLOCK, torch.Size([1, ])),
+        (
+            tuple(),
+            False,
+            QuantizationStrategy.TENSOR,
+            torch.Size(
+                [
+                    1,
+                ]
+            ),
+        ),
+        (tuple(), True, QuantizationStrategy.CHANNEL, torch.Size([1, 1])),
+        (tuple(), True, QuantizationStrategy.GROUP, torch.Size([1, 1])),
+        (
+            tuple(),
+            False,
+            QuantizationStrategy.BLOCK,
+            torch.Size(
+                [
+                    1,
+                ]
+            ),
+        ),
         (tuple(), True, QuantizationStrategy.TOKEN, torch.Size([1, 1])),
     ],
 )
 def test_calculate_qparams(dim, keepdims, strategy, exp_shape):
-    value = torch.randn(_OUT_DIMS, _IN_DIMS)
+    value = torch.randn(14, 5)
     min_val = torch.amin(value, dim=dim, keepdims=keepdims)
     max_val = torch.amax(value, dim=dim, keepdims=keepdims)
 
     if strategy == QuantizationStrategy.GROUP:
-        args = QuantizationArgs(strategy=strategy, group_size=_GROUP_SIZE)
-        scale, zp = calculate_qparams(min_val, max_val, args)
-        assert scale.shape == exp_shape
-        assert zp.shape == exp_shape
-        
+        args = QuantizationArgs(strategy=strategy, group_size=2)
     else:
         args = QuantizationArgs(strategy=strategy)
-
         scale, zp = calculate_qparams(min_val, max_val, args)
         assert scale.shape == exp_shape
         assert zp.shape == exp_shape