diff --git a/python/mlx/nn/layers/normalization.py b/python/mlx/nn/layers/normalization.py
index 92847e423..4f7ad7b60 100644
--- a/python/mlx/nn/layers/normalization.py
+++ b/python/mlx/nn/layers/normalization.py
@@ -283,39 +283,3 @@ class BatchNorm1d(Module):
             means, var = self.running_mean, self.running_var
         x = (x - means) * mx.rsqrt(var + self.eps)
         return (self.weight * x + self.bias) if "weight" in self else x
-
-
-# import unittest
-# import numpy as np
-
-# class TestBatchNorm1d(unittest.TestCase):
-#     def setUp(self):
-#         self.bn = BatchNorm1d(10)
-
-#     def test_forward(self):
-#         x = mx.random.uniform(shape=(20, 10))
-#         y = self.bn(x)
-#         expedted =
-#         self.assertEqual(y.shape, x.shape)
-
-#     def test_running_stats(self):
-#         x = mx.random.uniform(shape=(20, 10))
-#         self.bn(x)
-#         self.assertNotEqual(mx.sum(self.bn.running_mean), 0)
-#         self.assertNotEqual(mx.sum(self.bn.running_var), 10)
-
-#     def test_eval_mode(self):
-#         x = mx.random.uniform(shape=(20, 10))
-#         self.bn(x)
-#         self.bn.training = False
-#         y = self.bn(x)
-#         self.assertEqual(y.shape, x.shape)
-
-#     def test_no_affine(self):
-#         bn = BatchNorm1d(10, affine=False)
-#         x = mx.random.uniform(shape=(20, 10))
-#         y = bn(x)
-#         self.assertEqual(y.shape, x.shape)
-
-# if __name__ == '__main__':
-#     unittest.main()