diff --git a/core/leras/ops/__init__.py b/core/leras/ops/__init__.py
index f34461f..500a22a 100644
--- a/core/leras/ops/__init__.py
+++ b/core/leras/ops/__init__.py
@@ -307,47 +307,6 @@ def dssim(img1,img2, max_val, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03
 
 nn.dssim = dssim
 
-
-# def ms_ssim(img1, img2, resolution, kernel_size=11, k1=0.01, k2=0.03, max_value=1.0,
-#             power_factors=(0.0448, 0.2856, 0.3001, 0.2363, 0.1333)):
-#
-#     # restrict mssim factors to those greater/equal to kernel size
-#     power_factors = [power_factors[i] for i in range(len(power_factors)) if resolution//(2**i) >= kernel_size]
-#
-#     # normalize power factors if reduced because of size
-#     if sum(power_factors) < 1.0:
-#         power_factors = [x/sum(power_factors) for x in power_factors]
-#
-#     img_dtype = img1.dtype
-#     if img_dtype != img2.dtype:
-#         raise ValueError("img1.dtype != img2.dtype")
-#
-#     if img_dtype != tf.float32:
-#         img1 = tf.cast(img1, tf.float32)
-#         img2 = tf.cast(img2, tf.float32)
-#
-#     # Transpose images from NCHW to NHWC
-#     img1_t = tf.transpose(img1, [0, 2, 3, 1])
-#     img2_t = tf.transpose(img2, [0, 2, 3, 1])
-#
-#     def assign_device(op):
-#         if op.type != 'ListDiff':
-#             return '/gpu:0'
-#         else:
-#             return '/cpu:0'
-#
-#     with tf.device(assign_device):
-#         ms_ssim_val = tf.image.ssim_multiscale(img1_t, img2_t, max_val=max_value, power_factors=power_factors,
-#                                                filter_size=kernel_size, k1=k1, k2=k2)
-#     ms_ssim_loss = (1.0 - ms_ssim_val) / 2.0
-#
-#     if img_dtype != tf.float32:
-#         ms_ssim_loss = tf.cast(ms_ssim_loss, img_dtype)
-#     return ms_ssim_loss
-#
-# nn.ms_ssim = ms_ssim
-
-
 def space_to_depth(x, size):
     if nn.data_format == "NHWC":
         # match NCHW version in order to switch data_format without problems
@@ -426,7 +385,7 @@ def total_variation_mse(images):
     """
     pixel_dif1 = images[:, 1:, :, :] - images[:, :-1, :, :]
     pixel_dif2 = images[:, :, 1:, :] - images[:, :, :-1, :]
-
+    
     tot_var = ( tf.reduce_sum(tf.square(pixel_dif1), axis=[1,2,3]) +
                 tf.reduce_sum(tf.square(pixel_dif2), axis=[1,2,3]) )
     return tot_var
@@ -441,4 +400,4 @@ def tf_suppress_lower_mean(t, eps=0.00001):
     q = tf.clip_by_value(q-t_mean_eps, 0, eps)
     q = q * (t/eps)
     return q
-"""
+"""
\ No newline at end of file