remove debug comments

opacus/tests/privacy_engine_test.py

@@ -729,26 +729,26 @@ def batch_second_collate(batch):
     return data, labels
 
 
-# class PrivacyEngineTextTest(BasePrivacyEngineTest, unittest.TestCase):
-#     def setUp(self):
-#         super().setUp()
-#         self.BATCH_FIRST = False
-
-#     def _init_data(self):
-#         x = torch.randint(0, 100, (12, self.DATA_SIZE))
-#         y = torch.randint(0, 12, (self.DATA_SIZE,))
-#         ds = MockTextDataset(x, y)
-#         return DataLoader(
-#             ds,
-#             batch_size=self.BATCH_SIZE,
-#             collate_fn=batch_second_collate,
-#             drop_last=True,
-#         )
-
-#     def _init_model(
-#         self, private=False, state_dict=None, model=None, **privacy_engine_kwargs
-#     ):
-#         return SampleAttnNet()
+class PrivacyEngineTextTest(BasePrivacyEngineTest, unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        self.BATCH_FIRST = False
+
+    def _init_data(self):
+        x = torch.randint(0, 100, (12, self.DATA_SIZE))
+        y = torch.randint(0, 12, (self.DATA_SIZE,))
+        ds = MockTextDataset(x, y)
+        return DataLoader(
+            ds,
+            batch_size=self.BATCH_SIZE,
+            collate_fn=batch_second_collate,
+            drop_last=True,
+        )
+
+    def _init_model(
+        self, private=False, state_dict=None, model=None, **privacy_engine_kwargs
+    ):
+        return SampleAttnNet()
 
 
 class SampleTiedWeights(nn.Module):
@@ -777,15 +777,15 @@ def forward(self, x):
         return x
 
 
-# class PrivacyEngineTiedWeightsTest(BasePrivacyEngineTest, unittest.TestCase):
-#     def _init_data(self):
-#         ds = TensorDataset(
-#             torch.randint(low=0, high=100, size=(self.DATA_SIZE,)),
-#             torch.randint(low=0, high=100, size=(self.DATA_SIZE,)),
-#         )
-#         return DataLoader(ds, batch_size=self.BATCH_SIZE, drop_last=True)
+class PrivacyEngineTiedWeightsTest(BasePrivacyEngineTest, unittest.TestCase):
+    def _init_data(self):
+        ds = TensorDataset(
+            torch.randint(low=0, high=100, size=(self.DATA_SIZE,)),
+            torch.randint(low=0, high=100, size=(self.DATA_SIZE,)),
+        )
+        return DataLoader(ds, batch_size=self.BATCH_SIZE, drop_last=True)
 
-#     def _init_model(
-#         self, private=False, state_dict=None, model=None, **privacy_engine_kwargs
-#     ):
-#         return SampleTiedWeights(tie=False)
+    def _init_model(
+        self, private=False, state_dict=None, model=None, **privacy_engine_kwargs
+    ):
+        return SampleTiedWeights(tie=False)