diff --git a/captum/testing/helpers/classification_models.py b/captum/testing/helpers/classification_models.py
index 298cd2a29..a0db96901 100644
--- a/captum/testing/helpers/classification_models.py
+++ b/captum/testing/helpers/classification_models.py
@@ -12,23 +12,17 @@ class SigmoidModel(nn.Module):
         -pytorch-and-make-your-life-simpler-ec5367895199
     """
 
-    # pyre-fixme[2]: Parameter must be annotated.
-    def __init__(self, num_in, num_hidden, num_out) -> None:
+    def __init__(self, num_in: int, num_hidden: int, num_out: int) -> None:
         super().__init__()
-        # pyre-fixme[4]: Attribute must be annotated.
         self.num_in = num_in
-        # pyre-fixme[4]: Attribute must be annotated.
         self.num_hidden = num_hidden
-        # pyre-fixme[4]: Attribute must be annotated.
         self.num_out = num_out
         self.lin1 = nn.Linear(num_in, num_hidden)
         self.lin2 = nn.Linear(num_hidden, num_out)
         self.relu1 = nn.ReLU()
         self.sigmoid = nn.Sigmoid()
 
-    # pyre-fixme[3]: Return type must be annotated.
-    # pyre-fixme[2]: Parameter must be annotated.
-    def forward(self, input):
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
         lin1 = self.lin1(input)
         lin2 = self.lin2(self.relu1(lin1))
         return self.sigmoid(lin2)
@@ -40,14 +34,12 @@ class SoftmaxModel(nn.Module):
     https://adventuresinmachinelearning.com/pytorch-tutorial-deep-learning/
     """
 
-    # pyre-fixme[2]: Parameter must be annotated.
-    def __init__(self, num_in, num_hidden, num_out, inplace: bool = False) -> None:
+    def __init__(
+        self, num_in: int, num_hidden: int, num_out: int, inplace: bool = False
+    ) -> None:
         super().__init__()
-        # pyre-fixme[4]: Attribute must be annotated.
         self.num_in = num_in
-        # pyre-fixme[4]: Attribute must be annotated.
         self.num_hidden = num_hidden
-        # pyre-fixme[4]: Attribute must be annotated.
         self.num_out = num_out
         self.lin1 = nn.Linear(num_in, num_hidden)
         self.lin2 = nn.Linear(num_hidden, num_hidden)
@@ -56,9 +48,7 @@ def __init__(self, num_in, num_hidden, num_out, inplace: bool = False) -> None:
         self.relu2 = nn.ReLU(inplace=inplace)
         self.softmax = nn.Softmax(dim=1)
 
-    # pyre-fixme[3]: Return type must be annotated.
-    # pyre-fixme[2]: Parameter must be annotated.
-    def forward(self, input):
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
         lin1 = self.relu1(self.lin1(input))
         lin2 = self.relu2(self.lin2(lin1))
         lin3 = self.lin3(lin2)
@@ -72,14 +62,10 @@ class SigmoidDeepLiftModel(nn.Module):
         -pytorch-and-make-your-life-simpler-ec5367895199
     """
 
-    # pyre-fixme[2]: Parameter must be annotated.
-    def __init__(self, num_in, num_hidden, num_out) -> None:
+    def __init__(self, num_in: int, num_hidden: int, num_out: int) -> None:
         super().__init__()
-        # pyre-fixme[4]: Attribute must be annotated.
         self.num_in = num_in
-        # pyre-fixme[4]: Attribute must be annotated.
         self.num_hidden = num_hidden
-        # pyre-fixme[4]: Attribute must be annotated.
         self.num_out = num_out
         self.lin1 = nn.Linear(num_in, num_hidden, bias=False)
         self.lin2 = nn.Linear(num_hidden, num_out, bias=False)
@@ -88,9 +74,7 @@ def __init__(self, num_in, num_hidden, num_out) -> None:
         self.relu1 = nn.ReLU()
         self.sigmoid = nn.Sigmoid()
 
-    # pyre-fixme[3]: Return type must be annotated.
-    # pyre-fixme[2]: Parameter must be annotated.
-    def forward(self, input):
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
         lin1 = self.lin1(input)
         lin2 = self.lin2(self.relu1(lin1))
         return self.sigmoid(lin2)
@@ -102,14 +86,10 @@ class SoftmaxDeepLiftModel(nn.Module):
     https://adventuresinmachinelearning.com/pytorch-tutorial-deep-learning/
     """
 
-    # pyre-fixme[2]: Parameter must be annotated.
-    def __init__(self, num_in, num_hidden, num_out) -> None:
+    def __init__(self, num_in: int, num_hidden: int, num_out: int) -> None:
         super().__init__()
-        # pyre-fixme[4]: Attribute must be annotated.
         self.num_in = num_in
-        # pyre-fixme[4]: Attribute must be annotated.
         self.num_hidden = num_hidden
-        # pyre-fixme[4]: Attribute must be annotated.
         self.num_out = num_out
         self.lin1 = nn.Linear(num_in, num_hidden)
         self.lin2 = nn.Linear(num_hidden, num_hidden)
@@ -121,9 +101,7 @@ def __init__(self, num_in, num_hidden, num_out) -> None:
         self.relu2 = nn.ReLU()
         self.softmax = nn.Softmax(dim=1)
 
-    # pyre-fixme[3]: Return type must be annotated.
-    # pyre-fixme[2]: Parameter must be annotated.
-    def forward(self, input):
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
         lin1 = self.relu1(self.lin1(input))
         lin2 = self.relu2(self.lin2(lin1))
         lin3 = self.lin3(lin2)