diff --git a/pyproject.toml b/pyproject.toml
index bf8b35e..8803757 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,6 +24,8 @@ version = { attr = "torchattack.__version__" }
 
 [tool.ruff]
 line-length = 88
+
+[tool.ruff.lint]
 select = ["E", "F", "I", "N", "B", "SIM"]
 ignore = ["E501"]
 
@@ -32,9 +34,9 @@ ignore = ["E501"]
 quote-style = "single"
 
 [tool.mypy]
-disallow_any_unimported = true
 no_implicit_optional = true
 check_untyped_defs = true
-ignore_missing_imports = true  # Used as torchvision does not ship type hints
+ignore_missing_imports = true # Used as torchvision does not ship type hints
+# disallow_any_unimported = true
 # disallow_untyped_defs = true
 # warn_return_any = true
diff --git a/src/torchattack/fia.py b/src/torchattack/fia.py
index 163f866..f645e3a 100644
--- a/src/torchattack/fia.py
+++ b/src/torchattack/fia.py
@@ -58,6 +58,10 @@ def __init__(
         self.targeted = targeted
         # self.lossfn = nn.CrossEntropyLoss()
 
+        # TODO: Targeted attack is not supported yet.
+        if self.targeted:
+            print('Targeted attack is not supported, using non-targeted variant.')
+
     def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
         """Perform FIA on a batch of images.
 
@@ -86,12 +90,12 @@ def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
             output_random = torch.softmax(output_random, dim=1)
             loss = 0
             for batch_i in range(x.shape[0]):
-                loss += output_random[batch_i][y[batch_i]]
+                loss += output_random[batch_i][y[batch_i]]  # type: ignore
             self.model.zero_grad()
-            loss.backward()
+            loss.backward()  # type: ignore
             agg_grad += self.mid_grad[0].detach()
         for batch_i in range(x.shape[0]):
-            agg_grad[batch_i] /= agg_grad[batch_i].norm(p=2)
+            agg_grad[batch_i] /= agg_grad[batch_i].norm(p=2)  # type: ignore
         h2.remove()
 
         # Perform FIA