style: fix isort

omnisafe/adapter/crabs_adapter.py

@@ -74,13 +74,13 @@ def eval_policy(  # pylint: disable=too-many-locals
         """
         for _ in range(episode):
             ep_ret, ep_cost, ep_len = 0.0, 0.0, 0
-            obs, _ = self._eval_env.reset()
+            obs, _ = self._eval_env.reset()  # type: ignore
             obs = obs.to(self._device)
 
             done = False
             while not done:
                 act = agent.step(obs, deterministic=False)
-                obs, reward, cost, terminated, truncated, info = self._eval_env.step(act)
+                obs, reward, cost, terminated, truncated, info = self._eval_env.step(act)  # type: ignore
                 obs, reward, cost, terminated, truncated = (
                     torch.as_tensor(x, dtype=torch.float32, device=self._device)
                     for x in (obs, reward, cost, terminated, truncated)

omnisafe/common/control_barrier_function/crabs/models.py

@@ -101,11 +101,11 @@ def training_step(self, batch):
             self.log(f'model/{i}/training_loss', loss.item())
 
         opt = self.optimizers()
-        opt.zero_grad()  # type: ignore
+        opt.zero_grad()
 
-        self.manual_backward(total_loss)  # type: ignore
+        self.manual_backward(total_loss)
         nn.utils.clip_grad_norm_(self.parameters(), 10)
-        opt.step()  # type: ignore
+        opt.step()
 
     def validation_step(self, batch):
         """Validation step of the ensemble model.
@@ -291,10 +291,10 @@ def training_step(self, batch):
         self.log(f'{self.name}/training_loss', loss.item(), on_step=False, on_epoch=True)
 
         opt = self.optimizers()
-        opt.zero_grad()  # type: ignore
+        opt.zero_grad()
         self.manual_backward(loss, opt)
         nn.utils.clip_grad_norm_(self.parameters(), 10)
-        opt.step()  # type: ignore
+        opt.step()
 
         return {
             'loss': loss.item(),