[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

lerobot/common/policies/sac/modeling_sac.py

@@ -198,7 +198,7 @@ class SACPolicy(
 
     def grasp_critic_forward(self, observations, use_target=False, observation_features=None):
         """Forward pass through a grasp critic network
-        
+
         Args:
             observations: Dictionary of observations
             use_target: If True, use target critics, otherwise use ensemble critics
@@ -254,7 +254,7 @@ class SACPolicy(
                 observation_features=observation_features,
                 next_observation_features=next_observation_features,
             )
-        
+
         if model == "grasp_critic":
             # Extract grasp_critic-specific components
             complementary_info: dict[str, Tensor] = batch["complementary_info"]
@@ -307,7 +307,7 @@ class SACPolicy(
                 param.data * self.config.critic_target_update_weight
                 + target_param.data * (1.0 - self.config.critic_target_update_weight)
             )
-    
+
     def update_temperature(self):
         self.temperature = self.log_alpha.exp().item()
 
@@ -369,8 +369,17 @@ class SACPolicy(
         ).sum()
         return critics_loss
 
-    def compute_loss_grasp_critic(self, observations, actions, rewards, next_observations, done, observation_features=None, next_observation_features=None, complementary_info=None):
-
+    def compute_loss_grasp_critic(
+        self,
+        observations,
+        actions,
+        rewards,
+        next_observations,
+        done,
+        observation_features=None,
+        next_observation_features=None,
+        complementary_info=None,
+    ):
         batch_size = rewards.shape[0]
         grasp_actions = torch.clip(actions[:, -1].long() + 1, 0, 2)  # Map [-1, 0, 1] -> [0, 1, 2]
 
@@ -632,9 +641,7 @@ class GraspCritic(nn.Module):
         self.parameters_to_optimize += list(self.output_layer.parameters())
 
     def forward(
-        self,
-        observations: torch.Tensor,
-        observation_features: torch.Tensor | None = None
+        self, observations: torch.Tensor, observation_features: torch.Tensor | None = None
     ) -> torch.Tensor:
         device = get_device_from_parameters(self)
         # Move each tensor in observations to device