refactor: add 2 spaces in tp7 between function

tp7.py

@@ -25,6 +25,7 @@ class Actor(nn.Module):
         std = torch.exp(log_std)
         return mu, std
 
+
 class Critic(nn.Module):
     def __init__(self, state_dim):
         super().__init__()
@@ -53,6 +54,7 @@ def compute_gae(rewards, values, gamma, lam, next_value):
     advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
     return returns, advantages
 
+
 # ——— Entraînement ———
 def train_and_save():
     env = gym.make("Pusher-v5")
@@ -151,7 +153,7 @@ def train_and_save():
 
     torch.save(actor.state_dict(), "a2c_pusher.pth")
 
-# ——— Démonstration ———
+
 def show(weights_path="a2c_pusher.pth"):
     env = gym.make("Pusher-v5", render_mode="human")
     actor = Actor(env.observation_space.shape[0], env.action_space.shape[0])

tp7_gpt_exemple.py

@@ -41,6 +41,7 @@ class A2CAgent:
         self.model = ActorCritic(state_dim, action_dim).to(self.device)
         self.optimizer = optim.Adam(self.model.parameters(), lr=lr)
 
+
     def select_action(self, state):
         state = torch.FloatTensor(state).to(self.device)
         dist, _ = self.model(state)
@@ -48,6 +49,7 @@ class A2CAgent:
         log_prob = dist.log_prob(action).sum(dim=-1)
         return action.cpu().numpy(), log_prob
 
+
     def compute_returns(self, rewards, masks, next_value):
         R = next_value
         returns = []
@@ -56,6 +58,7 @@ class A2CAgent:
             returns.insert(0, R)
         return returns
 
+
     def update(self, trajectory, next_state):
         states = torch.FloatTensor([t[0] for t in trajectory]).to(self.device)
         actions = torch.FloatTensor([t[1] for t in trajectory]).to(self.device)
@@ -80,6 +83,7 @@ class A2CAgent:
         loss.backward()
         self.optimizer.step()
 
+
     def train(self, max_steps=2000, update_every=5):
         state, _ = self.env.reset()
         trajectory = []