Try futures in Optimizer

ml-agents/mlagents/trainers/sac/optimizer_torch.py

             indexed by name. If none, don't update the reward signals.
         :return: Output from update process.
         """
-        t0 = time.time()
+        # t0 = time.time()
         rewards = {}
         for name in self.reward_signals:
             rewards[name] = ModelUtils.list_to_tensor(batch[f"{name}_rewards"])
         self.target_network.network_body.copy_normalization(
             self.policy.actor_critic.network_body
         )
-        t1 = time.time()
+        # t1 = time.time()
         (
             sampled_actions,
             log_probs,
             seq_len=self.policy.sequence_length,
             all_log_probs=not self.policy.use_continuous_act,
         )
-        t2 = time.time()
+        # t2 = time.time()
         if self.policy.use_continuous_act:
             squeezed_actions = actions.squeeze(-1)
             # q1p_out, q2p_out = self.value_network(
             )
             q1_stream = self._condense_q_streams(q1_out, actions)
             q2_stream = self._condense_q_streams(q2_out, actions)
-        t3 = time.time()
+        # t3 = time.time()
         with torch.no_grad():
             target_values, _ = self.target_network(
                 next_vec_obs,
         masks = ModelUtils.list_to_tensor(batch["masks"], dtype=torch.bool)
         use_discrete = not self.policy.use_continuous_act
         dones = ModelUtils.list_to_tensor(batch["done"])
-        t4 = time.time()
+        # t4 = time.time()
         q1_loss, q2_loss = self.sac_q_loss(
             q1_stream, q2_stream, target_values, dones, rewards, masks
         )
         entropy_loss = self.sac_entropy_loss(log_probs, masks, use_discrete)
 
         total_value_loss = q1_loss + q2_loss + value_loss
-        t5 = time.time()
+        # t5 = time.time()
-        t6 = time.time()
+        # t6 = time.time()
-        t7 = time.time()
+        # t7 = time.time()
-        t8 = time.time()
+        # t8 = time.time()
         # Update target network
         self.soft_update(self.policy.actor_critic.critic, self.target_network, self.tau)
         update_stats = {
             "Policy/Entropy Coeff": torch.mean(torch.exp(self._log_ent_coef)).item(),
             "Policy/Learning Rate": decay_lr,
         }
-        t9 = time.time()
-        print(
-            t9 - t8,
-            t8 - t7,
-            t7 - t6,
-            t6 - t5,
-            t5 - t4,
-            t4 - t3,
-            t3 - t2,
-            t2 - t1,
-            t1 - t0,
-        )
+        # t9 = time.time()
+        # print(
+        #     t9 - t8,
+        #     t8 - t7,
+        #     t7 - t6,
+        #     t6 - t5,
+        #     t5 - t4,
+        #     t4 - t3,
+        #     t3 - t2,
+        #     t2 - t1,
+        #     t1 - t0,
+        # )
         return update_stats
 
     def update_reward_signals(