remove comments

ml-agents/mlagents/trainers/torch/action_model.py

         for split_action in split_actions:
             action_list = [split_action[..., i] for i in range(split_action.shape[-1])]
             action_lists += action_list
-        log_probs, entropies, _ = ModelUtils.get_probs_and_entropy(action_lists, dists) #self._get_stats(actions, dists)
+        log_probs, entropies, _ = ModelUtils.get_probs_and_entropy(action_lists, dists)
         return log_probs, entropies
 
     def get_action_out(self, inputs: torch.Tensor, masks: torch.Tensor) -> torch.Tensor:
         action_outs : List[torch.Tensor] = []
         action_lists = self._sample_action(dists)
         for action_list, dist in zip(action_lists, dists):
-            action_out = action_list.unsqueeze(-1)#torch.stack(action_list, dim=-1) 
+            action_out = action_list.unsqueeze(-1)
-        log_probs, entropies, _ = ModelUtils.get_probs_and_entropy(action_lists, dists) #self._get_stats(actions, dists)self._get_stats(action_lists, dists)
+        log_probs, entropies, _ = ModelUtils.get_probs_and_entropy(action_lists, dists)
         action = torch.cat(action_outs, dim=1) 
         return (action, log_probs, entropies)