clean up types/comments

ml-agents/mlagents/trainers/torch/components/bc/module.py

-from typing import Dict, Any
+from typing import Dict
 import numpy as np
 import torch
 
         self.has_updated = False
         self.use_recurrent = self.policy.use_recurrent
         self.samples_per_update = settings.samples_per_update
-        # self.out_dict = {
-        #    "loss": self.model.loss,
-        #    "update": self.model.update_batch,
-        #    "learning_rate": self.model.annealed_learning_rate,
-        # }
-    def update(self) -> Dict[str, Any]:
+    def update(self) -> Dict[str, np.ndarray]:
         """
         Updates model using buffer.
         :param max_batches: The maximum number of batches to use per update.
         return bc_loss
 
     def _update_batch(
-        self, mini_batch_demo: Dict[str, Any], n_sequences: int
-    ) -> Dict[str, Any]:
+        self, mini_batch_demo: Dict[str, np.ndarray], n_sequences: int
+    ) -> Dict[str, float]:
         """
         Helper function for update_batch.
         """
         else:
             vis_obs = []
 
-        selected_actions, all_log_probs, entropies, values, memories = self.policy.sample_actions(
+        selected_actions, all_log_probs, _, _, _ = self.policy.sample_actions(
             vec_obs,
             vis_obs,
             masks=act_masks,