add rpc

ml-agents-envs/mlagents_envs/rpc_utils.py

             action_mask = np.split(action_mask, indices, axis=1)
     return BatchedStepResult(obs_list, rewards, done, max_step, agent_id, action_mask)
 
+
-def proto_from_batched_step_result(batched_step_result: BatchedStepResult) -> AgentInfoProto:
+def proto_from_batched_step_result(
+    batched_step_result: BatchedStepResult
+) -> AgentInfoProto:
     reward = batched_step_result.reward
     done = batched_step_result.done
     max_step_reached = batched_step_result.max_step
-    return AgentInfoProto(reward=reward, done=done, id=agent_id, max_step_reached=max_step_reached, action_mask=action_mask, observations=observations)
+    return AgentInfoProto(
+        reward=reward,
+        done=done,
+        id=agent_id,
+        max_step_reached=max_step_reached,
+        action_mask=action_mask,
+        observations=observations,
+    )
+
 
 def _generate_split_indices(dims):
     if len(dims) <= 1: