ignoring commit checks but write to csv

ml-agents/mlagents/trainers/ppo/optimizer.py

 from mlagents.trainers.optimizer.tf_optimizer import TFOptimizer
 from mlagents.trainers.buffer import AgentBuffer
 
+from mlagents.trainers.stats import StatsSummary
 
 class PPOOptimizer(TFOptimizer):
     def __init__(self, policy: TFPolicy, trainer_params: Dict[str, Any]):
         """
         # Create the graph here to give more granular control of the TF graph to the Optimizer.
         policy.create_tf_graph()
-
         with policy.graph.as_default():
             with tf.variable_scope("optimizer/"):
                 super().__init__(policy, trainer_params)
             update_stats[stat_name] = update_vals[update_name]
         return update_stats
 
-    def compute_input_sensitivity(self, batch: AgentBuffer, num_sequences: int) -> None:
+    def compute_input_sensitivity(self, batch: AgentBuffer, num_sequences: int) -> Dict[int, float]:
+        out = dict((obs,StatsSummary(grad, 0.0, 0.0)) for obs, grad in enumerate(sens))
+        return out
 
     def _construct_feed_dict(
         self, mini_batch: AgentBuffer, num_sequences: int

ml-agents/mlagents/trainers/ppo/trainer.py

 from mlagents.trainers.exception import UnityTrainerException
 from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
 
+from mlagents.trainers.stats import CSVWriter
 
 logger = get_logger(__name__)
 
         self.load = load
         self.seed = seed
         self.policy: NNPolicy = None  # type: ignore
+        self.csv_writer = CSVWriter("sensitivity")
 
     def _check_param_keys(self):
         super()._check_param_keys()
         )
         num_epoch = self.trainer_parameters["num_epoch"]
         batch_update_stats = defaultdict(list)
-        self.optimizer.compute_input_sensitivity(
+        sensitivities = self.optimizer.compute_input_sensitivity(
+        self.csv_writer.write_stats("sensitivity", sensitivities, self.step)
+        
         for _ in range(num_epoch):
             self.update_buffer.shuffle(sequence_length=self.policy.sequence_length)
             buffer = self.update_buffer