Clean up errors and remove unused ignores] [batch:235/695] [shard:4/N]

Reviewed By: connernilsen

Differential Revision: D66311994

fbshipit-source-id: 1e9de0a727b946d73ec919c1cb99561f40205907

pearl/policy_learners/sequential_decision_making/actor_critic_base.py

@@ -358,8 +358,6 @@ def preprocess_batch(self, batch: TransitionBatch) -> TransitionBatch:
         # change reward to be the lambda_constraint weighted sum of reward and cost
         if hasattr(self.safety_module, "lambda_constraint"):
             batch.reward = (
-                # pyre-fixme[16]: Item `Tensor` of `Tensor | Module` has no
-                #  attribute `lambda_constraint`.
                 batch.reward
                 # pyre-fixme[16]: Item `Tensor` of `Tensor | Module` has no
                 #  attribute `lambda_constraint`.

pearl/utils/instantiations/spaces/box.py

@@ -51,11 +51,7 @@ def __init__(
             seed: Random seed used to initialize the random number generator of the
                 underlying Gymnasium `Box` space.
         """
-        # pyre-fixme[9]: low has type `Union[float, Tensor]`; used as `ndarray[Any,
-        #  Any]`.
         low = low.numpy(force=True) if isinstance(low, Tensor) else low
-        # pyre-fixme[9]: high has type `Union[float, Tensor]`; used as `ndarray[Any,
-        #  Any]`.
         high = high.numpy(force=True) if isinstance(high, Tensor) else high
         self._gym_space = Box(low=low, high=high, seed=seed)