Fix SAC interrupted condition and typing

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

 from mlagents.trainers.trainer.rl_trainer import RLTrainer
 from mlagents.trainers.policy.torch_policy import TorchPolicy
 from mlagents.trainers.sac.optimizer_torch import TorchSACOptimizer
-from mlagents.trainers.trajectory import Trajectory, SplitObservations
+from mlagents.trainers.trajectory import Trajectory
 from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
 from mlagents.trainers.settings import TrainerSettings, SACSettings, FrameworkType
 from mlagents.trainers.torch.components.reward_providers import BaseRewardProvider
         # Bootstrap using the last step rather than the bootstrap step if max step is reached.
         # Set last element to duplicate obs and remove dones.
         if last_step.interrupted:
-            vec_vis_obs = SplitObservations.from_observations(last_step.obs)
-            for i, obs in enumerate(vec_vis_obs.visual_observations):
-                agent_buffer_trajectory["next_visual_obs%d" % i][-1] = obs
-            if vec_vis_obs.vector_observations.size > 1:
-                agent_buffer_trajectory["next_vector_in"][
-                    -1
-                ] = vec_vis_obs.vector_observations
+            agent_buffer_trajectory["next_obs"] = last_step.obs
             agent_buffer_trajectory["done"][-1] = False
 
         # Append to update buffer

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

         return inputs
 
     def copy_normalization(self, other_input: "InputProcessor") -> None:
-        if self.normalizer is not None and other_input.normalizer is not None:
-            self.normalizer.copy_from(other_input.normalizer)
+        if isinstance(other_input, VectorInput):
+            if self.normalizer is not None and other_input.normalizer is not None:
+                self.normalizer.copy_from(other_input.normalizer)
 
     def update_normalization(self, inputs: torch.Tensor) -> None:
         if self.normalizer is not None: