code: def serving_obs_preprocessor(
      self, obs: np.ndarray
  ) -> rlt.ServingFeatureData:
      dense_val, id_list_val, id_score_list_val = self._split_state(obs)
      return rlt.ServingFeatureData(
          float_features_with_presence=(
              dense_val,
              torch.ones_like(dense_val, dtype=torch.uint8),
          ),
          id_list_features={
              ID_LIST_FEATURE_ID: (
                  torch.tensor([0], dtype=torch.long),
                  id_list_val + ID_LIST_OFFSET,
              )
          },
          id_score_list_features={
              ID_SCORE_LIST_FEATURE_ID: (
                  torch.tensor([0], dtype=torch.long),
                  torch.arange(self.num_arms, dtype=torch.long)
                  + ID_SCORE_LIST_OFFSET,
                  id_score_list_val,
              )
          },
      )
  
imports: import random
  import gym
  import numpy
  import reagent.core.types
  import torch
  from reagent.core.dataclasses import dataclass
  from reagent.core.parameters import NormalizationData, NormalizationKey
  from reagent.gym.envs.env_wrapper import EnvWrapper
  from reagent.gym.normalizers import only_continuous_normalizer
label_desc: Use image node_id 4 for calling the ChangingArms obj's underlying member method code with example usage: obj.serving_obs_preprocessor(obs) and returns: rlt