docstr: For generate data
docstr_tok: ["For","generate","data"]
code: def split_state_transform(self, elem: torch.Tensor):
      """For generate data"""
      dense_val, id_list_val, id_score_list_val = self._split_state(
          elem.numpy()
      )
      return (
          {i: s.item() for i, s in enumerate(dense_val.view(-1))},
          {ID_LIST_FEATURE_ID: (id_list_val + ID_LIST_OFFSET).tolist()},
          {
              ID_SCORE_LIST_FEATURE_ID: {
                  i + ID_SCORE_LIST_OFFSET: s.item()
                  for i, s in enumerate(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 5 for calling the ChangingArms obj's underlying member method code with example usage: obj.split_state_transform(elem) and returns: unknown,  dict,  dict