code: def trainer_preprocessor(self, obs: torch.Tensor):
      batch_size = obs.shape[0]
      assert obs.shape == (batch_size, 3, self.num_arms), f"{obs.shape}"
      dense_val = obs[:, 0, :].view(batch_size, self.num_arms)
      # extract one-hot encoded values from id_list
      batch_indices, id_list_val = obs[:, 1, :].nonzero(as_tuple=True)
      offsets = []
      prev_batch_idx = -1
      for i, batch_idx in enumerate(batch_indices.tolist()):
          if batch_idx > prev_batch_idx:
              offsets.extend([i] * (batch_idx - prev_batch_idx))
              prev_batch_idx = batch_idx
          else:
              assert batch_idx == prev_batch_idx
      # handle the case of trailing empty batches
      # pyre-fixme[61]: `batch_idx` may not be initialized here.
      if batch_idx < batch_size - 1:
          # pyre-fixme[61]: `batch_idx` may not be initialized here.
          offsets.extend([i] * (batch_size - 1 - batch_idx))
      assert (
          len(offsets) == batch_size
      ), f"{len(offsets)} != {batch_size}."
      id_list_offsets = torch.tensor(offsets)
  
      # id_score_list is easier because not one-hot encoded
      id_score_list_offsets = torch.tensor(
          list(range(0, batch_size * self.num_arms, self.num_arms))
      )
      id_score_list_keys = torch.arange(self.num_arms).repeat(
          batch_size
      )
      id_score_list_vals = obs[:, 2, :].reshape(-1)
      return rlt.FeatureData(
          # dense value
          float_features=dense_val,
          # (offset, value)
          id_list_features_raw={
              "legal": (id_list_offsets, id_list_val)
          },
          # (offset, key, value)
          id_score_list_features_raw={
              "mu_changes": (
                  id_score_list_offsets,
                  id_score_list_keys,
                  id_score_list_vals,
              )
          },
      )
  
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 7 for calling the ChangingArms obj's underlying member method code with example usage: obj.trainer_preprocessor(obs) and returns: rlt