code: def _postprocessing(
      self, run_states: List[RunState]
  ) -> List[List[np.ndarray]]:
      results = []
      for batch_state in run_states:
          batch_result = batch_state.run_results
          batch_pooled_features = batch_result[0]
          batch_seq_features = batch_result[1]
          for i in range(len(batch_pooled_features)):
              results.append(
                  [batch_pooled_features[i], batch_seq_features[i]]
              )
      return results
  
file_path: PaddleHub/paddlehub/compat/task/transformer_emb_task.py
imports: from typing import Generic, List
  import paddle
  import numpy
  from paddlehub.compat.task.config import RunConfig
  from paddlehub.compat.task.base_task import BaseTask
  from paddlehub.compat.task.task_utils import RunState
label_desc: Use image node_id 3 for calling the TransformerEmbeddingTask obj's underlying member method code with example usage: obj._postprocessing(run_states) and returns: results