class_docstr: generated `pooled_video` and `pooled_text`.
class_docstr_tok: ["generated","`","pooled_video","`","and","`","pooled_text","`","."]
code: def _aggregate_scores(self, scores):
      assert len(scores) == 2
      outputs = np.concatenate(scores[0], axis=0)
      targets = np.concatenate(scores[1], axis=0)
      # clear up.
      self.full_scores = []
      return outputs, targets
  
imports: import os
  import random
  import json
  import numpy
  import torch
  import pickle
  import math
  from tqdm import tqdm
label_desc: Use image node_id 6 for calling the QAPredictor obj's underlying member method code with example usage: obj._aggregate_scores(scores) and returns: outputs,  targets