class_docstr: generated `pooled_video` and `pooled_text`.
class_docstr_tok: ["generated","`","pooled_video","`","and","`","pooled_text","`","."]
code: def __call__(self, sample):
      hidden_size = sample["pooled_video"].size(-1)
      pooled_video = sample["pooled_video"].view(-1, 5, hidden_size)
      pooled_text = sample["pooled_text"].view(-1, 5, hidden_size)
      scores = torch.bmm(pooled_video, pooled_text.transpose(2, 1))
      scores = scores.argmax(-1)
      self._append_scores(
          scores[:, 0], sample["answers"], self.full_scores
      )
  
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 3 for calling the QAPredictor obj's underlying member method code with example usage: obj.__call__(sample) without return types