class_docstr: generated `pooled_video` and `pooled_text`.
class_docstr_tok: ["generated","`","pooled_video","`","and","`","pooled_text","`","."]
docstr: on-the-fly prediction on a single gpu.
docstr_tok: ["on-the-fly","prediction","on","a","single","gpu","."]
code: def predict_loop(self, model, eval_dataloader, output_file="qa.npy"):
      """on-the-fly prediction on a single gpu."""
      self.full_scores = []
      model.eval()
      model = model.cuda()
      with torch.no_grad():
          for data in eval_dataloader:
              # reshape ans and dup video 5 times.
              v_len = data["vfeats"].size(1)
              hidden_size = data["vfeats"].size(2)
              data["vfeats"] = (
                  data["vfeats"]
                  .unsqueeze(1)
                  .repeat(1, 5, 1, 1)
                  .view(-1, v_len, hidden_size)
              )
              data["vmasks"] = (
                  data["vmasks"]
                  .unsqueeze(1)
                  .repeat(1, 5, 1)
                  .view(-1, v_len)
              )
  
              t_len = data["caps"].size(-1)
              data["caps"] = data["caps"].view(-1, t_len)
              data["cmasks"] = data["cmasks"].view(-1, t_len)
  
              data = self.to_ctx(data)
              outputs = model(**data)
              outputs.update(data)
              self(outputs)
      return self.finalize(output_file)
  
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 2 for calling the QAPredictor obj's underlying member method code with example usage: obj.predict_loop(model, eval_dataloader, output_file) and returns: self