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="retrieval.npy"
  ):
      """on-the-fly prediction on a single gpu."""
      full_scores = []
      texts = []
      model.eval()
      model = model.cuda()
      with torch.no_grad():
          for data in eval_dataloader:
              # convert to dict.
              if not isinstance(data, dict):
                  data = {
                      "caps": data[0],
                      "cmasks": data[1],
                      "vfeats": data[2],
                      "vmasks": data[3],
                      "video_id": data[4],
                  }
              data = self.to_ctx(data)
              outputs = model(**data)
              outputs.update(data)
              self(outputs, full_scores)
              for _cap in data["caps"]:
                  texts.append(
                      self.tokenizer.decode(
                          _cap, skip_special_tokens=True
                      )
                  )
  
      return self.finalize(full_scores, texts, 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 RetrievalPredictor obj's underlying member method code with example usage: obj.predict_loop(model, eval_dataloader, output_file) and returns: self