class_docstr: reference: https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/eval.py
  https://github.com/LisaAnne/LocalizingMoments/blob/master/utils/data_processing.py
class_docstr_tok: ["reference",":","https",":","\/\/github.com\/LisaAnne\/LocalizingMoments\/blob\/master\/utils\/eval.py","https",":","\/\/github.com\/LisaAnne\/LocalizingMoments\/blob\/master\/utils\/data_processing.py"]
docstr: TODO: two solutions here.
docstr_tok: ["TODO",":","two","solutions","here","."]
code: def predict_loop(
      self, model, eval_dataloader, output_file="didemo.npy"
  ):
      """
      TODO: two solutions here.
      """
      import itertools
  
      # 21 chunks.
      self.possible_segments = [
          (0, 0),
          (1, 1),
          (2, 2),
          (3, 3),
          (4, 4),
          (5, 5),
      ]
      for i in itertools.combinations(range(6), 2):
          self.possible_segments.append(i)
      # pick segments from a video.
  
      """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:
              # TODO special forwarding logic here.
              data = self.to_ctx(data)
              data["output_hidden_states"] = True
              hidden_video = model.forward_video(**data)
              data["output_hidden_states"] = False
              pooled_text = model.forward_text(**data)
              outputs = {
                  "hidden_video": hidden_video,
                  "pooled_text": pooled_text,
              }
              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 DiDeMoPredictor obj's underlying member method code with example usage: obj.predict_loop(model, eval_dataloader, output_file) and returns: self