class_docstr: CrossTaskPredictor needs to compute the average of logits
  for overlapped sliding-window.
class_docstr_tok: ["CrossTaskPredictor","needs","to","compute","the","average","of","logits","for","overlapped","sliding-window","."]
code: def __call__(self, sample, model, Y_pred, Y_true):
      # please install dp from `https://github.com/DmZhukov/CrossTask`
      from dp import dp
  
      vid, task = sample["video_id"][0], sample["task"][0]
      sample = self.to_ctx(sample)
      # compute the average logits over sliding windows.
      output = model(**sample)
      batch_logits = output["logits"].cpu()
  
      video_len = sample["video_len"][0]
  
      # the following version is slow.
      logits = torch.zeros((video_len, batch_logits.size(1)))
      logits_counts = torch.zeros((video_len, 1), dtype=torch.long)
      # use the same loop as aligner to recover.
      batch_logit_idx = 0
      for window_start in range(0, video_len, self.sliding_window):
          video_end = min(
              video_len - window_start, self.sliding_window_size
          )
          logits[
              window_start : window_start + video_end
          ] += batch_logits[
              batch_logit_idx : batch_logit_idx + video_end
          ]
          batch_logit_idx += video_end
          logits_counts[
              window_start : window_start + video_end
          ] += torch.ones((video_end, 1), dtype=torch.long)
  
          if (video_len - window_start) <= self.sliding_window_size:
              break
  
      logits /= logits_counts
      assert logits.size() == (
          video_len,
          batch_logits.size(1),
      ), "{}, {}".format(logits.size(), video_len)
  
      O = self.lsm(logits)
      y = np.zeros(O.size(), dtype=np.float32)
      dp(y, -O.detach().cpu().numpy())
      if task not in Y_pred:
          Y_pred[task] = {}
      Y_pred[task][vid] = y
      annot_path = os.path.join(
          self.annotation_path, task + "_" + vid + ".csv"
      )
      if os.path.exists(annot_path):
          if task not in Y_true:
              Y_true[task] = {}
          Y_true[task][vid] = self._read_assignment(
              *y.shape, annot_path
          )
  
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 CrossTaskPredictor obj's underlying member method code with example usage: obj.__call__(sample, model, Y_pred, Y_true) without return types