class_docstr: COINZSPredictor for COIN zero-shot prediction.
class_docstr_tok: ["COINZSPredictor","for","COIN","zero-shot","prediction","."]
code: def __call__(
      self, sample, label_hidden_states, model, lbd, Y_pred, Y_true
  ):
      sample = self.reshape_subsample(sample)
      sample = self.to_ctx(sample)
      # compute the average logits over sliding windows.
      sample["output_hidden_states"] = True
      video_outputs = model.forward_video(**sample).cpu()
      output = {
          "logits": video_outputs[:, 1 : sample["vmasks"].size(1) + 1]
          @ label_hidden_states.t()
      }
      logits = self._merge_windows(sample, output)
      # logic of zero-shot for sequence labeling.
      logits_argmax = logits.argmax(dim=1) + 1  # 0 is "O" label.
      logits_max = logits.max(dim=1)[0]
  
      pred = torch.zeros_like(logits_argmax)
      label_select = logits_max > lbd  # 73 or 74
      pred[label_select] = logits_argmax[label_select]
  
      Y_pred.append(pred)
      Y_true.append(sample["video_targets"].squeeze(0).cpu())
  
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 5 for calling the COINZSPredictor obj's underlying member method code with example usage: obj.__call__(sample, label_hidden_states, model, lbd, Y_pred, Y_true) without return types