code: def get_top_pair_loss(n):
      def top_pair_loss(scores, targets, debug=False):
          """Top pairs (best true and best mistaken) and single mention probabilistic loss"""
          true_ants = targets[2]
          false_ants = targets[3] if len(targets) == 5 else None
          s_scores = clipped_sigmoid(scores)
          true_pairs = torch.gather(s_scores, 1, true_ants)
          top_true, top_true_arg = torch.log(true_pairs).max(
              dim=1
          )  # max(log(p)), p=sigmoid(s)
          if debug:
              print("true_pairs", true_pairs.data)
              print("top_true", top_true.data)
              print("top_true_arg", top_true_arg.data)
          out_score = torch.sum(top_true).neg()
          if (
              false_ants is not None
          ):  # We have no false antecedents when there are no pairs
              false_pairs = torch.gather(s_scores, 1, false_ants)
              top_false, _ = torch.log(1 - false_pairs).min(
                  dim=1
              )  # min(log(1-p)), p=sigmoid(s)
              out_score = out_score + torch.sum(top_false).neg()
          return out_score / n
  
      return top_pair_loss
  
imports: import os
  import time
  import argparse
  import socket
  from datetime import datetime
  import numpy
  import torch
  import torch.nn
  from torch.autograd import Variable
  from torch.optim import RMSprop
  from torch.utils.data import DataLoader
  from tensorboardX import SummaryWriter
  from neuralcoref.train.model import Model
  from neuralcoref.train.dataset import NCDataset, NCBatchSampler, load_embeddings_from_file, padder_collate, SIZE_PAIR_IN, SIZE_SINGLE_IN
  from neuralcoref.train.utils import SIZE_EMBEDDING
  from neuralcoref.train.evaluator import ConllEvaluator
label_desc: Use image node_id 3 for calling a global function with example usage: get_top_pair_loss(n) and returns: top_pair_loss, unknown