code: def get_max_score(self, batch, debug=False):
      inputs, mask = batch
      if self.cuda:
          inputs = tuple(i.cuda() for i in inputs)
          mask = mask.cuda()
      self.model.eval()
      with torch.no_grad():
          scores = self.model(inputs, concat_axis=1)
          scores.masked_fill_(mask, -float("Inf"))
          _, max_idx = scores.max(
              dim=1
          )  # We may want to weight the single score with coref.greedyness
      if debug:
          print("Max_idx", max_idx)
      return scores.cpu().numpy(), max_idx.cpu().numpy()
  
imports: import os
  import subprocess
  import io
  import pickle
  import torch
  from torch.utils.data import DataLoader
  from neuralcoref.train.conllparser import FEATURES_NAMES
  from neuralcoref.train.dataset import NCBatchSampler, padder_collate
  from neuralcoref.train.compat import unicode_
label_desc: Use image node_id 7 for calling the ConllEvaluator obj's underlying member method code with example usage: obj.get_max_score(batch, debug) and returns: scores,  max_idx