code: def forward(self, inputs, concat_axis=1):
      pairs = len(inputs) == 8
      if pairs:
          (
              spans,
              words,
              single_features,
              ant_spans,
              ant_words,
              ana_spans,
              ana_words,
              pair_features,
          ) = inputs
      else:
          spans, words, single_features = inputs
      words = words.type(torch.LongTensor)
      if torch.cuda.is_available():
          words = words.cuda()
      embed_words = self.drop(
          self.word_embeds(words).view(words.size()[0], -1)
      )
      single_input = torch.cat([spans, embed_words, single_features], 1)
      single_scores = self.single_top(single_input)
      if pairs:
          batchsize, pairs_num, _ = ana_spans.size()
          ant_words_long = ant_words.view(batchsize, -1).type(
              torch.LongTensor
          )
          ana_words_long = ana_words.view(batchsize, -1).type(
              torch.LongTensor
          )
          if torch.cuda.is_available():
              ant_words_long = ant_words_long.cuda()
              ana_words_long = ana_words_long.cuda()
          ant_embed_words = self.drop(
              self.word_embeds(ant_words_long).view(
                  batchsize, pairs_num, -1
              )
          )
          ana_embed_words = self.drop(
              self.word_embeds(ana_words_long).view(
                  batchsize, pairs_num, -1
              )
          )
          pair_input = torch.cat(
              [
                  ant_spans,
                  ant_embed_words,
                  ana_spans,
                  ana_embed_words,
                  pair_features,
              ],
              2,
          )
          pair_scores = self.pair_top(pair_input).squeeze(dim=2)
          total_scores = torch.cat(
              [pair_scores, single_scores], concat_axis
          )
      return total_scores if pairs else single_scores
  
file_path: neuralcoref/neuralcoref/train/model.py
imports: import os
  import numpy
  import torch
  import torch.nn
  import torch.utils.data
label_desc: Use image node_id 5 for calling the Model obj's underlying member method code with example usage: obj.forward(inputs, concat_axis) and returns: unknown