code: def forward(
      self,
      input_ids,
      token_type_ids=None,
      attention_mask=None,
      labels=None,
      sub_idx=None,
      obj_idx=None,
  ):
      outputs = self.albert(
          input_ids,
          token_type_ids=token_type_ids,
          attention_mask=attention_mask,
          output_hidden_states=False,
          output_attentions=False,
      )
      sequence_output = outputs[0]
      sub_output = torch.cat(
          [a[i].unsqueeze(0) for a, i in zip(sequence_output, sub_idx)]
      )
      obj_output = torch.cat(
          [a[i].unsqueeze(0) for a, i in zip(sequence_output, obj_idx)]
      )
      rep = torch.cat((sub_output, obj_output), dim=1)
      rep = self.layer_norm(rep)
      rep = self.dropout(rep)
      logits = self.classifier(rep)
  
      if labels is not None:
          loss_fct = CrossEntropyLoss()
          loss = loss_fct(
              logits.view(-1, self.num_labels), labels.view(-1)
          )
          return loss
      else:
          return logits
  
imports: import os
  import sys
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from transformers import BertModel, BertPreTrainedModel
  from transformers import AlbertModel, AlbertPreTrainedModel
  from allennlp.modules import FeedForward
  from allennlp.nn.util import batched_index_select
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the AlbertForRelation obj's underlying member method code with example usage: obj.forward(input_ids, token_type_ids, attention_mask, labels, sub_idx, obj_idx) and returns: loss, logits