code: def forward(
      self,
      input_ids=None,
      attention_mask=None,
      token_type_ids=None,
      labels=None,
      images=None,
      aux_imgs=None,
      rcnn_imgs=None,
  ):
      bsz = input_ids.size(0)
      output = self.model(
          input_ids=input_ids,
          attention_mask=attention_mask,
          token_type_ids=token_type_ids,
          pixel_values=images,
          aux_values=aux_imgs,
          rcnn_values=rcnn_imgs,
          return_dict=True,
      )
  
      last_hidden_state, pooler_output = (
          output.last_hidden_state,
          output.pooler_output,
      )
      bsz, seq_len, hidden_size = last_hidden_state.shape
      entity_hidden_state = torch.Tensor(
          bsz, 2 * hidden_size
      )  # batch, 2*hidden
      for i in range(bsz):
          head_idx = input_ids[i].eq(self.head_start).nonzero().item()
          tail_idx = input_ids[i].eq(self.tail_start).nonzero().item()
          head_hidden = last_hidden_state[i, head_idx, :].squeeze()
          tail_hidden = last_hidden_state[i, tail_idx, :].squeeze()
          entity_hidden_state[i] = torch.cat(
              [head_hidden, tail_hidden], dim=-1
          )
      entity_hidden_state = entity_hidden_state.to(self.args.device)
      logits = self.classifier(entity_hidden_state)
      if labels is not None:
          loss_fn = nn.CrossEntropyLoss()
          return loss_fn(logits, labels.view(-1)), logits
      return logits
  
file_path: DeepKE/src/deepke/relation_extraction/multimodal/models/IFA_model.py
imports: import torch
  from torch import nn
  import torch.nn.functional
  from .modeling_IFA import IFAModel
  from transformers import BertConfig, BertModel, CLIPConfig, CLIPModel
label_desc: Use image node_id 2 for calling the IFAREModel obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, token_type_ids, labels, images, aux_imgs, rcnn_imgs) and returns: logits, loss_fn,  logits