code: def forward(self, image, text, targets, train=True):
      image_embeds = self.visual_encoder(image)
      image_atts = torch.ones(
          image_embeds.size()[:-1], dtype=torch.long
      ).to(image.device)
      image0_embeds, image1_embeds = torch.split(
          image_embeds, targets.size(0)
      )
  
      text = self.tokenizer(
          text, padding="longest", return_tensors="pt"
      ).to(image.device)
      text.input_ids[:, 0] = self.tokenizer.enc_token_id
  
      output = self.text_encoder(
          text.input_ids,
          attention_mask=text.attention_mask,
          encoder_hidden_states=[image0_embeds, image1_embeds],
          encoder_attention_mask=[
              image_atts[: image0_embeds.size(0)],
              image_atts[image0_embeds.size(0) :],
          ],
          return_dict=True,
      )
      hidden_state = output.last_hidden_state[:, 0, :]
      prediction = self.cls_head(hidden_state)
  
      if train:
          loss = F.cross_entropy(prediction, targets)
          return loss
      else:
          return prediction
  
file_path: blip/models/blip_nlvr.py
imports: from models.med import BertConfig
  from models.nlvr_encoder import BertModel
  from models.vit import interpolate_pos_embed
  from models.blip import create_vit, init_tokenizer, is_url
  from timm.models.hub import download_cached_file
  import torch
  from torch import nn
  import torch.nn.functional
  from transformers import BertTokenizer
  import numpy
label_desc: Use image node_id 2 for calling the BLIP_NLVR obj's underlying member method code with example usage: obj.forward(image, text, targets, train) and returns: loss, prediction