code: def forward(self, image, caption):
      image_embeds = self.visual_encoder(image)
      image_atts = torch.ones(
          image_embeds.size()[:-1], dtype=torch.long
      ).to(image.device)
  
      text = self.tokenizer(
          caption,
          padding="longest",
          truncation=True,
          max_length=40,
          return_tensors="pt",
      ).to(image.device)
  
      text.input_ids[:, 0] = self.tokenizer.bos_token_id
  
      decoder_targets = text.input_ids.masked_fill(
          text.input_ids == self.tokenizer.pad_token_id, -100
      )
      decoder_targets[:, : self.prompt_length] = -100
  
      decoder_output = self.text_decoder(
          text.input_ids,
          attention_mask=text.attention_mask,
          encoder_hidden_states=image_embeds,
          encoder_attention_mask=image_atts,
          labels=decoder_targets,
          return_dict=True,
      )
      loss_lm = decoder_output.loss
  
      return loss_lm
  
imports: import warnings
  from models.vit import VisionTransformer, interpolate_pos_embed
  from models.med import BertConfig, BertModel, BertLMHeadModel
  from transformers import BertTokenizer
  import torch
  from torch import nn
  import torch.nn.functional
  import os
  from urllib.parse import urlparse
  from timm.models.hub import download_cached_file
label_desc: Use image node_id 2 for calling the BLIP_Decoder obj's underlying member method code with example usage: obj.forward(image, caption) and returns: loss_lm