code: def forward(self, image, caption, mode):
      assert mode in [
          "image",
          "text",
          "multimodal",
      ], "mode parameter must be image, text, or multimodal"
      text = self.tokenizer(caption, return_tensors="pt").to(
          image.device
      )
  
      if mode == "image":
          # return image features
          image_embeds = self.visual_encoder(image)
          return image_embeds
  
      elif mode == "text":
          # return text features
          text_output = self.text_encoder(
              text.input_ids,
              attention_mask=text.attention_mask,
              return_dict=True,
              mode="text",
          )
          return text_output.last_hidden_state
  
      elif mode == "multimodal":
          # return multimodel features
          image_embeds = self.visual_encoder(image)
          image_atts = torch.ones(
              image_embeds.size()[:-1], dtype=torch.long
          ).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=image_embeds,
              encoder_attention_mask=image_atts,
              return_dict=True,
          )
          return output.last_hidden_state
  
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_Base obj's underlying member method code with example usage: obj.forward(image, caption, mode) and returns: image_embeds, text_output, output