code: def forward(self, input_ids, attention_mask=None):
      key_padding_mask = (
          (attention_mask == 0)
          if (not self.autogressive and attention_mask is not None)
          else None
      )
      x = self.token_embedding(
          input_ids
      )  # [batch_size, n_ctx, d_model]
      x = x + self.positional_embedding
      x = x.permute(1, 0, 2)  # NLD -> LND
      for block in self.resblocks:
          x = block(x, key_padding_mask)
      x = x.permute(1, 0, 2)  # LND -> NLD
  
      x = self.ln_final(x)
  
      return {"last_hidden_state": x}
  
imports: import os
  from collections import OrderedDict
  import torch
  from mmcv.cnn.bricks import DropPath
  from torch import nn
  from transformers import CLIPTokenizer
  from .utils import get_prompt_templates
label_desc: Use image node_id 4 for calling the Transformer obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask) and returns: dict