code: def forward(self, img):
      b = img.shape[0]
  
      tokens = self.vit.img_to_tokens(img)
  
      # add task specific memory tokens
  
      memory_cls_tokens = repeat(
          self.memory_cls_token, "d -> b 1 d", b=b
      )
      tokens = torch.cat((memory_cls_tokens, tokens), dim=1)
  
      # pass memories along with image tokens through transformer for attending
  
      out = self.vit.transformer(
          tokens,
          memories=self.memories_per_layer,
          attn_mask=self.attn_mask,
      )
  
      # extract memory CLS tokens
  
      memory_cls_tokens = out[:, 0]
  
      # pass through task specific adapter head
  
      return self.mlp_head(memory_cls_tokens)
  
imports: import torch
  from torch import nn
  import torch.nn.functional
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 2 for calling the Adapter obj's underlying member method code with example usage: obj.forward(img) and returns: self