code: def forward(self, x, *, mask):
      num_tokens = x.shape[1]
  
      x = self.norm(x)
      qkv = self.to_qkv(x).chunk(3, dim=-1)
      q, k, v = map(
          lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.heads),
          qkv,
      )
  
      dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
  
      if exists(mask):
          dots_mask = rearrange(mask, "b i -> b 1 i 1") * rearrange(
              mask, "b j -> b 1 1 j"
          )
          mask_value = -torch.finfo(dots.dtype).max
          dots = dots.masked_fill(~dots_mask, mask_value)
  
      attn = self.attend(dots)
      attn = self.dropout(attn)
  
      sampled_token_ids = None
  
      # if adaptive token sampling is enabled
      # and number of tokens is greater than the number of output tokens
      if (
          exists(self.output_num_tokens)
          and (num_tokens - 1) > self.output_num_tokens
      ):
          attn, mask, sampled_token_ids = self.ats(attn, v, mask=mask)
  
      out = torch.matmul(attn, v)
      out = rearrange(out, "b h n d -> b n (h d)")
  
      return self.to_out(out), mask, sampled_token_ids
  
imports: import torch
  import torch.nn.functional
  from torch.nn.utils.rnn import pad_sequence
  from torch import nn, einsum
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 2 for calling the Attention obj's underlying member method code with example usage: obj.forward(x) and returns: self,  mask,  sampled_token_ids