code: def forward(self, x):
      b, n, device = *x.shape[:2], x.device
  
      # use mask to keep track of the paddings when sampling tokens
      # as the duplicates (when sampling) are just removed, as mentioned in the paper
      mask = torch.ones((b, n), device=device, dtype=torch.bool)
  
      token_ids = torch.arange(n, device=device)
      token_ids = repeat(token_ids, "n -> b n", b=b)
  
      for attn, ff in self.layers:
          attn_out, mask, sampled_token_ids = attn(x, mask=mask)
  
          # when token sampling, one needs to then gather the residual tokens with the sampled token ids
          if exists(sampled_token_ids):
              x = batched_index_select(x, sampled_token_ids, dim=1)
              token_ids = batched_index_select(
                  token_ids, sampled_token_ids, dim=1
              )
  
          x = x + attn_out
  
          x = ff(x) + x
  
      return x, 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 Transformer obj's underlying member method code with example usage: obj.forward(x) and returns: x,  token_ids