code: def forward(self, x):
      x = self.token_emb(x)
      x = x + self.pos_emb(
          torch.arange(x.shape[-2], device=self.device)
      )
  
      rotary_emb = None
      if exists(self.rotary_emb):
          rotary_emb = self.rotary_emb(x.shape[1])
  
      for attn, ff in self.layers:
          x = attn(x, rotary_emb=rotary_emb) + x
  
          x = ff(x) + x
  
      return self.to_logits(x)
  
file_path: mixture-of-attention/mixture_of_attention/transformer.py
imports: import torch
  import torch.nn.functional
  from torch import nn, einsum
  from einops import rearrange
  from mixture_of_attention.mixture_of_attention import MixtureOfAutoregressiveAttention
  from mixture_of_attention.rotary_emb import RotaryEmbedding
label_desc: Use image node_id 3 for calling the Transformer obj's underlying member method code with example usage: obj.forward(x) and returns: self