code: def forward(self, x, context=None):
      layers = dropout_layers(self.layers, dropout=self.layer_dropout)
  
      for attn, ff in layers:
          x = attn(x, context=context) + x
          x = ff(x) + x
      return x
  
imports: from random import randrange
  import torch
  from torch import nn, einsum
  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 Transformer obj's underlying member method code with example usage: obj.forward(x, context) and returns: x