code: def forward(self, x: torch.Tensor, weights: Optional[torch.Tensor]):
      attn, weights = self.attn(x, weights)
      # Add the self attention results
      x = x + self.dropout(attn)
  
      # Normalize for feed-forward
      z = self.norm_ff(x)
      # Pass through the feed-forward network
      ff = self.feed_forward(z)
      # Add the feed-forward results back
      x = x + self.dropout(ff)
  
      #
      return x, weights
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/transformers/fast_weights/token_wise.py
imports: from typing import Optional
  import torch
  from torch import nn
  from labml_helpers.module import Module
  from labml_nn.transformers.fast_weights import DPFP
  from labml_nn.transformers.feed_forward import FeedForward
  from labml_nn.transformers.mha import PrepareForMultiHeadAttention
  from labml_nn.utils import clone_module_list
label_desc: Use image node_id 2 for calling the FastWeightsAttentionTransformerLayer obj's underlying member method code with example usage: obj.forward(x, weights) and returns: x,  weights