class_docstr: This is a general transformer layer that combines self attention and feedforward network.
code: def forward(self, x: torch.Tensor):
      # Calculate fast weights self attention
      attn = self.attn(x)
      # 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
  
imports: import torch
  from torch import nn
  from labml_helpers.module import Module
  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) and returns: x