code: def forward(self, x_seq: torch.Tensor):
      # Split the input to a list along the sequence axis
      x_seq = torch.unbind(x_seq, dim=0)
      # List to store the outputs
      res = []
      # For each input step
      weights = [None for _ in range(len(self.layers))]
  
      for x in x_seq:
          # Run through each layer
          for i, layer in enumerate(self.layers):
              # Get layer output
              x, weights[i] = layer(x, weights[i])
  
          res.append(x)
  
      # Stack the output tensors
      res = torch.stack(res)
      # Normalize the output
      return self.norm(res)
  
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 FastWeightsAttentionTransformer obj's underlying member method code with example usage: obj.forward(x_seq) and returns: self