class_docstr: This is a general transformer module with multiple transformer layers
code: def __init__(
      self, layer: FastWeightsAttentionTransformerLayer, n_layers: int
  ):
      super().__init__()
      # Make copies of the transformer layer
      self.layers = clone_module_list(layer, n_layers)
      # Final normalization layer
      self.norm = nn.LayerNorm([layer.size])
  
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 1 to create a new FastWeightsAttentionTransformer object from inherited base classes: Module with example: obj = FastWeightsAttentionTransformer(layer, n_layers)