class_docstr: <a id="Encoder"></a>
  
  ## Transformer Encoder
code: def __init__(self, layer: TransformerLayer, 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 math
  import torch
  import torch.nn
  from labml_nn.utils import clone_module_list
  from .feed_forward import FeedForward
  from .mha import MultiHeadAttention
  from .positional_encoding import get_positional_encoding
label_desc: Use image node_id 1 to create a new Encoder object from inherited base classes: nn with example: obj = Encoder(layer, n_layers)