class_docstr: <a id="Encoder"></a>
  
  ## Transformer Encoder
code: def forward(self, x: torch.Tensor, mask: torch.Tensor):
      # Run through each transformer layer
      for layer in self.layers:
          x = layer(x=x, mask=mask)
      # Finally, normalize the vectors
      return self.norm(x)
  
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 2 for calling the Encoder obj's underlying member method code with example usage: obj.forward(x, mask) and returns: self