code: def __init__(self, args, dictionary, embed_tokens):
      super().__init__(args, dictionary, embed_tokens)
  
      self.dictionary = dictionary
      self.layers = nn.ModuleList([])
      self.layers.extend(
          [
              TransformerMonotonicEncoderLayer(args)
              for i in range(args.encoder_layers)
          ]
      )
  
imports: from typing import Dict, List, NamedTuple, Optional
  import torch
  import torch.nn
  from examples.simultaneous_translation.modules.monotonic_transformer_layer import TransformerMonotonicDecoderLayer, TransformerMonotonicEncoderLayer
  from fairseq.models import register_model, register_model_architecture
  from fairseq.models.transformer import TransformerModel, TransformerEncoder, TransformerDecoder, base_architecture, transformer_iwslt_de_en, transformer_vaswani_wmt_en_de_big, tiny_architecture
  from torch import Tensor
label_desc: Use image node_id 1 to create a new TransformerMonotonicEncoder object from inherited base classes: TransformerEncoder with example: obj = TransformerMonotonicEncoder(args, dictionary, embed_tokens)