class_docstr: Implementation of the paper:
  
  SimulMT to SimulST: Adapting Simultaneous Text Translation to
  End-to-End Simultaneous Speech Translation
  
  https://www.aclweb.org/anthology/2020.aacl-main.58.pdf
class_docstr_tok: ["Implementation","of","the","paper",":","SimulMT","to","SimulST",":","Adapting","Simultaneous","Text","Translation","to","End-to-End","Simultaneous","Speech","Translation","https",":","\/\/www.aclweb.org\/anthology\/2020.aacl-main.58.pdf"]
code: def build_decoder(cls, args, task, embed_tokens):
      tgt_dict = task.tgt_dict
  
      from examples.simultaneous_translation.models.transformer_monotonic_attention import (
          TransformerMonotonicDecoder,
      )
  
      decoder = TransformerMonotonicDecoder(
          args, tgt_dict, embed_tokens
      )
  
      if getattr(args, "load_pretrained_decoder_from", None):
          decoder = (
              checkpoint_utils.load_pretrained_component_from_model(
                  component=decoder,
                  checkpoint=args.load_pretrained_decoder_from,
              )
          )
      return decoder
  
imports: from fairseq import checkpoint_utils
  from fairseq.models import register_model, register_model_architecture
  from fairseq.models.speech_to_text import ConvTransformerModel, convtransformer_espnet, ConvTransformerEncoder
  from fairseq.models.speech_to_text.modules.augmented_memory_attention import augmented_memory, SequenceEncoder, AugmentedMemoryConvTransformerEncoder
  from torch import nn, Tensor
  from typing import Dict, List
  from fairseq.models.speech_to_text.modules.emformer import NoSegAugmentedMemoryTransformerEncoderLayer
label_desc: Use image node_id 2 for calling the SimulConvTransformerModel obj's underlying member method code with example usage: obj.build_decoder(cls, args, task, embed_tokens) and returns: decoder