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 add_args(parser):
      super(
          SimulConvTransformerModel, SimulConvTransformerModel
      ).add_args(parser)
      parser.add_argument(
          "--train-monotonic-only",
          action="store_true",
          default=False,
          help="Only train monotonic attention",
      )
  
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 1 for calling the SimulConvTransformerModel obj's underlying member method code with example usage: obj.add_args(parser) without return types