code: def __init__(
      self,
      dictionary,
      output_embedding,
      no_emb_update_unsup=False,
      use_output_proj=False,
  ):
      super().__init__(dictionary)
      self.output_embedding = output_embedding
      num_embedding, num_dim = self.output_embedding.weight.size()
      self.out_proj = (
          None
          if use_output_proj is False
          else nn.Linear(num_dim, num_dim)
      )
      self.no_emb_update_unsup = no_emb_update_unsup
  
imports: import logging
  from collections import OrderedDict, namedtuple
  from typing import Dict, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from fairseq import checkpoint_utils, utils
  from fairseq.file_io import PathManager
  from fairseq.models import FairseqDecoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.models.speech_to_text import MultiInputDecoder, MultiModalityEncoder, SpeechWavTransformerEncoder, StackedSpeechWavTransformerEncoder
  from fairseq.models.transformer import TransformerDecoder, TransformerEncoder, TransformerModel
label_desc: Use image node_id 1 to create a new SpeechDummyDecoder object from inherited base classes: FairseqDecoder with example: obj = SpeechDummyDecoder(dictionary, output_embedding, no_emb_update_unsup, use_output_proj)