code: def __init__(self, args, dictionary, *kargs, **kwargs):
      self.num_langs = kwargs.get("num_langs", 1)
      self.lang_embed_dim = kwargs.get("lang_embed_dim", 0)
      kwargs.pop("num_langs", None)
      kwargs.pop("lang_embed_dim", None)
  
      super().__init__(
          args, dictionary, *kargs, **kwargs, no_encoder_attn=True
      )
  
      if self.lang_embed_dim == 0:
          self.embed_lang = None
      else:
          self.embed_lang = nn.Embedding(
              self.num_langs, self.lang_embed_dim
          )
          nn.init.uniform_(self.embed_lang.weight, -0.1, 0.1)
  
      if self.output_projection is not None:
          laser_output_embed_dim = (
              self.output_embed_dim
              + self.lang_embed_dim
              + args.encoder_embed_dim
          )
          self.output_projection = nn.Linear(
              laser_output_embed_dim, len(dictionary), bias=False
          )
          nn.init.normal_(
              self.output_projection.weight,
              mean=0,
              std=laser_output_embed_dim**-0.5,
          )
  
imports: import logging
  from typing import Any, Dict, List, Optional
  from torch import Tensor
  import torch
  import torch.nn
  from fairseq.models import FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.models.transformer import base_architecture, Embedding, TransformerModel, TransformerEncoder, TransformerDecoder
  from fairseq.modules import TransformerDecoderLayer
label_desc: Use image node_id 1 to create a new LaserTransformerDecoder object from inherited base classes: TransformerDecoder with example: obj = LaserTransformerDecoder(args, dictionary)