code: def __init__(
      self,
      args,
      dictionary,
      embed_tokens,
      no_encoder_attn=False,
      output_projection=None,
  ):
      self.args = args
      super().__init__(
          TransformerConfig.from_namespace(args),
          dictionary,
          embed_tokens,
          no_encoder_attn=no_encoder_attn,
          output_projection=output_projection,
      )
  
imports: import math
  from typing import Any, Dict, List, Optional
  import torch
  import torch.nn
  from torch import Tensor
  from fairseq import utils
  from fairseq.distributed import fsdp_wrap
  from fairseq.models import FairseqIncrementalDecoder
  from fairseq.models.transformer import TransformerConfig
  from fairseq.modules import AdaptiveSoftmax, BaseLayer, FairseqDropout, LayerDropModuleList, LayerNorm, PositionalEmbedding, SinusoidalPositionalEmbedding, transformer_layer
  from fairseq.modules.checkpoint_activations import checkpoint_wrapper
  from fairseq.modules.quant_noise import quant_noise
label_desc: Use image node_id 1 to create a new TransformerDecoder object from inherited base classes: TransformerDecoderBase with example: obj = TransformerDecoder(args, dictionary, embed_tokens, no_encoder_attn, output_projection)