class_docstr: Represents a Transformer decoder.
class_docstr_tok: ["Represents","a","Transformer","decoder","."]
docstr: :meta private:
docstr_tok: [":","meta","private",":"]
code: def extra_repr(self) -> str:
      """:meta private:"""
      return f"model_dim={self.model_dim}"
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from collections import OrderedDict
  from typing import Dict, Iterable, Optional, Protocol, Tuple, final
  from torch import Tensor
  from torch.nn import Module
  from torch.utils.hooks import RemovableHandle
  from fairseq2.nn.incremental_state import IncrementalStateBag
  from fairseq2.nn.module_list import ModuleList
  from fairseq2.nn.normalization import LayerNorm
  from fairseq2.nn.padding import PaddingMask
  from fairseq2.nn.transformer.attention_mask import AttentionMaskFactory, CausalAttentionMaskFactory
  from fairseq2.nn.transformer.decoder_layer import TransformerDecoderLayer
  from fairseq2.nn.transformer.layer_norm import LayerNormFactory, create_standard_layer_norm
  from fairseq2.nn.transformer.norm_order import TransformerNormOrder
  from fairseq2.typing import DataType, Device, finaloverride
label_desc: Use image node_id 4 for calling the TransformerDecoder obj's underlying member method code with example usage: obj.extra_repr() and returns: str, self