class_docstr: Transformer decoder consisting of *cfg.decoder.layers* layers. Each layer
  is a :class:`TransformerDecoderLayer`.
  
  Args:
      cfg (argparse.Namespace): parsed command-line arguments
      dictionary (~fairseq.data.Dictionary): decoding dictionary
      embed_tokens (torch.nn.Embedding): output embedding
      no_encoder_attn (bool, optional): whether to attend to encoder outputs
          (default: False).
class_docstr_tok: ["Transformer","decoder","consisting","of","*","cfg.decoder.layers","*","layers",".","Each","layer","is","a",":","class",":","`","TransformerDecoderLayer","`",".","Args",":","cfg","(","argparse.Namespace",")",":","parsed","command-line","arguments","dictionary","(","~fairseq.data.Dictionary",")",":","decoding","dictionary","embed_tokens","(","torch.nn.Embedding",")",":","output","embedding","no_encoder_attn","(","bool",",","optional",")",":","whether","to","attend","to","encoder","outputs","(","default",":","False",")","."]
code: def extract_features(
      self,
      prev_output_tokens,
      encoder_out: Optional[Dict[str, List[Tensor]]],
      incremental_state: Optional[
          Dict[str, Dict[str, Optional[Tensor]]]
      ] = None,
      full_context_alignment: bool = False,
      alignment_layer: Optional[int] = None,
      alignment_heads: Optional[int] = None,
  ):
      return self.extract_features_scriptable(
          prev_output_tokens,
          encoder_out,
          incremental_state,
          full_context_alignment,
          alignment_layer,
          alignment_heads,
      )
  
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 5 for calling the TransformerDecoderBase obj's underlying member method code with example usage: obj.extract_features(prev_output_tokens, encoder_out, incremental_state, full_context_alignment, alignment_layer, alignment_heads) and returns: self