code: def forward(
      self,
      prev_output_tokens,
      encoder_out: Optional[Dict[str, List[Tensor]]] = None,
      incremental_state: Optional[
          Dict[str, Dict[str, Optional[Tensor]]]
      ] = None,
      features_only: bool = False,
      full_context_alignment: bool = False,
      alignment_layer: Optional[int] = None,
      alignment_heads: Optional[int] = None,
      src_lengths: Optional[Any] = None,
      return_all_hiddens: bool = False,
  ):
      if self.self_attn_head_selector is not None:
          self.self_attn_head_selector.head_select(self.task_ids)
      if self.enc_attn_head_selector is not None:
          self.enc_attn_head_selector.head_select(self.task_ids)
      return super().forward(
          prev_output_tokens=prev_output_tokens,
          encoder_out=encoder_out,
          incremental_state=incremental_state,
          features_only=features_only,
          full_context_alignment=full_context_alignment,
          alignment_layer=alignment_layer,
          alignment_heads=alignment_heads,
          src_lengths=src_lengths,
          return_all_hiddens=return_all_hiddens,
      )
  
imports: from typing import Any, List, Dict, Optional
  import torch
  import torch.nn
  from torch import Tensor
  from fairseq.utils import safe_hasattr
  from fairseq.models.transformer import TransformerModel, TransformerEncoder, TransformerDecoder
  from ..modules.attn_head_selector import AttnHeadSelector
  from ..modules.head_selection_transformer_layer import HeadSelectionTransformerEncoderLayer, HeadSelectionTransformerDecoderLayer
label_desc: Use image node_id 4 for calling the HeadSelectionTransformerDecoder obj's underlying member method code with example usage: obj.forward(prev_output_tokens, encoder_out, incremental_state, features_only, full_context_alignment, alignment_layer, alignment_heads, src_lengths, return_all_hiddens) and returns: super