code: def __init__(self, args, dictionary, embed_tokens):
      self.num_tasks = args.encoder_tasks
      self.num_layers = args.encoder_layers
      self.total_num_heads = args.total_encoder_attention_heads
      self.num_heads = args.encoder_attention_heads
      self.select_strategy = args.attn_head_select_strategy
  
      super().__init__(args, dictionary, embed_tokens)
      self.attn_head_selector = AttnHeadSelector(
          self.num_tasks,
          self.num_layers,
          self.total_num_heads,
          self.num_heads,
          self.select_strategy,
      )
      self.task_ids = None
      self.layers = nn.ModuleList(
          [
              self.build_encoder_layer(args, i)
              for i in range(args.encoder_layers)
          ]
      )
  
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 1 to create a new HeadSelectionTransformerEncoder object from inherited base classes: TransformerEncoder with example: obj = HeadSelectionTransformerEncoder(args, dictionary, embed_tokens)