code: def __init__(self, cfg: Data2VecTextConfig, dictionary, task_data):
      super().__init__(dictionary)
  
      self.cfg = cfg
  
      embed_tokens = self.build_embedding(
          len(dictionary),
          cfg.transformer.encoder.embed_dim,
          dictionary.pad(),
      )
  
      self.sentence_encoder = self.build_encoder(
          cfg, dictionary, embed_tokens
      )
      self.mask_idx = dictionary.index("<mask>")
      assert self.mask_idx != dictionary.unk(), dictionary.symbols
  
      self.ema = None
      self.average_top_k_layers = cfg.average_top_k_layers
      self.loss_scale = cfg.loss_scale
  
      assert self.cfg.head_layers >= 1
  
      embed_dim = cfg.transformer.encoder.embed_dim
      curr_dim = embed_dim
      projs = []
      for i in range(self.cfg.head_layers - 1):
          next_dim = embed_dim * 2 if i == 0 else curr_dim
          projs.append(nn.Linear(curr_dim, next_dim))
          projs.append(nn.GELU())
          curr_dim = next_dim
  
      projs.append(nn.Linear(curr_dim, embed_dim))
      self.regression_head = nn.Sequential(*projs)
  
      self.num_updates = 0
  
imports: from dataclasses import dataclass, field
  from typing import Optional
  import logging
  import math
  import torch
  import torch.nn
  import torch.nn.functional
  from omegaconf import II
  from fairseq.dataclass import FairseqDataclass
  from fairseq.modules import EMAModule, EMAModuleConfig
  from fairseq.models import FairseqEncoder, FairseqEncoderModel, register_model
  from fairseq.models.roberta.model import RobertaLMHead, RobertaClassificationHead
  from fairseq.models.transformer import TransformerEncoder, TransformerConfig
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
label_desc: Use image node_id 1 to create a new Data2VecTextEncoder object from inherited base classes: FairseqEncoder with example: obj = Data2VecTextEncoder(cfg, dictionary, task_data)