code: def __init__(self, args):
      super().__init__()
  
      self.dropout = args.dropout
      self.embedding_dim = args.encoder_embed_dim
  
      self.pos_conv = nn.Conv1d(
          self.embedding_dim,
          self.embedding_dim,
          kernel_size=args.conv_pos,
          padding=args.conv_pos // 2,
          groups=args.conv_pos_groups,
      )
      dropout = 0
      std = math.sqrt(
          (4 * (1.0 - dropout)) / (args.conv_pos * self.embedding_dim)
      )
      nn.init.normal_(self.pos_conv.weight, mean=0, std=std)
      nn.init.constant_(self.pos_conv.bias, 0)
  
      self.pos_conv = nn.utils.parametrizations.weight_norm(
          self.pos_conv, name="weight", dim=2
      )
      self.pos_conv = nn.Sequential(
          self.pos_conv, SamePad(args.conv_pos), nn.GELU()
      )
  
      if hasattr(args, "relative_position_embedding"):
          self.relative_position_embedding = (
              args.relative_position_embedding
          )
          self.num_buckets = args.num_buckets
          self.max_distance = args.max_distance
      else:
          self.relative_position_embedding = False
          self.num_buckets = 0
          self.max_distance = 0
  
      self.layers = nn.ModuleList(
          [
              TransformerSentenceEncoderLayer(
                  embedding_dim=self.embedding_dim,
                  ffn_embedding_dim=args.encoder_ffn_embed_dim,
                  num_attention_heads=args.encoder_attention_heads,
                  dropout=self.dropout,
                  attention_dropout=args.attention_dropout,
                  activation_dropout=args.activation_dropout,
                  activation_fn=args.activation_fn,
                  layer_norm_first=args.layer_norm_first,
                  has_relative_attention_bias=(
                      self.relative_position_embedding and i == 0
                  ),
                  num_buckets=self.num_buckets,
                  max_distance=self.max_distance,
                  gru_rel_pos=args.gru_rel_pos,
              )
              for i in range(args.encoder_layers)
          ]
      )
  
      self.layer_norm_first = args.layer_norm_first
      self.layer_norm = LayerNorm(self.embedding_dim)
      self.layerdrop = args.encoder_layerdrop
  
      self.apply(init_bert_params)
  
imports: import logging
  import math
  from typing import List, Optional, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import LayerNorm
  from TTS.vc.modules.freevc.wavlm.modules import Fp32GroupNorm, Fp32LayerNorm, GLU_Linear, GradMultiply, MultiheadAttention, SamePad, TransposeLast, get_activation_fn, init_bert_params
label_desc: Use image node_id 1 to create a new TransformerEncoder object from inherited base classes: nn with example: obj = TransformerEncoder(args)