docstr: Text Encoder for VITS model.
  
  Args:
      n_vocab (int): Number of characters for the embedding layer.
      out_channels (int): Number of channels for the output.
      hidden_channels (int): Number of channels for the hidden layers.
      hidden_channels_ffn (int): Number of channels for the convolutional layers.
      num_heads (int): Number of attention heads for the Transformer layers.
      num_layers (int): Number of Transformer layers.
      kernel_size (int): Kernel size for the FFN layers in Transformer network.
      dropout_p (float): Dropout rate for the Transformer layers.
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code: def __init__(
      self,
      n_vocab: int,
      out_channels: int,
      hidden_channels: int,
      hidden_channels_ffn: int,
      num_heads: int,
      num_layers: int,
      kernel_size: int,
      dropout_p: float,
      language_emb_dim: int = None,
  ):
      """Text Encoder for VITS model.
  
      Args:
          n_vocab (int): Number of characters for the embedding layer.
          out_channels (int): Number of channels for the output.
          hidden_channels (int): Number of channels for the hidden layers.
          hidden_channels_ffn (int): Number of channels for the convolutional layers.
          num_heads (int): Number of attention heads for the Transformer layers.
          num_layers (int): Number of Transformer layers.
          kernel_size (int): Kernel size for the FFN layers in Transformer network.
          dropout_p (float): Dropout rate for the Transformer layers.
      """
      super().__init__()
      self.out_channels = out_channels
      self.hidden_channels = hidden_channels
  
      self.emb = nn.Embedding(n_vocab, hidden_channels)
  
      nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
  
      if language_emb_dim:
          hidden_channels += language_emb_dim
  
      self.encoder = RelativePositionTransformer(
          in_channels=hidden_channels,
          out_channels=hidden_channels,
          hidden_channels=hidden_channels,
          hidden_channels_ffn=hidden_channels_ffn,
          num_heads=num_heads,
          num_layers=num_layers,
          kernel_size=kernel_size,
          dropout_p=dropout_p,
          layer_norm_type="2",
          rel_attn_window_size=4,
      )
  
      self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
  
imports: import math
  import torch
  from torch import nn
  from TTS.tts.layers.glow_tts.glow import WN
  from TTS.tts.layers.glow_tts.transformer import RelativePositionTransformer
  from TTS.tts.utils.helpers import sequence_mask
label_desc: Use image node_id 1 to create a new TextEncoder object from inherited base classes: nn with example: obj = TextEncoder(n_vocab, out_channels, hidden_channels, hidden_channels_ffn, num_heads, num_layers, kernel_size, dropout_p, language_emb_dim)