class_docstr: A PyTorch module for the Style Token Layer (STL) as described in
  "A Style-Based Generator Architecture for Generative Adversarial Networks"
  (https://arxiv.org/abs/1812.04948)
  
  The STL applies a multi-headed attention mechanism over the learned style tokens,
  using the text input as the query and the style tokens as the keys and values.
  The output of the attention mechanism is used as the text's style embedding.
  
  Args:
      token_num (int): The number of style tokens.
      n_hidden (int): Number of hidden dimensions.
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      N = x.size(0)
      query = x.unsqueeze(1)  # [N, 1, E//2]
  
      keys_soft = (
          torch.tanh(self.embed).unsqueeze(0).expand(N, -1, -1)
      )  # [N, token_num, E // num_heads]
  
      # Weighted sum
      emotion_embed_soft = self.attention(query, keys_soft)
  
      return emotion_embed_soft
  
imports: import math
  from typing import Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from TTS.tts.layers.delightful_tts.conv_layers import ConvNorm
label_desc: Use image node_id 2 for calling the STL obj's underlying member method code with example usage: obj.forward(x) and returns: emotion_embed_soft