class_docstr: HiFi-GAN scale discriminator module.
docstr: Calculate forward propagation.
  
  Args:
      x (Tensor): Input noise signal (B, 1, T).
  
  Returns:
      List[Tensor]: List of output tensors of each layer.
docstr_tok: ["Calculate","forward","propagation",".","Args",":","x","(","Tensor",")",":","Input","noise","signal","(","B",",","1",",","T",")",".","Returns",":","List","[","Tensor","]",":","List","of","output","tensors","of","each","layer","."]
code: def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
      """Calculate forward propagation.
  
      Args:
          x (Tensor): Input noise signal (B, 1, T).
  
      Returns:
          List[Tensor]: List of output tensors of each layer.
  
      """
      outs = []
      for f in self.layers:
          x = f(x)
          outs += [x]
  
      return outs
  
imports: import copy
  import logging
  from typing import Any, Dict, List, Optional
  import numpy
  import torch
  import torch.nn.functional
  from espnet2.gan_tts.hifigan.residual_block import ResidualBlock
label_desc: Use image node_id 2 for calling the HiFiGANScaleDiscriminator obj's underlying member method code with example usage: obj.forward(x) and returns: outs