class_docstr: HiFiGAN Multi-Scale Discriminator.
  It is similar to `MultiScaleMelganDiscriminator` but specially tailored for HiFiGAN as in the paper.
docstr: Args:
      x (Tensor): input waveform.
  
  Returns:
      List[Tensor]: discriminator scores.
      List[List[Tensor]]: list of list of features from each layers of each discriminator.
docstr_tok: ["Args",":","x","(","Tensor",")",":","input","waveform",".","Returns",":","List","[","Tensor","]",":","discriminator","scores",".","List","[","List","[","Tensor","]","]",":","list","of","list","of","features","from","each","layers","of","each","discriminator","."]
code: def forward(self, x):
      """
      Args:
          x (Tensor): input waveform.
  
      Returns:
          List[Tensor]: discriminator scores.
          List[List[Tensor]]: list of list of features from each layers of each discriminator.
      """
      scores = []
      feats = []
      for i, d in enumerate(self.discriminators):
          if i != 0:
              x = self.meanpools[i - 1](x)
          score, feat = d(x)
          scores.append(score)
          feats.append(feat)
      return scores, feats
  
imports: import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the MultiScaleDiscriminator obj's underlying member method code with example usage: obj.forward(x) and returns: scores,  feats