class_docstr: docstring for Discriminator.
class_docstr_tok: ["docstring","for","Discriminator","."]
code: def forward(self, y):
      fmap = []
      with torch.no_grad():
          y = y.squeeze(1)
          y = self.stft(y)
      y = y.unsqueeze(1)
      for _, d in enumerate(self.discriminators):
          y = d(y)
          y = F.leaky_relu(y, LRELU_SLOPE)
          fmap.append(y)
  
      y = self.out(y)
      fmap.append(y)
  
      return torch.flatten(y, 1, -1), fmap
  
file_path: TTS/TTS/vocoder/models/univnet_discriminator.py
imports: import torch
  import torch.nn.functional
  from torch import nn
  from torch.nn.utils import spectral_norm
  from torch.nn.utils.parametrizations import weight_norm
  from TTS.utils.audio.torch_transforms import TorchSTFT
  from TTS.vocoder.models.hifigan_discriminator import MultiPeriodDiscriminator
label_desc: Use image node_id 2 for calling the SpecDiscriminator obj's underlying member method code with example usage: obj.forward(y) and returns: torch,  fmap