docstr: Perform forward pass through the base frequency discriminator.
  
  Args:
      x (torch.Tensor): Input tensor of shape
                        (B, in_channels, freq_bins, time_steps).
  
  Returns:
      List[torch.Tensor]: List of output tensors from each layer of the
                          discriminator, where the first tensor corresponds to
                          the output of the first layer, and so on.
code: def forward(self, x):
      """Perform forward pass through the base frequency discriminator.
  
      Args:
          x (torch.Tensor): Input tensor of shape
                            (B, in_channels, freq_bins, time_steps).
  
      Returns:
          List[torch.Tensor]: List of output tensors from each layer of the
                              discriminator, where the first tensor corresponds to
                              the output of the first layer, and so on.
      """
  
      outs = []
      for f in self.discriminators:
          x = f(x)
          outs = outs + [x]
  
      return outs
  
imports: import logging
  import math
  from typing import Any, Dict, List, Optional
  import numpy
  import torch
  import torch.nn.functional
  from espnet2.gan_svs.visinger2.ddsp import remove_above_nyquist, scale_function, upsample
  from espnet2.gan_tts.hifigan import HiFiGANMultiPeriodDiscriminator, HiFiGANMultiScaleDiscriminator, HiFiGANMultiScaleMultiPeriodDiscriminator, HiFiGANPeriodDiscriminator, HiFiGANScaleDiscriminator
  from espnet2.gan_tts.hifigan.residual_block import ResidualBlock
label_desc: Use image node_id 2 for calling the BaseFrequenceDiscriminator obj's underlying member method code with example usage: obj.forward(x) and returns: outs