docstr: Args:
      in_channels (int): Number of input channels.
      hidden_channels (int, optional): Number of channels in hidden layers.
                                       Defaults to 512.
      divisors (List[int], optional): List of divisors for the number of channels
                                      in each layer. The length of the list
                                      determines the number of layers. Defaults
                                      to [32, 16, 8, 4, 2, 1, 1].
      strides (List[int], optional): List of stride values for each layer. The
                                     length of the list determines the number
                                     of layers.Defaults to [1, 2, 1, 2, 1, 2, 1].
code: def __init__(
      self,
      in_channels,
      hidden_channels=512,
      divisors=[32, 16, 8, 4, 2, 1, 1],
      strides=[1, 2, 1, 2, 1, 2, 1],
  ):
      """
      Args:
          in_channels (int): Number of input channels.
          hidden_channels (int, optional): Number of channels in hidden layers.
                                           Defaults to 512.
          divisors (List[int], optional): List of divisors for the number of channels
                                          in each layer. The length of the list
                                          determines the number of layers. Defaults
                                          to [32, 16, 8, 4, 2, 1, 1].
          strides (List[int], optional): List of stride values for each layer. The
                                         length of the list determines the number
                                         of layers.Defaults to [1, 2, 1, 2, 1, 2, 1].
      """
  
      super().__init__()
  
      layers = []
      for i in range(len(divisors) - 1):
          in_ch = (
              in_channels
              if i == 0
              else hidden_channels // divisors[i - 1]
          )
          out_ch = hidden_channels // divisors[i]
          stride = strides[i]
          layers.append((in_ch, out_ch, stride))
      layers.append((hidden_channels // divisors[-1], 1, strides[-1]))
      self.discriminators = torch.nn.ModuleList()
  
      for in_ch, out_ch, stride in layers:
          seq = torch.nn.Sequential(
              torch.nn.LeakyReLU(0.2, True)
              if out_ch != 1
              else torch.nn.Identity(),
              torch.nn.ReflectionPad2d((1, 1, 1, 1)),
              torch.nn.utils.weight_norm(
                  torch.nn.Conv2d(
                      in_ch,
                      out_ch,
                      kernel_size=(3, 3),
                      stride=(stride, stride),
                  )
              ),
          )
          self.discriminators += [seq]
  
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 1 to create a new BaseFrequenceDiscriminator object from inherited base classes: torch.nn with example: obj = BaseFrequenceDiscriminator(in_channels, hidden_channels, divisors, strides)