code: def __init__(
      self,
      period,
      kernel_size=5,
      stride=3,
      use_spectral_norm=False,
      debug=False,
  ):
      super().__init__()
      self.period = period
      norm_f = (
          weight_norm if use_spectral_norm == False else spectral_norm
      )
      conv_ch = [32, 128, 512, 1024] if not debug else [8, 12, 32, 64]
      self.convs = nn.ModuleList(
          [
              norm_f(
                  Conv2d(
                      1,
                      conv_ch[0],
                      (kernel_size, 1),
                      (stride, 1),
                      padding=(get_padding(5, 1), 0),
                  )
              ),
              norm_f(
                  Conv2d(
                      conv_ch[0],
                      conv_ch[1],
                      (kernel_size, 1),
                      (stride, 1),
                      padding=(get_padding(5, 1), 0),
                  )
              ),
              norm_f(
                  Conv2d(
                      conv_ch[1],
                      conv_ch[2],
                      (kernel_size, 1),
                      (stride, 1),
                      padding=(get_padding(5, 1), 0),
                  )
              ),
              norm_f(
                  Conv2d(
                      conv_ch[2],
                      conv_ch[3],
                      (kernel_size, 1),
                      (stride, 1),
                      padding=(get_padding(5, 1), 0),
                  )
              ),
              norm_f(
                  Conv2d(
                      conv_ch[3],
                      conv_ch[3],
                      (kernel_size, 1),
                      1,
                      padding=(2, 0),
                  )
              ),
          ]
      )
      self.conv_post = norm_f(
          Conv2d(conv_ch[3], 1, (3, 1), 1, padding=(1, 0))
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
  from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
  from nemo.core.classes.common import typecheck
  from nemo.core.classes.module import NeuralModule
  from nemo.core.neural_types.elements import AudioSignal, MelSpectrogramType, VoidType
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 1 to create a new DiscriminatorP object from inherited base classes: NeuralModule with example: obj = DiscriminatorP(period, kernel_size, stride, use_spectral_norm, debug)