code: def __init__(
      self, period, kernel_size=5, stride=3, use_spectral_norm=False
  ):
      super(DiscriminatorP, self).__init__()
      self.period = period
      norm_f = weight_norm if not use_spectral_norm else spectral_norm
      self.convs = nn.ModuleList(
          [
              norm_f(
                  Conv2d(
                      1,
                      32,
                      (kernel_size, 1),
                      (stride, 1),
                      padding=(get_padding(5, 1), 0),
                  )
              ),
              norm_f(
                  Conv2d(
                      32,
                      128,
                      (kernel_size, 1),
                      (stride, 1),
                      padding=(get_padding(5, 1), 0),
                  )
              ),
              norm_f(
                  Conv2d(
                      128,
                      512,
                      (kernel_size, 1),
                      (stride, 1),
                      padding=(get_padding(5, 1), 0),
                  )
              ),
              norm_f(
                  Conv2d(
                      512,
                      1024,
                      (kernel_size, 1),
                      (stride, 1),
                      padding=(get_padding(5, 1), 0),
                  )
              ),
              norm_f(
                  Conv2d(
                      1024, 1024, (kernel_size, 1), 1, padding=(2, 0)
                  )
              ),
          ]
      )
      self.conv_post = norm_f(
          Conv2d(1024, 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 .utils import get_padding, init_weights
label_desc: Use image node_id 1 to create a new DiscriminatorP object from inherited base classes: torch.nn with example: obj = DiscriminatorP(period, kernel_size, stride, use_spectral_norm)