code: def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None):
      super().__init__()
  
      layers = [
          nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1),
          nn.LeakyReLU(0.2, True),
      ]
      ndf_mult = 1
      ndf_mult_prev = 1
      for n in range(
          1, n_layers
      ):  # gradually increase the number of filters
          ndf_mult_prev = ndf_mult
          ndf_mult = min(2**n, 8)
          layers += [
              nn.Conv2d(
                  ndf * ndf_mult_prev,
                  ndf * ndf_mult,
                  kernel_size=4,
                  stride=2,
                  padding=1,
                  bias=False,
              ),
              nn.BatchNorm2d(ndf * ndf_mult),
              nn.LeakyReLU(0.2, True),
          ]
  
      ndf_mult_prev = ndf_mult
      ndf_mult = min(2**n_layers, 8)
  
      layers += [
          nn.Conv2d(
              ndf * ndf_mult_prev,
              ndf * ndf_mult,
              kernel_size=4,
              stride=1,
              padding=1,
              bias=False,
          ),
          nn.BatchNorm2d(ndf * ndf_mult),
          nn.LeakyReLU(0.2, True),
      ]
  
      layers += [
          nn.Conv2d(
              ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1
          )
      ]  # output 1 channel prediction map
      self.main = nn.Sequential(*layers)
  
      if model_path is not None:
          chkpt = torch.load(model_path, map_location="cpu")
          if "params_d" in chkpt:
              self.load_state_dict(
                  torch.load(model_path, map_location="cpu")["params_d"]
              )
          elif "params" in chkpt:
              self.load_state_dict(
                  torch.load(model_path, map_location="cpu")["params"]
              )
          else:
              raise ValueError("Wrong params!")
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from basicsr.utils import get_root_logger
  from basicsr.utils.registry import ARCH_REGISTRY
label_desc: Use image node_id 1 to create a new VQGANDiscriminator object from inherited base classes: nn with example: obj = VQGANDiscriminator(nc, ndf, n_layers, model_path)