code: def gan_critic(
      n_channels: int = 3,  # Number of channels of the input for the critic
      nf: int = 128,  # Number of features for the critic
      n_blocks: int = 3,  # Number of ResNet blocks within the critic
      p: float = 0.15,  # Amount of dropout in the critic
  ) -> nn.Sequential:
      "Critic to train a `GAN`."
      layers = [
          _conv(n_channels, nf, ks=4, stride=2),
          nn.Dropout2d(p / 2),
          DenseResBlock(nf, **_conv_args),
      ]
      nf *= 2  # after dense block
      for i in range(n_blocks):
          layers += [
              nn.Dropout2d(p),
              _conv(
                  nf, nf * 2, ks=4, stride=2, self_attention=(i == 0)
              ),
          ]
          nf *= 2
      layers += [
          ConvLayer(
              nf,
              1,
              ks=4,
              bias=False,
              padding=0,
              norm_type=NormType.Spectral,
              act_cls=None,
          ),
          Flatten(),
      ]
      return nn.Sequential(*layers)
  
imports: from __future__ import annotations
  from ..basics import *
  from .all import *
label_desc: Use image node_id 5 for calling a global function with example usage: gan_critic(n_channels, nf, n_blocks, p) and returns: nn