class Discriminator([nn.Module](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module "torch.nn.Module")):
    def __init__(self, ngpu):
        super([Discriminator](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module "torch.nn.Module"), self).__init__()
        self.ngpu = ngpu
        self.main = [nn.Sequential](https://docs.pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential "torch.nn.Sequential")(
            # input is ``(nc) x 64 x 64``
            [nn.Conv2d](https://docs.pytorch.org/docs/stable/generated/torch.nn.Conv2d.html#torch.nn.Conv2d "torch.nn.Conv2d")(nc, ndf, 4, 2, 1, bias=False),
            [nn.LeakyReLU](https://docs.pytorch.org/docs/stable/generated/torch.nn.LeakyReLU.html#torch.nn.LeakyReLU "torch.nn.LeakyReLU")(0.2, inplace=True),
            # state size. ``(ndf) x 32 x 32``
            [nn.Conv2d](https://docs.pytorch.org/docs/stable/generated/torch.nn.Conv2d.html#torch.nn.Conv2d "torch.nn.Conv2d")(ndf, ndf * 2, 4, 2, 1, bias=False),
            [nn.BatchNorm2d](https://docs.pytorch.org/docs/stable/generated/torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d "torch.nn.BatchNorm2d")(ndf * 2),
            [nn.LeakyReLU](https://docs.pytorch.org/docs/stable/generated/torch.nn.LeakyReLU.html#torch.nn.LeakyReLU "torch.nn.LeakyReLU")(0.2, inplace=True),
            # state size. ``(ndf*2) x 16 x 16``
            [nn.Conv2d](https://docs.pytorch.org/docs/stable/generated/torch.nn.Conv2d.html#torch.nn.Conv2d "torch.nn.Conv2d")(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
            [nn.BatchNorm2d](https://docs.pytorch.org/docs/stable/generated/torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d "torch.nn.BatchNorm2d")(ndf * 4),
            [nn.LeakyReLU](https://docs.pytorch.org/docs/stable/generated/torch.nn.LeakyReLU.html#torch.nn.LeakyReLU "torch.nn.LeakyReLU")(0.2, inplace=True),
            # state size. ``(ndf*4) x 8 x 8``
            [nn.Conv2d](https://docs.pytorch.org/docs/stable/generated/torch.nn.Conv2d.html#torch.nn.Conv2d "torch.nn.Conv2d")(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
            [nn.BatchNorm2d](https://docs.pytorch.org/docs/stable/generated/torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d "torch.nn.BatchNorm2d")(ndf * 8),
            [nn.LeakyReLU](https://docs.pytorch.org/docs/stable/generated/torch.nn.LeakyReLU.html#torch.nn.LeakyReLU "torch.nn.LeakyReLU")(0.2, inplace=True),
            # state size. ``(ndf*8) x 4 x 4``
            [nn.Conv2d](https://docs.pytorch.org/docs/stable/generated/torch.nn.Conv2d.html#torch.nn.Conv2d "torch.nn.Conv2d")(ndf * 8, 1, 4, 1, 0, bias=False),
            [nn.Sigmoid](https://docs.pytorch.org/docs/stable/generated/torch.nn.Sigmoid.html#torch.nn.Sigmoid "torch.nn.Sigmoid")()
        )

    def forward(self, input):
        return self.main(input)