# Generator Code

class Generator([nn.Module](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module "torch.nn.Module")):
    def __init__(self, ngpu):
        super([Generator](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 Z, going into a convolution
            [nn.ConvTranspose2d](https://docs.pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html#torch.nn.ConvTranspose2d "torch.nn.ConvTranspose2d")( nz, ngf * 8, 4, 1, 0, bias=False),
            [nn.BatchNorm2d](https://docs.pytorch.org/docs/stable/generated/torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d "torch.nn.BatchNorm2d")(ngf * 8),
            [nn.ReLU](https://docs.pytorch.org/docs/stable/generated/torch.nn.ReLU.html#torch.nn.ReLU "torch.nn.ReLU")(True),
            # state size. ``(ngf*8) x 4 x 4``
            [nn.ConvTranspose2d](https://docs.pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html#torch.nn.ConvTranspose2d "torch.nn.ConvTranspose2d")(ngf * 8, ngf * 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")(ngf * 4),
            [nn.ReLU](https://docs.pytorch.org/docs/stable/generated/torch.nn.ReLU.html#torch.nn.ReLU "torch.nn.ReLU")(True),
            # state size. ``(ngf*4) x 8 x 8``
            [nn.ConvTranspose2d](https://docs.pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html#torch.nn.ConvTranspose2d "torch.nn.ConvTranspose2d")( ngf * 4, ngf * 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")(ngf * 2),
            [nn.ReLU](https://docs.pytorch.org/docs/stable/generated/torch.nn.ReLU.html#torch.nn.ReLU "torch.nn.ReLU")(True),
            # state size. ``(ngf*2) x 16 x 16``
            [nn.ConvTranspose2d](https://docs.pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html#torch.nn.ConvTranspose2d "torch.nn.ConvTranspose2d")( ngf * 2, ngf, 4, 2, 1, bias=False),
            [nn.BatchNorm2d](https://docs.pytorch.org/docs/stable/generated/torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d "torch.nn.BatchNorm2d")(ngf),
            [nn.ReLU](https://docs.pytorch.org/docs/stable/generated/torch.nn.ReLU.html#torch.nn.ReLU "torch.nn.ReLU")(True),
            # state size. ``(ngf) x 32 x 32``
            [nn.ConvTranspose2d](https://docs.pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html#torch.nn.ConvTranspose2d "torch.nn.ConvTranspose2d")( ngf, nc, 4, 2, 1, bias=False),
            [nn.Tanh](https://docs.pytorch.org/docs/stable/generated/torch.nn.Tanh.html#torch.nn.Tanh "torch.nn.Tanh")()
            # state size. ``(nc) x 64 x 64``
        )

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