Yes of course, my apologies!
My generator code is as follows:
class Generator(nn.Module):
    def __init__(self, ngpu, b_val):
        super(Generator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # input is Z, going into a convolution
            nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(True),
            # state size. (ngf*8) x 4 x 4
            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),
            # state size. (ngf*4) x 8 x 8
            nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(True),
            # state size. (ngf*2) x 16 x 16
            nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf),
            ShiftLayer(b_val),
            nn.ReLU(True),
            # state size. (ngf) x 32 x 32
            nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False),
            nn.Tanh()
            # state size. (nc) x 64 x 64
        )
    
    def forward(self, input):
        return self.main(input)

The layer called ShiftLayer is defined as:
class ShiftLayer(nn.Module):
    def __init__(self, b):
        super().__init__()
        self.b = nn.Parameter(torch.tensor(1.))
        
    def forward(self, x):
        return x + self.b

During training, I have an optimizer for the generator. Since b is a parameter defined within the generator, when I execute
optimizerG = optim.Adam(generator.parameters(), lr=lr, betas=(beta1, 0.999))
b is returned as one of these generator.parameters(). I want to create a separate optimizer for optimizing b  so that I can do something like:
optimizerb = optim.SGD(ShiftLayer.parameters(), . . .)

How can I do this?
(Sorry about the long post, I am not sure how to make it more concise without getting too abstract)
@ptrblck sorry for the tag, would you have any idea?