Hi, I am new to PyTorch. I was going through the DCGAN code in pytorch.

class Generator(nn.Module):
    def __init__(self, ngpu):
        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),#(ic,oc,kernel,stride,padding)
            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 = 256) 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 = 128) x 16 x 16
            nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(True),
            # state size. (ngf = 64) 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)
    
netG = Generator(ngpu).to(device)
netG.apply(weights_init)
print(netG)

fixed_noise = torch.randn(64,nz,1,1, device=device)
netG(fixed_noise.detach())


What I wanted to ask is how does the generator knows how many batches of images to generate? The code only specifies the numbers of input and output channels for the layers, there is no batch size specified in the generator class. is it because the input to the generator must be a tensor of [Batch_size, Channels, height, width] ? I am a bit confused. Thanks in advance