Thank you for your reply, @G.M.
So, in model I should add
class Model(nn.Module):
    def __init__(self, batch_size):
        super(Model, self).__init__()
        shape = torch.Size((batch_size, 3, 32, 32))
        scalar = torch.FloatTensor(shape)
        self.multp = nn.Parameter(torch.randn(shape, out=scalar))

and in training should add
def train(epoch):
     for batch_idx, (inputs, targets) in enumerate(trainloader):
            if use_cuda:
                inputs, targets = inputs.cuda(), targets.cuda()
 
            M1 = net.parameters()
            inputs_new = inputs * M1
            outputs = net(inputs_new)

If this is correct, how I can give batch_size to the network in every epoch ? since I am defining net = models.__dict__[args.model](num_classes) before training function and I am considering batch_size as batch_size = inputs.size(0). I should bring net inside the training function?