class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()

    def forward(self, input):
        return F.linear(input, torch.randn(out_features, in_features))

In this condition, Model.parameter has no parameter, since
m  = Model ()
len(list(m.parameters())) # returns 0

So, in this condition, is the parameter tensor the size of [in_features, out_features] in linear function learnable?
In BackPropagation , can i use SGD to update parameters in linear funtion?