How to make nn.Linear be y=Wx+b?

When I read the document the description of nn.Linear() is implemented as y = xA^T + b. However, I need y=Wx+b. Is there anyway to get y=Wx+b with nn.Linear()?

nn.Linear does exactly y=Wx+b.
In the documentation, A is a matrix and A^T denotes A-transpose.
This is due to how the data is passed through the network.

nn.Linear accepts inputs of size (B, D_in) and transforms it into output of size (B, D_out) by a linear transformation using the weight matrix of size (D_out, D_in).

Thanks for the answer. Actually I already solve this problem. Previously I had a misunderstanding of this implementation. I only treat my input vector as a column vector, so I need a left multiply weight matrix. However, I can use a row vector instead so right multiply a matrix then I can still get a new row vector.

m = nn.Linear(20, 30)
input = torch.randn(128, 20)
output = m(input)
  1. W: (30,20), x should transpose to(20,128) so Wx = (30,20)*(20,128)=(30,128)
  2. xW^T = (128,20)*(20,30)=(128,30)
    Wx and xW^T are the same result, but Wx is by column and xW^T is by row? Thanks.