That's a good point, for example in Eigen you can do
Eigen::Matrix<float, 10, 5>
I just really want it in Python because that's where I do most of my matrix manipulation nowadays. I guess you also would really like it to handle non-constants. It would be nice if these complicated library functions like
torch.nn.MultiheadAttention(embed_dim, num_heads, dropout=0.0, bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None, batch_first=False, device=None, dtype=None)
would actually typecheck that kdim and vdim are correct, and ensure that I correctly pass a K x V matrix and not a V x K one.
Eigen::Matrix<float, 10, 5>
I just really want it in Python because that's where I do most of my matrix manipulation nowadays. I guess you also would really like it to handle non-constants. It would be nice if these complicated library functions like
torch.nn.MultiheadAttention(embed_dim, num_heads, dropout=0.0, bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None, batch_first=False, device=None, dtype=None)
would actually typecheck that kdim and vdim are correct, and ensure that I correctly pass a K x V matrix and not a V x K one.