Transform 2D embeddings into 3D embeddings

Question

I want to fuse node embeddings from a GNN shaped {[N, 128], where N can vary, with hidden state from an LSTM shaped [2, 32, 128]. To do so, I wanted to add both feature tensors, but they need to be the same shape. I have something like:

N = 75
t = torch.rand(N, 128)
t.unsqueeze_(0)
t = t.expand(2,N, 128)

I do not seem to figure out how to make dimension 1 equal to 32. I am looking for some combination of transforms and learnable layers that can help me make this shape conversion. Thanks in advance for any help you can give.