Hi folks, I’d like some help on indexing a 2D tensor. Suppose we have the following tensors

`d = torch.tensor([[118, 175, 1], [118, 188, 0], [ 66, 201, 1], [ 94, 204, 1], [ 94, 206, 0]]) e = torch.tensor([[66, 201, 0.1], [94, 206, 0.2], [1, 23, 0.6], [118, 188, 0.3], [2, 3, 0.1], [3, 1, 0.2], [94, 204, 0.8], [118, 175, 0.7]])`

Tensor `e`

is a set of predictions from a model where the first 2 columns are sort of an ID and tensor `d`

is a set of labels with first 2 columns as an ID. So I have less labels than predictions here and I’d like to only calculate the loss for the real labels. Hence, I need to index tensor `e`

by tensor `d`

's first 2 columns. Is there a way to do this? Or would using a 3D tensor be better?