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I want to use T-1 labels to help predict the Tth label

In theory computing the loss over all of the labels during training should help the model predict the final label at inference time. You can train the model as described above, then for computing accuracy on the valid set, you only consider the final label and the final output of the LSTM.
Alternatively, you could append the t-1th label to the feature vector for xt, and compute the loss for the final LSTM state (or the mean over all LSTM states), passed through the linear layer, with yT. This would mean at t=0 you wouldn’t have a label yt-1, here you could append a 0 (sort of “padding”), or you could simply ignore this input.