def get_pointwise_loss(self, inputs: List[str], tok):
    self.model.eval()
    all_probs = []
    with torch.inference_mode():
        for input in inputs:
            ids_list: List[int] = tok.encode(input).ids
            # ids has shape (1, len(ids_list))
            ids: Torch.Tensor = torch.tensor(ids_list, device=self.device).unsqueeze(0)

            # probs below is the probability that the token at that location
            # completes the sentence (in ids) so far.
            y = self.model(ids)
            criterion = nn.CrossEntropyLoss(reduction='none', ignore_index=0)

            # Compute the loss starting from the 2nd token in the model's output.
            loss = criterion(y[:,:,:-1], ids[:,1:])

            # To compute the probability of each token, we need to compute the
            # negative of the log loss and exponentiate it.
            loss = loss * -1.0

            # Set the probabilities that we are not interested in to -inf.
            # This is done to make the softmax set these values to 0.0
            loss[loss == 0.0] = float("-inf")

            # probs holds the probability of each token's prediction
            # starting from the 2nd token since we don't want to include
            # the probability of the model predicting the beginning of
            # a sentence given no existing sentence context.
            #
            # To compute perplexity, we should probably ignore the first
            # handful of predictions of the model since there's insufficient
            # context. We don't do that here, though.
            probs = loss.exp()
            all_probs.append(probs)
        #
    #
    return all_probs
#