def get_log_prob(self, input, target):
        """
        Computes the log probabilities for all the words of the vocabulary,
        given a 2D tensor of hidden vectors.
        """

        bsz, length, dim = input.size()
        input = input.contiguous().view(-1, dim)

        if target is not None:
            _, target_idxs = self.adapt_target(target)
        else:
            target_idxs = None

        head_y = self.head(input)
        log_probs = head_y.new_zeros(input.size(0), self.vocab_size)

        head_sz = self.cutoff[0] + len(self.tail)
        log_probs[:, :head_sz] = self.lsm(head_y)
        tail_priors = log_probs[:, self.cutoff[0] : head_sz].clone()

        for i in range(len(self.tail)):
            start = self.cutoff[i]
            end = self.cutoff[i + 1]

            if target_idxs is None:
                tail_out = log_probs[:, start:end]
                tail_out.copy_(self.tail[i](input))
                log_probs[:, start:end] = self.lsm(tail_out).add_(
                    tail_priors[:, i, None]
                )
            elif target_idxs[i] is not None:
                idxs = target_idxs[i]
                tail_out = log_probs[idxs, start:end]
                tail_out.copy_(self.tail[i](input[idxs]))
                log_probs[idxs, start:end] = self.lsm(tail_out).add_(
                    tail_priors[idxs, i, None]
                )

        log_probs = log_probs.view(bsz, length, -1)
        return log_probs

        Computes the log probabilities for all the words of the vocabulary,
        given a 2D tensor of hidden vectors.
        
understand_icl/fairseq/fairseq/modules/adaptive_softmax.py