def tie_weights(self):
        """
        Run this to be sure output and input (adaptive) softmax weights are tied
        """

        if self.config.tie_word_embeddings:
            for i in range(len(self.crit.out_layers)):
                self._tie_or_clone_weights(self.crit.out_layers[i], self.transformer.word_emb.emb_layers[i])
        if self.config.tie_projs:
            for i, tie_proj in enumerate(self.config.tie_projs):
                if tie_proj and self.config.div_val == 1 and self.config.d_model != self.config.d_embed:
                    if self.config.torchscript:
                        self.crit.out_projs[i] = nn.Parameter(self.transformer.word_emb.emb_projs[0].clone())
                    else:
                        self.crit.out_projs[i] = self.transformer.word_emb.emb_projs[0]
                elif tie_proj and self.config.div_val != 1:
                    if self.config.torchscript:
                        self.crit.out_projs[i] = nn.Parameter(self.transformer.word_emb.emb_projs[i].clone())
                    else:
                        self.crit.out_projs[i] = self.transformer.word_emb.emb_projs[i]

        Run this to be sure output and input (adaptive) softmax weights are tied
        
dpkd/transformers/src/transformers/models/transfo_xl/modeling_transfo_xl.py