def resize_position_embeddings(self, new_num_position_embeddings: int):
        """
        Resizes position embeddings of the model if `new_num_position_embeddings != config.max_position_embeddings`.

        Arguments:
            new_num_position_embeddings (`int`):
                The number of new position embedding matrix. If position embeddings are learned, increasing the size
                will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the
                end. If position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the
                size will add correct vectors at the end following the position encoding algorithm, whereas reducing
                the size will remove vectors from the end.
        """
        num_position_embeds_diff = new_num_position_embeddings - self.config.max_position_embeddings

        # no resizing needs to be done if the length stays the same
        if num_position_embeds_diff == 0:
            return

        logger.info(f"Setting `config.max_position_embeddings={new_num_position_embeddings}`...")
        self.config.max_position_embeddings = new_num_position_embeddings

        old_position_embeddings_weight = self.embeddings.position_embeddings.weight.clone()

        self.embeddings.position_embeddings = nn.Embedding(self.config.max_position_embeddings, self.config.dim)

        if self.config.sinusoidal_pos_embds:
            create_sinusoidal_embeddings(
                n_pos=self.config.max_position_embeddings, dim=self.config.dim, out=self.position_embeddings.weight
            )
        else:
            with torch.no_grad():
                if num_position_embeds_diff > 0:
                    self.embeddings.position_embeddings.weight[:-num_position_embeds_diff] = nn.Parameter(
                        old_position_embeddings_weight
                    )
                else:
                    self.embeddings.position_embeddings.weight = nn.Parameter(
                        old_position_embeddings_weight[:num_position_embeds_diff]
                    )
        # move position_embeddings to correct device
        self.embeddings.position_embeddings.to(self.device)

        Resizes position embeddings of the model if `new_num_position_embeddings != config.max_position_embeddings`.

        Arguments:
            new_num_position_embeddings (`int`):
                The number of new position embedding matrix. If position embeddings are learned, increasing the size
                will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the
                end. If position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the
                size will add correct vectors at the end following the position encoding algorithm, whereas reducing
                the size will remove vectors from the end.
        
dpkd/transformers/src/transformers/models/distilbert/modeling_distilbert.py