def get_rel_pos(self, q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
        """
        Get relative positional embeddings according to the relative positions of
            query and key sizes.

        Args:
            q_size (int):
                size of the query.
            k_size (int):
                size of key k.
            rel_pos (`torch.Tensor`):
                relative position embeddings (L, channel).

        Returns:
            Extracted positional embeddings according to relative positions.
        """
        max_rel_dist = int(2 * max(q_size, k_size) - 1)
        # Interpolate rel pos.
        rel_pos_resized = F.interpolate(
            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
            size=max_rel_dist,
            mode="linear",
        )
        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)

        # Scale the coords with short length if shapes for q and k are different.
        q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
        k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
        relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)

        return rel_pos_resized[relative_coords.long()]

        Get relative positional embeddings according to the relative positions of
            query and key sizes.

        Args:
            q_size (int):
                size of the query.
            k_size (int):
                size of key k.
            rel_pos (`torch.Tensor`):
                relative position embeddings (L, channel).

        Returns:
            Extracted positional embeddings according to relative positions.
        
dpkd/transformers/src/transformers/models/sam/modeling_sam.py