code: def gen_relative_positions(
      length: int,
      max_relative_positions: int,
      cache: bool = False,
      device: Optional[torch.device] = None,
  ) -> Tensor:
      """Generate the clipped relative positions matrix
      for a given length and maximum relative positions"""
      if cache:
          distance_mat = torch.arange(
              -length + 1, 1, 1, device=device
          ).unsqueeze(0)
      else:
          range_vec = torch.arange(length, device=device)
          range_mat = (
              range_vec.unsqueeze(-1).expand(-1, length).transpose(0, 1)
          )
          distance_mat = range_mat - range_mat.transpose(0, 1)
      distance_mat_clipped = torch.clamp(
          distance_mat,
          min=-max_relative_positions,
          max=max_relative_positions,
      )
      # Shift values to be >= 0
      final_mat = distance_mat_clipped + max_relative_positions
      return final_mat
  
imports: import torch
  import torch.nn
  from math import log, sqrt
  from torch import Tensor
  from typing import Optional, Tuple
  from torch.nn.functional import scaled_dot_product_attention
  from torch.utils.checkpoint import checkpoint
  from torch.nn.utils import skip_init
  from .alibi_position_bias import AlibiPositionalBias
  from torch.distributed import all_reduce
  from importlib import import_module
label_desc: Use image node_id 5 for calling a global function with example usage: gen_relative_positions(length, max_relative_positions, cache, device) and returns: final_mat