code: def attn_bias_from_alibi_slopes(
      slopes,
      seqlen_q,
      seqlen_k,
      query_padding_mask=None,
      key_padding_mask=None,
      causal=False,
  ):
      batch, nheads = slopes.shape
      device = slopes.device
      slopes = rearrange(slopes, "b h -> b h 1 1")
      if causal:
          return (
              torch.arange(
                  -seqlen_k + 1, 1, device=device, dtype=torch.float32
              )
              * slopes
          )
      else:
          row_idx = rearrange(
              torch.arange(seqlen_q, device=device, dtype=torch.long),
              "s -> s 1",
          )
          col_idx = torch.arange(
              seqlen_k, device=device, dtype=torch.long
          )
          sk = (
              seqlen_k
              if key_padding_mask is None
              else rearrange(key_padding_mask.sum(-1), "b -> b 1 1 1")
          )
          sq = (
              seqlen_q
              if query_padding_mask is None
              else rearrange(query_padding_mask.sum(-1), "b -> b 1 1 1")
          )
          relative_pos = torch.abs(row_idx + sk - sq - col_idx)
          return -slopes * relative_pos.to(dtype=slopes.dtype)
  
imports: import math
  import pytest
  import torch
  import torch.nn.functional
  from einops import rearrange, repeat
  from flash_attn import flash_attn_func, flash_attn_kvpacked_func, flash_attn_qkvpacked_func, flash_attn_varlen_func, flash_attn_varlen_kvpacked_func, flash_attn_varlen_qkvpacked_func, flash_attn_with_kvcache
  from flash_attn.bert_padding import pad_input, unpad_input
  from flash_attn.flash_attn_interface import _get_block_size_n
  from flash_attn.layers.rotary import apply_rotary_emb
label_desc: Use image node_id 1 for calling a global function with example usage: attn_bias_from_alibi_slopes(slopes, seqlen_q, seqlen_k, query_padding_mask, key_padding_mask, causal) and returns: unknown, unknown