code: def operator_flop(
      cls,
      grad,
      query,
      key,
      value,
      out,
      lse,
      dq,
      dk,
      dv,
      cu_seq_lens_q,
      cu_seq_lens_k,
      max_seq_len_q,
      max_seq_len_k,
      p,
      softmax_scale,
      causal,
  ) -> int:
      return cls.attn_operator_flop(
          query.unsqueeze(0),
          key.unsqueeze(0),
          value.unsqueeze(0),
          causal=causal,
          seqstart_k=cu_seq_lens_k,
          seqstart_q=cu_seq_lens_q,
      )
  
imports: import os
  from dataclasses import replace
  from itertools import zip_longest
  from typing import Any, List, Optional, Set, Tuple, Union
  import torch
  from ..common import _get_storage_base, get_operator, register_operator
  from .attn_bias import AttentionBias, BlockDiagonalCausalFromBottomRightMask, BlockDiagonalCausalLocalAttentionFromBottomRightMask, BlockDiagonalCausalLocalAttentionMask, BlockDiagonalCausalMask, BlockDiagonalCausalWithOffsetPaddedKeysMask, BlockDiagonalMask, LocalAttentionFromBottomRightMask, LowerTriangularFromBottomRightLocalAttentionMask, LowerTriangularFromBottomRightMask, LowerTriangularMask
  from .common import AttentionBwOpBase, AttentionFwOpBase, Context, Gradients, Inputs, check_lastdim_alignment_stride1
label_desc: Use image node_id 4 for calling the BwOp obj's underlying member method code with example usage: obj.operator_flop(cls, grad, query, key, value, out, lse, dq, dk, dv, cu_seq_lens_q, cu_seq_lens_k, max_seq_len_q, max_seq_len_k, p, softmax_scale, causal) and returns: cls