code: def backward(ctx, do):
      q, k, v, o, M = ctx.saved_tensors
      assert do.is_contiguous()
      assert (
          q.stride()
          == k.stride()
          == v.stride()
          == o.stride()
          == do.stride()
      )
      dq = torch.empty_like(q)
      dk = torch.empty_like(k)
      dv = torch.empty_like(v)
      BATCH, N_HEAD, N_CTX = q.shape[:3]
      PRE_BLOCK = 128
      NUM_WARPS, NUM_STAGES = 4, 5
      BLOCK_M1, BLOCK_N1, BLOCK_M2, BLOCK_N2 = 32, 128, 128, 32
      BLK_SLICE_FACTOR = 2
      RCP_LN2 = 1.4426950408889634  # = 1.0 / ln(2)
      arg_k = k
      arg_k = arg_k * (ctx.sm_scale * RCP_LN2)
      PRE_BLOCK = 128
      assert N_CTX % PRE_BLOCK == 0
      pre_grid = (N_CTX // PRE_BLOCK, BATCH * N_HEAD)
      delta = torch.empty_like(M)
      _attn_bwd_preprocess[pre_grid](
          o,
          do,  #
          delta,  #
          BATCH,
          N_HEAD,
          N_CTX,  #
          BLOCK_M=PRE_BLOCK,
          D_HEAD=ctx.BLOCK_DMODEL,  #
      )
      grid = (N_CTX // BLOCK_N1, 1, BATCH * N_HEAD)
      _attn_bwd[grid](
          q,
          arg_k,
          v,
          ctx.sm_scale,
          do,
          dq,
          dk,
          dv,  #
          M,
          delta,  #
          q.stride(0),
          q.stride(1),
          q.stride(2),
          q.stride(3),  #
          N_HEAD,
          N_CTX,  #
          BLOCK_M1=BLOCK_M1,
          BLOCK_N1=BLOCK_N1,  #
          BLOCK_M2=BLOCK_M2,
          BLOCK_N2=BLOCK_N2,  #
          BLK_SLICE_FACTOR=BLK_SLICE_FACTOR,  #
          BLOCK_DMODEL=ctx.BLOCK_DMODEL,  #
          num_warps=NUM_WARPS,  #
          num_stages=NUM_STAGES,  #
      )
  
      return dq, dk, dv, None, None
  
file_path: triton/python/tutorials/06-fused-attention.py
imports: import pytest
  import torch
  import triton
  import triton.language
label_desc: Use image node_id 2 for calling the _attention obj's underlying member method code with example usage: obj.backward(ctx, do) and returns: dq,  dk,  dv,  None,  None