code: def _layer_norm_bwd_dx_fused(
      DX,  # pointer to the input gradient
      DY,  # pointer to the output gradient
      DW,  # pointer to the partial sum of weights gradient
      DB,  # pointer to the partial sum of biases gradient
      X,  # pointer to the input
      W,  # pointer to the weights
      B,  # pointer to the biases
      Mean,  # pointer to the mean
      Rstd,  # pointer to the 1/std
      Lock,  # pointer to the lock
      stride,  # how much to increase the pointer when moving by 1 row
      N,  # number of columns in X
      eps,  # epsilon to avoid division by zero
      GROUP_SIZE_M: tl.constexpr,
      BLOCK_SIZE_N: tl.constexpr,
  ):
      # Map the program id to the elements of X, DX, and DY it should compute.
      row = tl.program_id(0)
      cols = tl.arange(0, BLOCK_SIZE_N)
      mask = cols < N
      X += row * stride
      DY += row * stride
      DX += row * stride
      # Offset locks and weights/biases gradient pointer for parallel reduction
      lock_id = row % GROUP_SIZE_M
      Lock += lock_id
      Count = Lock + GROUP_SIZE_M
      DW = DW + lock_id * N + cols
      DB = DB + lock_id * N + cols
      # Load data to SRAM
      x = tl.load(X + cols, mask=mask, other=0).to(tl.float32)
      dy = tl.load(DY + cols, mask=mask, other=0).to(tl.float32)
      w = tl.load(W + cols, mask=mask).to(tl.float32)
      mean = tl.load(Mean + row)
      rstd = tl.load(Rstd + row)
      # Compute dx
      xhat = (x - mean) * rstd
      wdy = w * dy
      xhat = tl.where(mask, xhat, 0.0)
      wdy = tl.where(mask, wdy, 0.0)
      c1 = tl.sum(xhat * wdy, axis=0) / N
      c2 = tl.sum(wdy, axis=0) / N
      dx = (wdy - (xhat * c1 + c2)) * rstd
      # Write dx
      tl.store(DX + cols, dx, mask=mask)
      # Accumulate partial sums for dw/db
      partial_dw = (dy * xhat).to(w.dtype)
      partial_db = (dy).to(w.dtype)
      while tl.atomic_cas(Lock, 0, 1) == 1:
          pass
      count = tl.load(Count)
      # First store doesn't accumulate
      if count == 0:
          tl.atomic_xchg(Count, 1)
      else:
          partial_dw += tl.load(DW, mask=mask)
          partial_db += tl.load(DB, mask=mask)
      tl.store(DW, partial_dw, mask=mask)
      tl.store(DB, partial_db, mask=mask)
      # Release the lock
      tl.atomic_xchg(Lock, 0)
  
file_path: triton/python/tutorials/05-layer-norm.py
imports: import torch
  import triton
  import triton.language
label_desc: Use image node_id 2 for calling a global function with example usage: _layer_norm_bwd_dx_fused(DX, DY, DW, DB, X, W, B, Mean, Rstd, Lock, stride, N, eps, GROUP_SIZE_M, BLOCK_SIZE_N) without return types