code: def layer_norm_residual_kernel(
      Out,
      A,
      Residual,
      ln_input,
      Weight,
      Bias,
      stride,
      N,
      eps,
      BLOCK_SIZE: tl.constexpr,
  ):
      # position of elements processed by this program
      row = tl.program_id(0)
      Out += row * stride
      A += row * stride
      Residual += row * stride
      ln_input += row * stride
      # compute mean
      mean = 0
      _mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
      for off in range(0, N, BLOCK_SIZE):
          cols = off + tl.arange(0, BLOCK_SIZE)
          a = tl.load(A + cols, mask=cols < N, other=0.0).to(tl.float32)
          res = tl.load(Residual + cols, mask=cols < N, other=0.0).to(
              tl.float32
          )
          a = a + res
          tl.store(ln_input + cols, a, mask=cols < N)
          _mean += a
      mean = tl.sum(_mean, axis=0) / N
      # compute variance
      _var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
      for off in range(0, N, BLOCK_SIZE):
          cols = off + tl.arange(0, BLOCK_SIZE)
          a = tl.load(ln_input + cols, mask=cols < N, other=0.0).to(
              tl.float32
          )
          a = tl.where(cols < N, a - mean, 0.0)
          _var += a * a
      var = tl.sum(_var, axis=0) / N
      rstd = 1 / tl.sqrt(var + eps)
      # multiply by weight and add bias
      for off in range(0, N, BLOCK_SIZE):
          cols = off + tl.arange(0, BLOCK_SIZE)
          mask = cols < N
          weight = tl.load(Weight + cols, mask=mask)
          bias = tl.load(Bias + cols, mask=mask)
          a = tl.load(ln_input + cols, mask=mask, other=0.0).to(
              tl.float32
          )
          a_hat = (a - mean) * rstd
          out = a_hat * weight + bias
          # write-back
          tl.store(Out + cols, out, mask=mask)
  
file_path: DeepSpeed/deepspeed/ops/transformer/inference/triton/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_residual_kernel(Out, A, Residual, ln_input, Weight, Bias, stride, N, eps, BLOCK_SIZE) without return types