code: def _rms_norm_add_kernel(
      x_ptr,
      y_ptr,
      h1_ptr,
      w_ptr,
      eps,
      stride,
      N_COLS: tl.constexpr,
      BLOCK_SIZE: tl.constexpr,
      INCLUDE_WEIGHT: tl.constexpr,
  ):
      row = tl.program_id(0)
      x_ptr += row * stride
      y_ptr += row * stride
      h1_ptr += row * stride
  
      _mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
      for offset in range(0, N_COLS, BLOCK_SIZE):
          cols = offset + tl.arange(0, BLOCK_SIZE)
          mask = cols < N_COLS
          ax = tl.load(
              x_ptr + cols,
              mask=mask,
              other=0.0,
              eviction_policy="evict_last",
          ).to(tl.float32)
          ay = tl.load(
              y_ptr + cols,
              mask=mask,
              other=0.0,
              eviction_policy="evict_first",
          ).to(tl.float32)
          a = ax + ay
          tl.store(x_ptr + cols, a, mask=mask)
          _mean += a * a
      rstd = tl_math.rsqrt((tl.sum(_mean, axis=0) / N_COLS) + eps)
      for offset in range(0, N_COLS, BLOCK_SIZE):
          cols = offset + tl.arange(0, BLOCK_SIZE)
          mask = cols < N_COLS
          a = tl.load(
              x_ptr + cols,
              mask=mask,
              other=0.0,
              eviction_policy="evict_first",
          ).to(tl.float32)
          if INCLUDE_WEIGHT:
              w = tl.load(w_ptr + cols, mask=mask)
              tl.store(h1_ptr + cols, a * rstd * w, mask=mask)
          else:
              tl.store(h1_ptr + cols, a * rstd, mask=mask)
  
imports: import torch
  import triton
  import triton.language
label_desc: Use image node_id 2 for calling a global function with example usage: _rms_norm_add_kernel(x_ptr, y_ptr, h1_ptr, w_ptr, eps, stride, N_COLS, BLOCK_SIZE, INCLUDE_WEIGHT) without return types