code: def layer_norm_ref(
      x,
      weight,
      bias,
      residual=None,
      x1=None,
      weight1=None,
      bias1=None,
      eps=1e-6,
      dropout_p=0.0,
      rowscale=None,
      prenorm=False,
      dropout_mask=None,
      dropout_mask1=None,
      upcast=False,
  ):
      dtype = x.dtype
      if upcast:
          x = x.float()
          weight = weight.float()
          bias = bias.float() if bias is not None else None
          residual = (
              residual.float() if residual is not None else residual
          )
          x1 = x1.float() if x1 is not None else None
          weight1 = weight1.float() if weight1 is not None else None
          bias1 = bias1.float() if bias1 is not None else None
      if x1 is not None:
          assert (
              rowscale is None
          ), "rowscale is not supported with parallel LayerNorm"
      if rowscale is not None:
          x = x * rowscale[..., None]
      if dropout_p > 0.0:
          if dropout_mask is not None:
              x = x.masked_fill(~dropout_mask, 0.0) / (1.0 - dropout_p)
          else:
              x = F.dropout(x, p=dropout_p)
          if x1 is not None:
              if dropout_mask1 is not None:
                  x1 = x1.masked_fill(~dropout_mask1, 0.0) / (
                      1.0 - dropout_p
                  )
              else:
                  x1 = F.dropout(x1, p=dropout_p)
      if x1 is not None:
          x = x + x1
      if residual is not None:
          x = (x + residual).to(x.dtype)
      out = F.layer_norm(
          x.to(weight.dtype),
          x.shape[-1:],
          weight=weight,
          bias=bias,
          eps=eps,
      ).to(dtype)
      if weight1 is None:
          return out if not prenorm else (out, x)
      else:
          out1 = F.layer_norm(
              x.to(weight1.dtype),
              x.shape[-1:],
              weight=weight1,
              bias=bias1,
              eps=eps,
          ).to(dtype)
          return (out, out1) if not prenorm else (out, out1, x)
  
imports: import math
  import torch
  import torch.nn.functional
  from torch.cuda.amp import custom_fwd, custom_bwd
  import triton
  import triton.language
label_desc: Use image node_id 1 for calling a global function with example usage: layer_norm_ref(x, weight, bias, residual, x1, weight1, bias1, eps, dropout_p, rowscale, prenorm, dropout_mask, dropout_mask1, upcast) and returns: unknown, unknown