code: def _dropout_add_layer_norm_forward(
      x0,
      residual,
      gamma,
      beta,
      rowscale,
      colscale,
      dropout_p,
      epsilon,
      residual_in_fp32=False,
      is_rms_norm=False,
  ):
      """Assume that arguments are contiguous and aligned to 16 bytes"""
      hidden_size = gamma.numel()
      x0mat = x0.view((-1, hidden_size))
      residualmat = (
          residual.view((-1, hidden_size))
          if residual is not None
          else None
      )
      rowscale = rowscale.view(-1) if rowscale is not None else None
      (
          zmat,
          xmat,
          dmask,
          mu,
          rsigma,
      ) = dropout_layer_norm.dropout_add_ln_fwd(
          x0mat,
          residualmat,
          gamma,
          beta,
          rowscale,
          colscale,
          None,
          None,
          dropout_p,
          epsilon,
          1.0,
          0,
          None,
          residual_in_fp32,
          is_rms_norm,
      )
      # dmask is None if dropout_p == 0.0
      # xmat is None if dropout_p == 0.0 and residual is None and residual_dtype != input_dtype
      return (
          zmat,
          xmat if xmat is not None else x0mat,
          dmask,
          mu,
          rsigma,
      )
  
imports: import dropout_layer_norm
  import torch
  from torch.nn import init
label_desc: Use image node_id 2 for calling a global function with example usage: _dropout_add_layer_norm_forward(x0, residual, gamma, beta, rowscale, colscale, dropout_p, epsilon, residual_in_fp32, is_rms_norm) and returns: zmat,  unknown,  dmask,  mu,  rsigma