code: def _dropout_add_layer_norm_forward(
      x0,
      x1,
      gamma,
      beta,
      rowscale,
      dropout_p,
      epsilon,
      residual_in_fp32,
  ):
      """Assume that arguments are contiguous"""
      hidden_size = gamma.numel()
      x0mat = x0.view((-1, hidden_size))
      x1mat = x1.view((-1, hidden_size)) if x1 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,
          x1mat,
          gamma,
          beta,
          rowscale,
          dropout_p,
          epsilon,
          None,
          residual_in_fp32,
      )
      # dmask is None if dropout_p == 0.0
      # xmat is None if dropout_p == 0.0 and x1 is None and residual_dtype != input_dtype
      return (
          zmat,
          xmat if xmat is not None else x0mat,
          dmask,
          mu,
          rsigma,
      )
  
imports: import torch
  from torch.nn import init
  from apex.contrib.layer_norm.layer_norm import FastLayerNormFN, FastLayerNorm
  import dropout_layer_norm
label_desc: Use image node_id 2 for calling a global function with example usage: _dropout_add_layer_norm_forward(x0, x1, gamma, beta, rowscale, dropout_p, epsilon, residual_in_fp32) and returns: zmat,  unknown,  dmask,  mu,  rsigma