code: def _dropout_add_layer_norm_prenorm_backward(
      dz,
      dx,
      x,
      dmask,
      mu,
      rsigma,
      gamma,
      rowscale,
      dropout_p,
      has_residual,
  ):
      """Assume that arguments are contiguous"""
      hidden_size = gamma.numel()
      xmat = x.view((-1, hidden_size))
      dzmat = dz.view(xmat.shape)
      dxmat = dx.view(xmat.shape)
      rowscale = rowscale.view(-1) if rowscale is not None else None
      (
          dx0mat,
          dx1mat,
          dgamma,
          dbeta,
          _,
          _,
      ) = dropout_layer_norm.dropout_add_ln_prenorm_bwd(
          dzmat,
          dxmat,
          xmat,
          dmask,
          mu,
          rsigma,
          gamma,
          rowscale,
          dropout_p,
          has_residual,
      )
      return dx0mat, dx1mat, dgamma, dbeta
  
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 4 for calling a global function with example usage: _dropout_add_layer_norm_prenorm_backward(dz, dx, x, dmask, mu, rsigma, gamma, rowscale, dropout_p, has_residual) and returns: dx0mat,  dx1mat,  dgamma,  dbeta