code: def forward(
      ctx,
      x0,
      x1,
      gamma,
      beta,
      rowscale,
      dropout_p,
      epsilon,
      residual_in_fp32,
      return_dmask=False,
  ):
      x0 = x0.contiguous()
      x1 = x1.contiguous() if x1 is not None else None
      gamma = gamma.contiguous()
      beta = beta.contiguous()
      rowscale = rowscale.contiguous() if rowscale is not None else None
      zmat, xmat, dmask, mu, rsigma = _dropout_add_layer_norm_forward(
          x0,
          x1,
          gamma,
          beta,
          rowscale,
          dropout_p,
          epsilon,
          residual_in_fp32,
      )
      ctx.save_for_backward(
          xmat.view(x0.shape), dmask, gamma, mu, rsigma, rowscale
      )
      ctx.dropout_p = dropout_p
      ctx.has_residual = x1 is not None
      if not return_dmask:
          return zmat.view(x0.shape), xmat.view(x0.shape)
      else:
          dmask = (
              dmask.view(x0.shape)
              if dropout_p > 0.0
              else torch.ones(
                  x0.shape, dtype=torch.uint8, device=x0.device
              )
          )
          ctx.mark_non_differentiable(dmask)
          return zmat.view(x0.shape), xmat.view(x0.shape), dmask
  
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 1 for calling the DropoutAddLayerNormPrenormFN obj's underlying member method code with example usage: obj.forward(ctx, x0, x1, gamma, beta, rowscale, dropout_p, epsilon, residual_in_fp32, return_dmask) and returns: zmat,  xmat, zmat,  xmat,  dmask