code: def forward(
      ctx,
      x0,
      x1,
      residual,
      gamma0,
      beta0,
      gamma1,
      beta1,
      dropout_p,
      epsilon,
      residual_in_fp32=False,
      prenorm=False,
      is_rms_norm=False,
      return_dmask=False,
  ):
      x0 = maybe_align(x0.contiguous(), 16)
      x1 = maybe_align(x1.contiguous(), 16) if x1 is not None else None
      residual = (
          maybe_align(residual.contiguous(), 16)
          if residual is not None
          else None
      )
      gamma0 = maybe_align(gamma0.contiguous(), 16)
      beta0 = (
          maybe_align(beta0.contiguous(), 16)
          if beta0 is not None
          else None
      )
      gamma1 = (
          maybe_align(gamma1.contiguous(), 16)
          if gamma1 is not None
          else None
      )
      beta1 = (
          maybe_align(beta1.contiguous(), 16)
          if beta1 is not None
          else None
      )
      (
          z0mat,
          z1mat,
          xmat,
          dmask0,
          dmask1,
          mu,
          rsigma,
      ) = _dropout_add_layer_norm_parallel_residual_forward(
          x0,
          x1,
          residual,
          gamma0,
          beta0,
          gamma1,
          beta1,
          dropout_p,
          epsilon,
          residual_in_fp32,
          is_rms_norm,
      )
      ctx.save_for_backward(
          xmat.view(x0.shape),
          dmask0,
          dmask1,
          gamma0,
          gamma1,
          mu,
          rsigma,
      )
      ctx.prenorm = prenorm
      ctx.dropout_p = dropout_p
      ctx.has_x1 = x1 is not None
      ctx.has_residual = residual is not None
      ctx.is_rms_norm = is_rms_norm
      ctx.has_beta = beta0 is not None
      z = (
          z0mat.view(x0.shape),
          z1mat.view(x0.shape) if z1mat is not None else None,
      )
      if not return_dmask:
          return z if not prenorm else (*z, xmat.view(x0.shape))
      else:
          dmask0 = (
              dmask0.view(x0.shape)
              if dropout_p > 0.0
              else torch.ones(
                  x0.shape, dtype=torch.uint8, device=x0.device
              )
          )
          dmask1 = (
              dmask1.view(x0.shape)
              if dropout_p > 0.0 and x1 is not None
              else torch.ones(
                  x0.shape, dtype=torch.uint8, device=x0.device
              )
          )
          ctx.mark_non_differentiable(dmask0)
          ctx.mark_non_differentiable(dmask1)
          return (
              (*z, dmask0, dmask1)
              if not prenorm
              else (*z, xmat.view(x0.shape), dmask0, dmask1)
          )
  
imports: import dropout_layer_norm
  import torch
  from torch.nn import init
label_desc: Use image node_id 1 for calling the DropoutAddLayerNormParallelResidualFn obj's underlying member method code with example usage: obj.forward(ctx, x0, x1, residual, gamma0, beta0, gamma1, beta1, dropout_p, epsilon, residual_in_fp32, prenorm, is_rms_norm, return_dmask) and returns: unknown, unknown