code: def backward(ctx, dz0, dz1, *args):
      dz0 = maybe_align(dz0.contiguous(), 16)  # this happens!
      dz1 = (
          maybe_align(dz1.contiguous(), 16) if dz1 is not None else None
      )
      dx = (
          maybe_align(args[0].contiguous(), 16) if ctx.prenorm else None
      )
      x, dmask0, dmask1, gamma0, gamma1, mu, rsigma = ctx.saved_tensors
      dropout_p = ctx.dropout_p
      has_x1 = ctx.has_x1
      has_residual = ctx.has_residual
      (
          dx0mat,
          dx1mat,
          dresidualmat,
          dgamma0,
          dbeta0,
          dgamma1,
          dbeta1,
      ) = _dropout_add_layer_norm_parallel_residual_backward(
          dz0,
          dz1,
          dx,
          x,
          dmask0,
          dmask1,
          mu,
          rsigma,
          gamma0,
          gamma1,
          dropout_p,
          has_x1,
          has_residual,
          ctx.is_rms_norm,
      )
      dx0 = dx0mat.view(x.shape)
      dx1 = dx1mat.view(x.shape) if dx1mat is not None else None
      dresidual = (
          dresidualmat.view(x.shape)
          if dresidualmat is not None
          else None
      )
      return (
          dx0,
          dx1,
          dresidual,
          dgamma0,
          dbeta0 if ctx.has_beta else None,
          dgamma1,
          dbeta1 if ctx.has_beta else None,
          None,
          None,
          None,
          None,
          None,
          None,
      )
  
imports: import dropout_layer_norm
  import torch
  from torch.nn import init
label_desc: Use image node_id 2 for calling the DropoutAddLayerNormParallelResidualFn obj's underlying member method code with example usage: obj.backward(ctx, dz0, dz1) and returns: dx0,  dx1,  dresidual,  dgamma0,  unknown,  dgamma1,  unknown,  None,  None,  None,  None,  None,  None