code: def _dropout_add_layer_norm_parallel_residual_forward(
      x0,
      x1,
      residual,
      gamma0,
      beta0,
      gamma1,
      beta1,
      dropout_p,
      epsilon,
      residual_in_fp32=False,
      is_rms_norm=False,
  ):
      """Assume that arguments are contiguous and aligned to 16 bytes"""
      hidden_size = gamma0.numel()
      x0mat = x0.view((-1, hidden_size))
      x1mat = x1.view((-1, hidden_size)) if x1 is not None else None
      residualmat = (
          residual.view((-1, hidden_size))
          if residual is not None
          else None
      )
      (
          z0mat,
          z1mat,
          xmat,
          dmask0,
          dmask1,
          mu,
          rsigma,
      ) = dropout_layer_norm.dropout_add_ln_parallel_residual_fwd(
          x0mat,
          x1mat,
          residualmat,
          gamma0,
          beta0,
          gamma1,
          beta1,
          dropout_p,
          epsilon,
          None,
          residual_in_fp32,
          is_rms_norm,
      )
      # dmask0 and dmask1 are None if dropout_p == 0.0
      # xmat is None if dropout_p == 0.0 and residual is None and residual_dtype != input_dtype
      return (
          z0mat,
          z1mat,
          xmat if xmat is not None else x0mat,
          dmask0,
          dmask1,
          mu,
          rsigma,
      )
  
imports: import dropout_layer_norm
  import torch
  from torch.nn import init
label_desc: Use image node_id 6 for calling a global function with example usage: _dropout_add_layer_norm_parallel_residual_forward(x0, x1, residual, gamma0, beta0, gamma1, beta1, dropout_p, epsilon, residual_in_fp32, is_rms_norm) and returns: z0mat,  z1mat,  unknown,  dmask0,  dmask1,  mu,  rsigma