code: def dropout_add_layer_norm(
      x0,
      x1,
      weight,
      bias,
      dropout_p,
      epsilon,
      rowscale=None,
      prenorm=False,
      residual_in_fp32=False,
      return_dropout_mask=False,
  ):
      """residual_in_fp32 only has an effect if x1 is None.
      Otherwise residual dtype is x1.dtype.
      """
      args = (
          x0,
          x1,
          weight,
          bias,
          rowscale,
          dropout_p,
          epsilon,
          residual_in_fp32,
          return_dropout_mask,
      )
      if not prenorm:
          return DropoutAddLayerNormFN.apply(*args)
      else:
          return DropoutAddLayerNormPrenormFN.apply(*args)
  
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 5 for calling a global function with example usage: dropout_add_layer_norm(x0, x1, weight, bias, dropout_p, epsilon, rowscale, prenorm, residual_in_fp32, return_dropout_mask) and returns: DropoutAddLayerNormFN, DropoutAddLayerNormPrenormFN