code: def forward(self, x):
      output = self._norm(x.float()).type_as(x)
      return output * self.weight
  
imports: import math
  from dataclasses import dataclass
  from typing import Any, Optional, Tuple
  import fairscale.nn.model_parallel.initialize
  import torch
  import torch.nn.functional
  from fairscale.nn.model_parallel.layers import ColumnParallelLinear, ParallelEmbedding, RowParallelLinear
  from torch import nn
label_desc: Use image node_id 3 for calling the RMSNorm obj's underlying member method code with example usage: obj.forward(x) and returns: unknown