docstr: Forward pass through the RMSNorm layer.
  
  Args:
      x (torch.Tensor): The input tensor.
  
  Returns:
      torch.Tensor: The output tensor after applying RMSNorm.
docstr_tok: ["Forward","pass","through","the","RMSNorm","layer",".","Args",":","x","(","torch.Tensor",")",":","The","input","tensor",".","Returns",":","torch.Tensor",":","The","output","tensor","after","applying","RMSNorm","."]
code: def forward(self, x):
      """
      Forward pass through the RMSNorm layer.
  
      Args:
          x (torch.Tensor): The input tensor.
  
      Returns:
          torch.Tensor: The output tensor after applying RMSNorm.
  
      """
      output = self._norm(x.float()).type_as(x)
      return output * self.weight
  
imports: import math
  from dataclasses import dataclass
  from typing import 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