code: def forward(self, x):
      downcast_x = _cast_if_autocast_enabled(x)
      downcast_weight = (
          _cast_if_autocast_enabled(self.weight)
          if self.weight is not None
          else self.weight
      )
      with torch.autocast(enabled=False, device_type=x.device.type):
          return rms_norm(downcast_x, downcast_weight, self.eps).to(
              dtype=x.dtype
          )
  
imports: import math
  import os
  import warnings
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from einops import rearrange
  from packaging import version
  from text_generation_server.utils.layers import TensorParallelEmbedding, TensorParallelColumnLinear, TensorParallelRowLinear, TensorParallelHead, get_linear
label_desc: Use image node_id 2 for calling the LPRMSNorm obj's underlying member method code with example usage: obj.forward(x) and returns: rms_norm