code: def forward(self, hidden_states):
      variance = (
          hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
      )
      hidden_states = hidden_states * torch.rsqrt(
          variance + self.variance_epsilon
      )
  
      # convert into half-precision if necessary
      if self.weight.dtype in [torch.float16, torch.bfloat16]:
          hidden_states = hidden_states.to(self.weight.dtype)
  
      return self.weight * hidden_states
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from transformers.models.llama.configuration_llama import LlamaConfig
label_desc: Use image node_id 2 for calling the LlamaRMSNorm obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: unknown