code: def llama_rms_norm_forward(self, hidden_states):
      if hidden_states.device.type == "xpu" and not (
          self.training and hidden_states.requires_grad
      ):
          import linear_q4_0
  
          result = linear_q4_0.fused_rms_norm(
              hidden_states,
              [self.weight.size(0)],
              self.weight,
              None,
              self.variance_epsilon,
          )
          # if nelement == 0, means fused norm failed, go back to python implement.
          if result.nelement != 0:
              # We should copy this result to avoid <unk> by unknown reason on Arc GPUs.
              result = result.clone()
              return result
      input_dtype = hidden_states.dtype
      hidden_states = hidden_states.to(torch.float32)
      variance = hidden_states.pow(2).mean(-1, keepdim=True)
      hidden_states = hidden_states * torch.rsqrt(
          variance + self.variance_epsilon
      )
      return self.weight * hidden_states.to(input_dtype)
  
imports: import torch
  import warnings
  import importlib
  import torch.nn
  from typing import Optional, Tuple, Union, List
  import math
  import os
  import torch.nn.functional
  from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
  from bigdl.llm.transformers.models.utils import is_enough_kv_cache_room_4_31, apply_rotary_pos_emb, is_enough_kv_cache_room_4_36
  from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu
  from bigdl.llm.transformers.models.utils import use_flash_attention, use_esimd_sdp
  from bigdl.llm.transformers.models.utils import mlp_fusion_check
  from transformers.modeling_outputs import BaseModelOutputWithPast
  from bigdl.llm.transformers.low_bit_linear import SYM_INT4, FP8E5
  from bigdl.llm.ggml.quantize import ggml_tensor_qtype
  from bigdl.llm.utils.common import invalidInputError
label_desc: Use image node_id 3 for calling a global function with example usage: llama_rms_norm_forward(hidden_states) and returns: unknown, result