code: def bloom_layer_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_layer_norm(
              hidden_states,
              [self.weight.size(0)],
              self.weight,
              self.bias,
              self.eps,
          )
          # if nelement == 0, means fused norm failed, go back to python implement.
          if result.nelement != 0:
              return result
      return F.layer_norm(
          hidden_states,
          self.normalized_shape,
          self.weight,
          self.bias,
          self.eps,
      )
  
imports: from typing import Optional, Tuple
  import torch
  import torch.utils.checkpoint
  from torch.nn import functional
  from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
label_desc: Use image node_id 2 for calling a global function with example usage: bloom_layer_norm_forward(hidden_states) and returns: F, result