code: def forward(self, x):
      module_device = x.device
      downcast_x = _cast_if_autocast_enabled(x)
      downcast_weight = (
          _cast_if_autocast_enabled(self.weight)
          if self.weight is not None
          else self.weight
      )
      downcast_bias = (
          _cast_if_autocast_enabled(self.bias)
          if self.bias is not None
          else self.bias
      )
      with torch.autocast(
          enabled=False, device_type=module_device.type
      ):
          return torch.nn.functional.layer_norm(
              downcast_x,
              self.normalized_shape,
              downcast_weight,
              downcast_bias,
              self.eps,
          )
  
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 LPLayerNorm obj's underlying member method code with example usage: obj.forward(x) and returns: torch