code: def __init__(
      self,
      normalized_shape,
      eps=1e-05,
      elementwise_affine=True,
      device=None,
      dtype=None,
  ):
      super().__init__(
          normalized_shape=normalized_shape,
          eps=eps,
          elementwise_affine=elementwise_affine,
          device=device,
          dtype=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 1 to create a new LPLayerNorm object from inherited base classes: torch.nn with example: obj = LPLayerNorm(normalized_shape, eps, elementwise_affine, device, dtype)