docstr: RMSNorm is equivalent to T5LayerNorm
docstr_tok: ["RMSNorm","is","equivalent","to","T5LayerNorm"]
code: def __init__(self, hidden_size, eps=1e-6):
      """
      RMSNorm is equivalent to T5LayerNorm
      """
      super().__init__()
      self.weight = nn.Parameter(torch.ones(hidden_size))
      self.variance_epsilon = eps
  
imports: from .configuration_baichuan import BaiChuanConfig
  from transformers import PreTrainedModel, add_start_docstrings
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
  from transformers.utils import logging, add_start_docstrings_to_model_forward, replace_return_docstrings
  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
label_desc: Use image node_id 1 to create a new RMSNorm object from inherited base classes: nn with example: obj = RMSNorm(hidden_size, eps)