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