docstr: Initialize the RMSNorm normalization layer.
  
  Args:
      dim (int): The dimension of the input tensor.
      eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
  
  Attributes:
      eps (float): A small value added to the denominator for numerical stability.
      weight (nn.Parameter): Learnable scaling parameter.
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code: def __init__(self, dim: int, eps: float = 1e-6):
      """
      Initialize the RMSNorm normalization layer.
  
      Args:
          dim (int): The dimension of the input tensor.
          eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
  
      Attributes:
          eps (float): A small value added to the denominator for numerical stability.
          weight (nn.Parameter): Learnable scaling parameter.
  
      """
      super().__init__()
      self.eps = eps
      self.weight = nn.Parameter(torch.ones(dim))
  
imports: import math
  from dataclasses import dataclass
  from typing import Optional, Tuple
  import fairscale.nn.model_parallel.initialize
  import torch
  import torch.nn.functional
  from fairscale.nn.model_parallel.layers import ColumnParallelLinear, ParallelEmbedding, RowParallelLinear
  from torch import nn
label_desc: Use image node_id 1 to create a new RMSNorm object from inherited base classes: torch.nn with example: obj = RMSNorm(dim, eps)