code: def __init__(self, dim, eps: float, elementwise_affine: bool = True):
      super().__init__()
  
      self.eps = eps
  
      if isinstance(dim, numbers.Integral):
          dim = (dim,)
  
      self.dim = torch.Size(dim)
  
      if elementwise_affine:
          self.weight = nn.Parameter(torch.ones(dim))
      else:
          self.weight = None
  
imports: import numbers
  from typing import Dict, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from ..utils import is_torch_version
  from .activations import get_activation
  from .embeddings import CombinedTimestepLabelEmbeddings, PixArtAlphaCombinedTimestepSizeEmbeddings
label_desc: Use image node_id 1 to create a new RMSNorm object from inherited base classes: nn with example: obj = RMSNorm(dim, eps, elementwise_affine)