code: def __init__(self, dim, scale=True, dim_cond=None):
      super().__init__()
      self.cond = exists(dim_cond)
      self.to_gamma_beta = (
          nn.Linear(dim_cond, dim * 2) if self.cond else None
      )
  
      self.scale = dim**0.5
      self.gamma = nn.Parameter(torch.ones(dim)) if scale else None
  
imports: from collections import namedtuple
  from functools import wraps
  import torch
  import torch.nn.functional
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
  from packaging import version
  from torch import einsum, nn
label_desc: Use image node_id 1 to create a new RMSNorm object from inherited base classes: nn with example: obj = RMSNorm(dim, scale, dim_cond)