docstr: RMS Normaliation module
  
  Arguments:
      dim (int): The width of input, i.e. hidden size
      eps (float): epsilon to use for the norm, default to 1e-6
      sequence_parallel (bool): Set to true if sequence parallelism is being used,
        this marks the weights as needing to be allreduced.
docstr_tok: ["RMS","Normaliation","module","Arguments",":","dim","(","int",")",":","The","width","of","input",",","i.e",".","hidden","size","eps","(","float",")",":","epsilon","to","use","for","the","norm",",","default","to","1e-6","sequence_parallel","(","bool",")",":","Set","to","true","if","sequence","parallelism","is","being","used",",","this","marks","the","weights","as","needing","to","be","allreduced","."]
code: def __init__(
      self, dim: int, eps: float = 1e-6, sequence_parallel: bool = False
  ):
      """RMS Normaliation module
  
      Arguments:
          dim (int): The width of input, i.e. hidden size
          eps (float): epsilon to use for the norm, default to 1e-6
          sequence_parallel (bool): Set to true if sequence parallelism is being used,
            this marks the weights as needing to be allreduced.
      """
      super().__init__()
      self.eps = eps
      self.weight = nn.Parameter(torch.ones(dim))
  
      setattr(self.weight, "sequence_parallel", sequence_parallel)
  
imports: import torch
  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, sequence_parallel)