class_docstr: A GraphNorm layer implemented as in
  
  GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training (arXiv:2009.03294v1)
    Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-yan Liu, Liwei Wang
class_docstr_tok: ["A","GraphNorm","layer","implemented","as","in","GraphNorm",":","A","Principled","Approach","to","Accelerating","Graph","Neural","Network","Training","(","arXiv:2009.03294v1",")","Tianle","Cai",",","Shengjie","Luo",",","Keyulu","Xu",",","Di","He",",","Tie-yan","Liu",",","Liwei","Wang"]
code: def output_state_dimension(self) -> int:
      return self.__input_state_dim
  
file_path: ptgnn/ptgnn/neuralmodels/gnn/messagepassing/graphnorm.py
imports: import torch
  from torch import nn
  from torch_scatter import scatter_mean
  from typing import Dict, List, Tuple
  from ptgnn.neuralmodels.gnn.messagepassing.abstractmessagepassing import AbstractMessagePassingLayer
label_desc: Use image node_id 4 for calling the GraphNorm obj's underlying member method code with example usage: obj.output_state_dimension() and returns: self