class_docstr: Applies graph normalization over individual graphs as described in the
  `"GraphNorm: A Principled Approach to Accelerating Graph Neural Network
  Training" <https://arxiv.org/abs/2009.03294>`_ paper.
  
  .. math::
      \mathbf{x}^{\prime}_i = \frac{\mathbf{x} - \alpha \odot
      \textrm{E}[\mathbf{x}]}
      {\sqrt{\textrm{Var}[\mathbf{x} - \alpha \odot \textrm{E}[\mathbf{x}]]
      + \epsilon}} \odot \gamma + \beta
  
  where :math:`\alpha` denotes parameters that learn how much information
  to keep in the mean.
  
  Args:
      in_channels (int): Size of each input sample.
      eps (float, optional): A value added to the denominator for numerical
          stability. (default: :obj:`1e-5`)
class_docstr_tok: ["Applies","graph","normalization","over","individual","graphs","as","described","in","the","`","``","GraphNorm",":","A","Principled","Approach","to","Accelerating","Graph","Neural","Network","Training","''","<","https",":","\/\/arxiv.org\/abs\/2009.03294",">","`","_","paper",".","..","math",":",":","\\mathbf","{","x","}","^","{","\\prime","}","_i","=","\\frac","{","\\mathbf","{","x","}","-","\\alpha","\\odot","\\textrm","{","E","}","[","\\mathbf","{","x","}","]","}","{","\\sqrt","{","\\textrm","{","Var","}","[","\\mathbf","{","x","}","-","\\alpha","\\odot","\\textrm","{","E","}","[","\\mathbf","{","x","}","]","]","+","\\epsilon","}","}","\\odot","\\gamma","+","\\beta","where",":","math",":","`","\\alpha","`","denotes","parameters","that","learn","how","much","information","to","keep","in","the","mean",".","Args",":","in_channels","(","int",")",":","Size","of","each","input","sample",".","eps","(","float",",","optional",")",":","A","value","added","to","the","denominator","for","numerical","stability",".","(","default",":",":","obj",":","`","1e-5","`",")"]
code: def __repr__(self):
      return f"{self.__class__.__name__}({self.in_channels})"
  
file_path: pytorch_geometric/torch_geometric/nn/norm/graph_norm.py
imports: from typing import Optional
  import torch
  from torch import Tensor
  from torch_geometric.nn.inits import ones, zeros
  from torch_geometric.typing import OptTensor
  from torch_geometric.utils import scatter
label_desc: Use image node_id 4 for calling the GraphNorm obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str