class_docstr: Applies the GCN normalization from the `"Semi-supervised Classification
  with Graph Convolutional Networks" <https://arxiv.org/abs/1609.02907>`_
  paper (functional name: :obj:`gcn_norm`).
  
  .. math::
      \mathbf{\hat{A}} = \mathbf{\hat{D}}^{-1/2} (\mathbf{A} + \mathbf{I})
      \mathbf{\hat{D}}^{-1/2}
  
  where :math:`\hat{D}_{ii} = \sum_{j=0} \hat{A}_{ij} + 1`.
code: def __repr__(self) -> str:
      return (
          f"{self.__class__.__name__}("
          f"add_self_loops={self.add_self_loops})"
      )
  
file_path: pytorch_geometric/torch_geometric/transforms/gcn_norm.py
imports: import torch_geometric
  from torch_geometric.data import Data
  from torch_geometric.data.datapipes import functional_transform
  from torch_geometric.transforms import BaseTransform
label_desc: Use image node_id 3 for calling the GCNNorm obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str