class_docstr: Notes
  -----
  a graph feature generation method. This is a simple wrapper of NetLSD [#]_.
  
  References
  ----------
  ..  [#] A. Tsitsulin, D. Mottin, P. Karras, A. Bronstein, and E. Müller, “NetLSD: Hearing the shape of a graph,”
      Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 2347–2356, 2018.
code: def __edge_index_to_nx_graph(
      cls, edge_index: torch.Tensor
  ) -> networkx.Graph:
      g: networkx.Graph = networkx.Graph()
      for u, v in edge_index.t().tolist():
          if u == v:
              continue
          else:
              g.add_edge(u, v)
      return g
  
file_path: AutoGL/autogl/module/preprocessing/feature_engineering/_graph/_netlsd.py
imports: import netlsd
  import networkx
  import torch
  import typing
  from autogl.data.graph import GeneralStaticGraph
  from autogl.data.graph.utils import conversion
  from .._feature_engineer import FeatureEngineer
  from ..._data_preprocessor_registry import DataPreprocessorUniversalRegistry
label_desc: Use image node_id 4 for calling the NetLSD obj's underlying member method code with example usage: obj.__edge_index_to_nx_graph(cls, edge_index) and returns: g