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.
class_docstr_tok: ["Notes","--","--","-","a","graph","feature","generation","method",".","This","is","a","simple","wrapper","of","NetLSD","[","#","]","_.","References","--","--","--","--","--","..","[","#","]","A.","Tsitsulin",",","D.","Mottin",",","P.","Karras",",","A.","Bronstein",",","and","E.","M\u00fcller",",","\u201c","NetLSD",":","Hearing","the","shape","of","a","graph",",","\u201d","Proc",".","ACM","SIGKDD","Int",".","Conf",".","Knowl",".","Discov",".","Data","Min.",",","pp",".","2347\u20132356",",","2018","."]
code: def __transform_data(self, data):
      if not (
          hasattr(data, "edge_index")
          and torch.is_tensor(data.edge_index)
          and isinstance(data.edge_index, torch.Tensor)
          and data.edge_index.dim() == data.edge_index.size(0) == 2
          and data.edge_index.dtype == torch.long
      ):
          raise TypeError("Unsupported provided data")
      dsc: torch.Tensor = self.__extract(
          self.__edge_index_to_nx_graph(data.edge_index)
      )
      if hasattr(data, "gf") and isinstance(data.gf, torch.Tensor):
          gf = data.gf.view(-1)
          data.gf = torch.cat([gf, dsc])
      else:
          data.gf = dsc
      return data
  
file_path: AutoGL/autogl/module/feature/_graph/_netlsd.py
imports: import netlsd
  import networkx
  import torch
  from autogl.data.graph import GeneralStaticGraph
  from autogl.data.graph.utils import conversion
  from .._base_feature_engineer import BaseFeatureEngineer
  from .._feature_engineer_registry import FeatureEngineerUniversalRegistry
label_desc: Use image node_id 5 for calling the NetLSD obj's underlying member method code with example usage: obj.__transform_data(data) and returns: data