code: def _transform(self, data):
      dsc = self.extract(data)
      dsc = torch.FloatTensor([[dsc]])
      data.gf = torch.cat([data.gf, dsc], dim=1)
      return data
  
imports: from networkx.algorithms.euler import is_eulerian
  from networkx.algorithms.efficiency_measures import global_efficiency
  from networkx.algorithms.efficiency_measures import local_efficiency
  from networkx.algorithms.distance_regular import is_distance_regular
  from networkx.algorithms.components import number_connected_components
  from networkx.algorithms.components import is_connected
  from networkx.algorithms.cluster import average_clustering
  from networkx.algorithms.cluster import transitivity
  from networkx.algorithms.clique import graph_number_of_cliques
  from networkx.algorithms.clique import graph_clique_number
  from networkx.algorithms.bridges import has_bridges
  from networkx.algorithms.assortativity import degree_pearson_correlation_coefficient
  from networkx.algorithms.assortativity import degree_assortativity_coefficient
  from networkx.algorithms.approximation.clustering_coefficient import average_clustering
  from networkx.algorithms.approximation.clique import large_clique_size
  import netlsd
  from .base import BaseGraph
  import numpy
  import torch
  from functools import wraps
  from ..None import register_feature
label_desc: Use image node_id 3 for calling the NxGraph obj's underlying member method code with example usage: obj._transform(data) and returns: data