code: def _transform(
      self, data: _typing.Union[GeneralStaticGraph, _typing.Any]
  ) -> _typing.Union[GeneralStaticGraph, _typing.Any]:
      if isinstance(data, GeneralStaticGraph):
          return self.__transform_homogeneous_static_graph(data)
      else:
          return self.__transform_data(data)
  
imports: import torch
  import typing
  import networkx
  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 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
  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 5 for calling the _NetworkXGraphFeatureEngineer obj's underlying member method code with example usage: obj._transform(data) and returns: self, self