code: def __transform_homogeneous_static_graph(
      self, homogeneous_static_graph: GeneralStaticGraph
  ) -> GeneralStaticGraph:
      if not (
          homogeneous_static_graph.nodes.is_homogeneous
          and homogeneous_static_graph.edges.is_homogeneous
      ):
          raise ValueError("Provided static graph must be homogeneous")
      dsc: torch.Tensor = torch.tensor(
          [
              self.__feature_extractor(
                  conversion.HomogeneousStaticGraphToNetworkX(
                      to_undirected=True
                  )(homogeneous_static_graph)
              )
          ]
      ).view(-1)
      if "gf" in homogeneous_static_graph.data:
          gf = homogeneous_static_graph.data["gf"].view(-1)
          homogeneous_static_graph.data["gf"] = torch.cat([gf, dsc])
      else:
          homogeneous_static_graph.data["gf"] = dsc
      return homogeneous_static_graph
  
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 2 for calling the _NetworkXGraphFeatureEngineer obj's underlying member method code with example usage: obj.__transform_homogeneous_static_graph(homogeneous_static_graph) and returns: homogeneous_static_graph