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 = torch.tensor(
          [
              self.__feature_extractor(
                  self.__edge_index_to_nx_graph(data.edge_index)
              )
          ]
      ).view(-1)
      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
  
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 4 for calling the _NetworkXGraphFeatureEngineer obj's underlying member method code with example usage: obj.__transform_data(data) and returns: data