code: def __init__(self):
      super(NXIsEulerian, self).__init__(is_eulerian)
  
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 1 to create a new NXIsEulerian object from inherited base classes: _NetworkXGraphFeatureEngineer with example: obj = NXIsEulerian()