code: def __edge_index_to_nx_graph(
      cls, edge_index: torch.Tensor
  ) -> networkx.Graph:
      g: networkx.Graph = networkx.Graph()
      for u, v in edge_index.t().tolist():
          if u == v:
              continue
          else:
              g.add_edge(u, v)
      return g
  
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 3 for calling the _NetworkXGraphFeatureEngineer obj's underlying member method code with example usage: obj.__edge_index_to_nx_graph(cls, edge_index) and returns: g