class_docstr: The supergraph structure in GripNet. Each supergraph is a directed acyclic graph (DAG) containing
  supervertices and superedges.
  
  Args:
      supervertex_list (list[SuperVertex]): a list of supervertices.
      superedge_list (list[SuperEdge]): a list of superedges.
      supervertex_para_setting (dict[str, SuperVertexParaSetting], Optional): the parameter settings for each supervertex.
class_docstr_tok: ["The","supergraph","structure","in","GripNet",".","Each","supergraph","is","a","directed","acyclic","graph","(","DAG",")","containing","supervertices","and","superedges",".","Args",":","supervertex_list","(","list","[","SuperVertex","]",")",":","a","list","of","supervertices",".","superedge_list","(","list","[","SuperEdge","]",")",":","a","list","of","superedges",".","supervertex_para_setting","(","dict","[","str",",","SuperVertexParaSetting","]",",","Optional",")",":","the","parameter","settings","for","each","supervertex","."]
code: def __repr__(self) -> str:
      return (
          f"SuperGraph(\n  svertex_dict={self.supervertex_dict.values()}, "
          f"\n  sedge_dict={self.superedge_dict.values()}, \n  G={self.G}), "
          f"\n  topological_order={self.topological_order}"
      )
  
imports: import logging
  from typing import Dict, List, Optional
  import networkx
  import torch
label_desc: Use image node_id 5 for calling the SuperGraph obj's underlying member method code with example usage: obj.__repr__() and returns: str, self, str, self, str, self, str, self