class_docstr: Class for self-defined node data structure
  
  ...
  
  Attributes
  ----------
  word_ : (str)
      The text contained in this node.
  
  type_ : (int)
      Node type in graph data structure, 0 for word nodes, 1 for constituency nodes,             2~n can be defined by other node types.
  
  id_ : (int)
      Unique identifier for nodes.
  
  sentence_ : (int)
      To illustrate which sentence the node is related when multiple sentences included             in a graph.
class_docstr_tok: ["Class","for","self-defined","node","data","structure","...","Attributes","--","--","--","--","--","word_",":","(","str",")","The","text","contained","in","this","node",".","type_",":","(","int",")","Node","type","in","graph","data","structure",",","0","for","word","nodes",",","1","for","constituency","nodes",",","2~n","can","be","defined","by","other","node","types",".","id_",":","(","int",")","Unique","identifier","for","nodes",".","sentence_",":","(","int",")","To","illustrate","which","sentence","the","node","is","related","when","multiple","sentences","included","in","a","graph","."]
code: def __init__(self, word_, type_, id_, sentence_):
      # word: this node's text
      self.word = word_
  
      # type: 0 for word nodes, 1 for constituency nodes  # noqa
      self.type = type_
  
      # id: unique identifier for every node
      self.id = id_
  
      self.head = False
  
      self.tail = False
  
      self.sentence = sentence_
  
file_path: graph4nlp/graph4nlp/pytorch/modules/graph_construction/utility_functions.py
imports: import copy
label_desc: Use image node_id 1 to create a new Node object  with example: obj = Node(word_, type_, id_, sentence_)