class_docstr:     Line based graph construction class
  Parameters
  ----------
  embedding_style: dict
      Specify embedding styles including ``word_emb_type``, ``node_edge_level_emb_type``
      and ``graph_level_emb_type``.
  vocab: VocabModel
      Vocabulary including all words appeared in graphs.
class_docstr_tok: ["Line","based","graph","construction","class","Parameters","--","--","--","--","--","embedding_style",":","dict","Specify","embedding","styles","including","``","word_emb_type","``",",","``","node_edge_level_emb_type","``","and","``","graph_level_emb_type","``",".","vocab",":","VocabModel","Vocabulary","including","all","words","appeared","in","graphs","."]
code: def __init__(
      self,
      vocab,
  ):
      super(
          DependencyBasedGraphConstruction_without_tokenizer, self
      ).__init__()
      self.vocab = vocab
      self.verbose = 1
  
file_path: graph4nlp/examples/pytorch/name_entity_recognition/dependency_graph_construction_without_tokenize.py
imports: import copy
  import itertools
  import json
  from collections import Counter
  from graph4nlp.pytorch.data.data import GraphData
  from graph4nlp.pytorch.modules.graph_construction.base import StaticGraphConstructionBase
label_desc: Use image node_id 1 to create a new DependencyBasedGraphConstruction_without_tokenizer object from inherited base classes: StaticGraphConstructionBase with example: obj = DependencyBasedGraphConstruction_without_tokenizer(vocab)