class_docstr: Class for node embedding based refined dynamic graph construction.
  
  Parameters
  ----------
  alpha_fusion : float
      Specify the fusion value for combining initial and learned adjacency matrices.
class_docstr_tok: ["Class","for","node","embedding","based","refined","dynamic","graph","construction",".","Parameters","--","--","--","--","--","alpha_fusion",":","float","Specify","the","fusion","value","for","combining","initial","and","learned","adjacency","matrices","."]
code: def __init__(self, alpha_fusion, **kwargs):
      super(NodeEmbeddingBasedRefinedGraphConstruction, self).__init__(
          **kwargs
      )
      assert (
          0 <= alpha_fusion <= 1
      ), "alpha_fusion should be a `float` number between 0 and 1"
      self.alpha_fusion = alpha_fusion
  
file_path: graph4nlp/graph4nlp/pytorch/modules/graph_construction/node_embedding_based_refined_graph_construction.py
imports: import torch
  from nltk.tokenize import word_tokenize
  from ...data.data import GraphData
  from ..utils.generic_utils import normalize_adj
  from ..utils.vocab_utils import Vocab
  from .base import DynamicGraphConstructionBase
  from .constituency_graph_construction import ConstituencyBasedGraphConstruction
  from .dependency_graph_construction import DependencyBasedGraphConstruction
  from .ie_graph_construction import IEBasedGraphConstruction
  from .utils import convert_adj_to_graph
label_desc: Use image node_id 1 to create a new NodeEmbeddingBasedRefinedGraphConstruction object from inherited base classes: DynamicGraphConstructionBase with example: obj = NodeEmbeddingBasedRefinedGraphConstruction(alpha_fusion)