docstr: Args:
      w2v_path: Word2Vec file path.
      w2v_kwargs: params pass to the ``load_word2vec_format()`` function
        of `gensim.models.KeyedVectors <https://radimrehurek.com/gensim/models/keyedvectors.html#module-gensim.models.keyedvectors>`_
      kwargs: additional params
docstr_tok: ["Args",":","w2v_path",":","Word2Vec","file","path",".","w2v_kwargs",":","params","pass","to","the","``","load_word2vec_format","(",")","``","function","of","`","gensim.models.KeyedVectors","<","https",":","\/\/radimrehurek.com\/gensim\/models\/keyedvectors.html","#","module-gensim.models.keyedvectors",">","`","_","kwargs",":","additional","params"]
code: def __init__(
      self,
      w2v_path: str,
      *,
      w2v_kwargs: Dict[str, Any] = None,
      **kwargs: Any,
  ):
      """
      Args:
          w2v_path: Word2Vec file path.
          w2v_kwargs: params pass to the ``load_word2vec_format()`` function
            of `gensim.models.KeyedVectors <https://radimrehurek.com/gensim/models/keyedvectors.html#module-gensim.models.keyedvectors>`_
          kwargs: additional params
      """
      if w2v_kwargs is None:
          w2v_kwargs = {}
  
      self.w2v_path = w2v_path
      self.w2v_kwargs = w2v_kwargs
  
      self.embedding_size = None
      self.w2v_matrix: np.ndarray = None
  
      super(WordEmbedding, self).__init__(**kwargs)
  
file_path: Kashgari/kashgari/embeddings/word_embedding.py
imports: from typing import Dict, Any, Optional
  import numpy
  from gensim.models import KeyedVectors
  from tensorflow import keras
  from kashgari.embeddings.abc_embedding import ABCEmbedding
  from kashgari.logger import logger
label_desc: Use image node_id 2 to create a new WordEmbedding object from inherited base classes: ABCEmbedding with example: obj = WordEmbedding(w2v_path)