code: def __init__(self, embedding, verbose) -> None:
      super().__init__(embedding, "sqlite", verbose)
      # if embedding database already exists
      if self.store_path.exists() and self.store_path.is_file():
          try:
              self.db = sqlite3.connect(str(self.store_path))
              cursor = self.db.cursor()
              cursor.execute("SELECT * FROM embedding LIMIT 1;")
              result = list(cursor)
              self.k = len(result[0]) - 1
              return
          except sqlite3.Error as err:
              logger.exception(
                  f"Fail to open sqlite database {self.store_path!s}: {err!s}"
              )
      # otherwise, push embedding to database
      if hasattr(embedding, "precomputed_word_embeddings"):
          self.db = sqlite3.connect(str(self.store_path))
          pwe = embedding.precomputed_word_embeddings
          self.k = pwe.vector_size
          self.db.execute("DROP TABLE IF EXISTS embedding;")
          self.db.execute(
              f"CREATE TABLE embedding(word text,{','.join('v' + str(i) + ' float' for i in range(self.k))});"
          )
          vectors_it = (
              [word, *pwe.get_vector(word).tolist()]
              for word in pwe.vocab
          )
          if verbose:
              logger.info("load vectors to store")
          self.db.executemany(
              f"INSERT INTO embedding(word,{','.join('v' + str(i) for i in range(self.k))}) \
              values ({','.join(['?'] * (1 + self.k))})",
              tqdm(vectors_it),
          )
          self.db.execute("DROP INDEX IF EXISTS embedding_index;")
          self.db.execute(
              "CREATE INDEX embedding_index ON embedding(word);"
          )
          self.db.commit()
          self.db.close()
  
imports: import logging
  import pickle
  import re
  import shutil
  import sqlite3
  from pathlib import Path
  from typing import Union
  import numpy
  import torch
  from tqdm import tqdm
  import flair
  from flair.embeddings import WordEmbeddings
label_desc: Use image node_id 1 to create a new SqliteWordEmbeddingsStoreBackend object from inherited base classes: WordEmbeddingsStoreBackend with example: obj = SqliteWordEmbeddingsStoreBackend(embedding, verbose)