code: def _get_vector(self, word="house"):
      try:
          import lmdb
  
          with self.env.begin() as txn:
              vector = txn.get(word.encode(encoding="UTF-8"))
              if vector:
                  word_vector = pickle.loads(vector)
                  vector = None
              else:
                  word_vector = np.zeros((self.k,), dtype=np.float32)
      except lmdb.Error:
          # no idea why, but we need to close and reopen the environment to avoid
          # mdb_txn_begin: MDB_BAD_RSLOT: Invalid reuse of reader locktable slot
          # when opening new transaction !
          self.env.close()
          self.env = lmdb.open(
              self.store_path,
              readonly=True,
              max_readers=2048,
              max_spare_txns=2,
              lock=False,
          )
          return self._get_vector(word)
      except ModuleNotFoundError:
          logger.warning("-" * 100)
          logger.warning(
              'ATTENTION! The library "lmdb" is not installed!'
          )
          logger.warning(
              'To use LMDB, please first install with "pip install lmdb"'
          )
          logger.warning("-" * 100)
          word_vector = np.zeros((self.k,), dtype=np.float32)
      return word_vector
  
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 2 for calling the LmdbWordEmbeddingsStoreBackend obj's underlying member method code with example usage: obj._get_vector(word) and returns: word_vector, self