code: def __init__(self, embedding, verbose) -> None:
      super().__init__(embedding, "lmdb", verbose)
      try:
          import lmdb
  
          # if embedding database already exists
          if self.store_path.exists() and self.store_path.is_dir():
              # open the database in read mode
              try:
                  self.env = lmdb.open(
                      str(self.store_path),
                      readonly=True,
                      max_readers=2048,
                      max_spare_txns=4,
                  )
                  if self.env:
                      # we need to set self.k
                      with self.env.begin() as txn:
                          cursor = txn.cursor()
                          for _key, value in cursor:
                              vector = pickle.loads(value)
                              self.k = vector.shape[0]
                              break
                          cursor.close()
                      return
              except lmdb.Error as err:
                  logger.exception(
                      f"Fail to open lmdb database {self.store_path!s}: {err!s}"
                  )
          # create and load the database in write mode
          if hasattr(embedding, "precomputed_word_embeddings"):
              pwe = embedding.precomputed_word_embeddings
              self.k = pwe.vector_size
              self.store_path.mkdir(parents=True, exist_ok=True)
              self.env = lmdb.open(
                  str(self.store_path), map_size=DEFAULT_MAP_SIZE
              )
              if verbose:
                  logger.info("load vectors to store")
  
              txn = self.env.begin(write=True)
              for word in tqdm(pwe.vocab.keys()):
                  vector = pwe.get_vector(word)
                  if (
                      len(word.encode(encoding="UTF-8"))
                      < self.env.max_key_size()
                  ):
                      txn.put(
                          word.encode(encoding="UTF-8"),
                          pickle.dumps(vector),
                      )
              txn.commit()
              return
      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)
  
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 LmdbWordEmbeddingsStoreBackend object from inherited base classes: WordEmbeddingsStoreBackend with example: obj = LmdbWordEmbeddingsStoreBackend(embedding, verbose)