class_docstr: Class implementing database connection.
  
  Example
  -------
  >>> AMPLIGRAPH_DATA_HOME='/your/path/to/datasets/'
  >>> # Initialize GraphDataLoader from .csv file
  >>> data = GraphDataLoader("data.csv", backend=SQLiteAdapter)
  >>> # Initialize GraphDataLoader from .txt file using indexer
  >>> # to map entities to integers
  >>> data = GraphDataLoader(AMPLIGRAPH_DATA_HOME + "fb15k/test.txt",
  >>>                        backend=SQLiteAdapter("database.db",
  >>>                        use_indexer=True))
  >>> for elem in data:
  >>>     print(elem)
  >>>     break
  [(1, 1, 2)]
  >>> # Populate the database with raw triples for training
  >>> with SQLiteAdapter("database.db") as backend:
  >>>     backend.populate(AMPLIGRAPH_DATA_HOME + "fb15k/train.txt",
  >>>                      dataset_type="train")
  >>> # Populate the database with indexed triples for training
  >>> with SQLiteAdapter("database.db", use_indexer=True) as backend:
  >>>     backend.populate(AMPLIGRAPH_DATA_HOME + "fb15k/train.txt",
  >>>                      dataset_type="train")
docstr: Open the database.
docstr_tok: ["Open","the","database","."]
code: def open_db(self):
      """Open the database."""
      db_uri = "file:{}?mode=rw".format(pathname2url(self.db_path))
      self.connection = sqlite3.connect(db_uri, uri=True)
      self.flag_db_open = True
      logger.debug(
          "----------------DB OPENED - normally -----------------"
      )
  
imports: import logging
  import os
  import sqlite3
  import tempfile
  from sqlite3 import Error
  from urllib.request import pathname2url
  import numpy
  import pandas
  import tensorflow
  from ampligraph.utils.profiling import get_human_readable_size
  from .data_indexer import DataIndexer
label_desc: Use image node_id 3 for calling the SQLiteAdapter obj's underlying member method code with example usage: obj.open_db() without return types