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: Returns the participating entities in the relation
  ?-p-o and s-p-?.
  
  Parameters
  ----------
  triples: ndarray of shape (N,3)
      Triples (s-p-o) that we are querying.
  
  Returns
  -------
  entities: list, list
          Two lists of subjects and objects participating in the
          relations ?-p-o and s-p-?.
docstr_tok: ["Returns","the","participating","entities","in","the","relation","?","-p-o","and","s-p-","?",".","Parameters","--","--","--","--","--","triples",":","ndarray","of","shape","(","N,3",")","Triples","(","s-p-o",")","that","we","are","querying",".","Returns","--","--","--","-","entities",":","list",",","list","Two","lists","of","subjects","and","objects","participating","in","the","relations","?","-p-o","and","s-p-","?","."]
code: def _get_complementary_entities(self, triples, use_filter=None):
      """Returns the participating entities in the relation
      ?-p-o and s-p-?.
  
      Parameters
      ----------
      triples: ndarray of shape (N,3)
          Triples (s-p-o) that we are querying.
  
      Returns
      -------
      entities: list, list
              Two lists of subjects and objects participating in the
              relations ?-p-o and s-p-?.
  
      """
      objects = self._get_complementary_objects(
          triples, use_filter=use_filter
      )
      subjects = self._get_complementary_subjects(
          triples, use_filter=use_filter
      )
      return subjects, objects
  
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 25 for calling the SQLiteAdapter obj's underlying member method code with example usage: obj._get_complementary_entities(triples, use_filter) and returns: subjects,  objects