class_docstr: Class for handling KGE models which follows the ranking based protocol.
  
  Example
  -------
  >>> # create model and compile using user defined optimizer settings and
  >>> # user defined settings of an existing loss
  >>> from ampligraph.datasets import load_fb15k_237
  >>> from ampligraph.latent_features import ScoringBasedEmbeddingModel
  >>> from ampligraph.latent_features.loss_functions import SelfAdversarialLoss
  >>> import tensorflow as tf
  >>> X = load_fb15k_237()
  >>> loss = SelfAdversarialLoss({'margin': 0.1, 'alpha': 5, 'reduction': 'sum'})
  >>> model = ScoringBasedEmbeddingModel(eta=5,
  >>>                                    k=300,
  >>>                                    scoring_type='ComplEx',
  >>>                                    seed=0)
  >>> model.compile(optimizer='adam', loss=loss)
  >>> model.fit(X['train'],
  >>>           batch_size=10000,
  >>>           epochs=5)
  Epoch 1/5
  29/29 [==============================] - 3s 87ms/step - loss: 13496.5752
  Epoch 2/5
  29/29 [==============================] - 1s 36ms/step - loss: 13488.8682
  Epoch 3/5
  29/29 [==============================] - 1s 35ms/step - loss: 13436.2725
  Epoch 4/5
  29/29 [==============================] - 1s 35ms/step - loss: 13259.0840
  Epoch 5/5
  29/29 [==============================] - 1s 34ms/step - loss: 12977.0117
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docstr: Converts given data to indexes or to raw data (according to ``order``).
  
   It works for ``X`` containing triples, entities, or relations.
  
  Parameters
  ----------
  X: np.array or list
      Data to be indexed.
  type_of: str
      Specifies whether to get indexes/raw data for triples (``type_of='t'``), entities (``type_of='e'``),
      or relations (``type_of='r'``).
  order: str
      Specifies whether to get indexes from raw data (``order='raw2ind'``) or
      raw data from indexes (``order='ind2raw'``).
  
  Returns
  -------
  Y: np.array
      Indexed data or raw data.
  
  Example
  -------
  >>> from ampligraph.latent_features import ScoringBasedEmbeddingModel
  >>> from ampligraph.datasets import load_fb15k_237
  >>> X = load_fb15k_237()
  >>> model = ScoringBasedEmbeddingModel(eta=5,
  >>>                                    k=300,
  >>>                                    scoring_type='ComplEx',
  >>>                                    seed=0)
  >>> model.compile(optimizer='adam', loss='nll')
  >>> model.fit(X['train'],
  >>>           batch_size=10000,
  >>>           epochs=5,
  >>>           verbose=False)
  >>> print(model.get_indexes(['/m/027rn', '/m/06v8s0'], 'e', 'raw2ind'))
  >>> print(model.get_indexes([3877, 0], 'e', 'ind2raw'))
  [0, 3877]
  ['/m/06v8s0', '/m/027rn']
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code: def get_indexes(self, X, type_of="t", order="raw2ind"):
      """Converts given data to indexes or to raw data (according to ``order``).
  
       It works for ``X`` containing triples, entities, or relations.
  
      Parameters
      ----------
      X: np.array or list
          Data to be indexed.
      type_of: str
          Specifies whether to get indexes/raw data for triples (``type_of='t'``), entities (``type_of='e'``),
          or relations (``type_of='r'``).
      order: str
          Specifies whether to get indexes from raw data (``order='raw2ind'``) or
          raw data from indexes (``order='ind2raw'``).
  
      Returns
      -------
      Y: np.array
          Indexed data or raw data.
  
      Example
      -------
      >>> from ampligraph.latent_features import ScoringBasedEmbeddingModel
      >>> from ampligraph.datasets import load_fb15k_237
      >>> X = load_fb15k_237()
      >>> model = ScoringBasedEmbeddingModel(eta=5,
      >>>                                    k=300,
      >>>                                    scoring_type='ComplEx',
      >>>                                    seed=0)
      >>> model.compile(optimizer='adam', loss='nll')
      >>> model.fit(X['train'],
      >>>           batch_size=10000,
      >>>           epochs=5,
      >>>           verbose=False)
      >>> print(model.get_indexes(['/m/027rn', '/m/06v8s0'], 'e', 'raw2ind'))
      >>> print(model.get_indexes([3877, 0], 'e', 'ind2raw'))
      [0, 3877]
      ['/m/06v8s0', '/m/027rn']
      """
      return self.data_indexer.get_indexes(X, type_of, order)
  
imports: import tensorflow
  import copy
  import shelve
  import pickle
  import numpy
  import os
  import tempfile
  import logging
  from ampligraph.evaluation.metrics import mrr_score, hits_at_n_score, mr_score
  from ampligraph.datasets import data_adapter
  from ampligraph.datasets.partitioned_data_manager import PartitionDataManager
  from ampligraph.latent_features.layers.scoring.AbstractScoringLayer import SCORING_LAYER_REGISTRY
  from ampligraph.latent_features.layers.encoding import EmbeddingLookupLayer
  from ampligraph.latent_features.layers.calibration import CalibrationLayer
  from ampligraph.latent_features.layers.corruption_generation import CorruptionGenerationLayerTrain
  from ampligraph.datasets.data_indexer import DataIndexer
  from ampligraph.latent_features import optimizers
  from ampligraph.latent_features import loss_functions
  from ampligraph.evaluation import train_test_split_no_unseen
  from tensorflow.python.keras import callbacks
  from tensorflow.python.keras.engine import training_utils
  from tensorflow.python.eager import def_function
  from tensorflow.python.keras import metrics
  from tensorflow.python.keras.engine import compile_utils
label_desc: Use image node_id 16 for calling the ScoringBasedEmbeddingModel obj's underlying member method code with example usage: obj.get_indexes(X, type_of, order) and returns: self