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: Returns the count of entities and relations that were present during training.
  
  Parameters
  ----------
  concept_type: str
      Indicates whether to count entities (``concept_type='e'``) or
      relations (``concept_type='r'``) (default: `'e'`).
  
  Returns
  -------
  count: int
      Count of the entities or relations.
  
  Example
  -------
  >>> from ampligraph.datasets import load_fb15k_237
  >>> from ampligraph.latent_features import ScoringBasedEmbeddingModel
  >>> 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('Entities:', model.get_count('e'))
  >>> print('Relations:', model.get_count('r'))
  Entities: 14505
  Relations: 237
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code: def get_count(self, concept_type="e"):
      """Returns the count of entities and relations that were present during training.
  
      Parameters
      ----------
      concept_type: str
          Indicates whether to count entities (``concept_type='e'``) or
          relations (``concept_type='r'``) (default: `'e'`).
  
      Returns
      -------
      count: int
          Count of the entities or relations.
  
      Example
      -------
      >>> from ampligraph.datasets import load_fb15k_237
      >>> from ampligraph.latent_features import ScoringBasedEmbeddingModel
      >>> 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('Entities:', model.get_count('e'))
      >>> print('Relations:', model.get_count('r'))
      Entities: 14505
      Relations: 237
      """
      assert self.is_fitted, "Model is not fit on the data yet!"
      if concept_type == "e":
          return self.data_indexer.get_entities_count()
      elif concept_type == "r":
          return self.data_indexer.get_relations_count()
      else:
          raise ValueError("Invalid Concept Type (expected 'e' or 'r')")
  
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 17 for calling the ScoringBasedEmbeddingModel obj's underlying member method code with example usage: obj.get_count(concept_type) and returns: self, self