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: Save the trainable weights.
  
   Use this function if the training process is complete and you want to
   use the model only for inference. Use :meth:`load_weights` to load the model weights back.
  
   .. Note ::
       If you want to be able of continuing the training, you can use the :meth:`ampligraph.utils.save_model`
       and :meth:`ampligraph.utils.restore_model`.These functions save and restore the entire state
       of the graph, which allows to continue the training from where it was stopped.
  
  Parameters
  ----------
  filepath: str
      Path to save the model.
  overwrite: bool
      Flag which indicates whether the model, if present, needs to be overwritten or not (default: `True`).
docstr_tok: ["Save","the","trainable","weights",".","Use","this","function","if","the","training","process","is","complete","and","you","want","to","use","the","model","only","for","inference",".","Use",":","meth",":","`","load_weights","`","to","load","the","model","weights","back",".","..","Note",":",":","If","you","want","to","be","able","of","continuing","the","training",",","you","can","use","the",":","meth",":","`","ampligraph.utils.save_model","`","and",":","meth",":","`","ampligraph.utils.restore_model","`",".These","functions","save","and","restore","the","entire","state","of","the","graph",",","which","allows","to","continue","the","training","from","where","it","was","stopped",".","Parameters","--","--","--","--","--","filepath",":","str","Path","to","save","the","model",".","overwrite",":","bool","Flag","which","indicates","whether","the","model",",","if","present",",","needs","to","be","overwritten","or","not","(","default",":","`","True","`",")","."]
code: def save_weights(self, filepath, overwrite=True):
      """Save the trainable weights.
  
       Use this function if the training process is complete and you want to
       use the model only for inference. Use :meth:`load_weights` to load the model weights back.
  
       .. Note ::
           If you want to be able of continuing the training, you can use the :meth:`ampligraph.utils.save_model`
           and :meth:`ampligraph.utils.restore_model`.These functions save and restore the entire state
           of the graph, which allows to continue the training from where it was stopped.
  
      Parameters
      ----------
      filepath: str
          Path to save the model.
      overwrite: bool
          Flag which indicates whether the model, if present, needs to be overwritten or not (default: `True`).
      """
      # TODO: verify other formats
  
      # call the base class method to save the weights
      if not self.is_partitioned_training:
          super(ScoringBasedEmbeddingModel, self).save_weights(
              filepath, overwrite
          )
      self.save_metadata(filepath)
  
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 21 for calling the ScoringBasedEmbeddingModel obj's underlying member method code with example usage: obj.save_weights(filepath, overwrite) without return types