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 metadata.
docstr_tok: ["Save","metadata","."]
code: def save_metadata(self, filepath=None, filedir=None):
      """Save metadata."""
      # store ampligraph specific metadata
      if filepath is not None:
          base_dir = os.path.dirname(filedir)
          base_dir = "." if base_dir == "" else base_dir
          filepath = os.path.basename(filepath)
  
      if filedir is not None:
          base_dir = filedir
          filepath = os.path.basename(filedir)
  
      with open(
          os.path.join(base_dir, filepath + "_metadata.ampkl"), "wb"
      ) as f:
          metadata = {
              "is_partitioned_training": self.is_partitioned_training,
              "max_ent_size": self.max_ent_size,
              "max_rel_size": self.max_rel_size,
              "eta": self.eta,
              "k": self.k,
              "is_fitted": self.is_fitted,
              "is_calibrated": self.is_calibrated,
              "is_backward": self.is_backward,
              "data_shape": self.data_shape,
          }
  
          metadata.update(
              self.data_indexer.get_update_metadata(base_dir)
          )
          if self.is_partitioned_training:
              self.partitioner_metadata = (
                  self.data_handler.get_update_partitioner_metadata(
                      base_dir
                  )
              )
              metadata.update(self.partitioner_metadata)
  
          if self.is_calibrated:
              metadata[
                  "calib_w"
              ] = self.calibration_layer.calib_w.numpy()
              metadata[
                  "calib_b"
              ] = self.calibration_layer.calib_b.numpy()
              metadata["pos_size"] = self.calibration_layer.pos_size
              metadata["neg_size"] = self.calibration_layer.neg_size
              metadata[
                  "positive_base_rate"
              ] = self.calibration_layer.positive_base_rate
          pickle.dump(metadata, f)
  
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 20 for calling the ScoringBasedEmbeddingModel obj's underlying member method code with example usage: obj.save_metadata(filepath, filedir) without return types