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: Similar to keras lib, this function returns the handle to the training step function.
  It processes one batch of data by iterating over the dataset iterator, it computes the loss and optimizes on it.
  
  Returns
  -------
  out: Function handle
        Handle to the training step function.
docstr_tok: ["Similar","to","keras","lib",",","this","function","returns","the","handle","to","the","training","step","function",".","It","processes","one","batch","of","data","by","iterating","over","the","dataset","iterator",",","it","computes","the","loss","and","optimizes","on","it",".","Returns","--","--","--","-","out",":","Function","handle","Handle","to","the","training","step","function","."]
code: def make_train_function(self):
      """Similar to keras lib, this function returns the handle to the training step function.
      It processes one batch of data by iterating over the dataset iterator, it computes the loss and optimizes on it.
  
      Returns
      -------
      out: Function handle
            Handle to the training step function.
      """
      if self.train_function is not None:
          return self.train_function
  
      def train_function(iterator):
          """This is the function whose handle will be returned.
  
          Parameters
          ----------
          iterator: tf.data.Iterator
              Data iterator.
  
          Returns
          -------
          output: dict
            Return a dictionary containing values that will be passed to ``tf.keras.Callbacks.on_train_batch_end``.
          """
          data = next(iterator)
          output = self.train_step(data)
          return output
  
      if not self.run_eagerly and not self.is_partitioned_training:
          train_function = def_function.function(
              train_function, experimental_relax_shapes=True
          )
  
      self.train_function = train_function
      return self.train_function
  
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 12 for calling the ScoringBasedEmbeddingModel obj's underlying member method code with example usage: obj.make_train_function() and returns: self, self, output