docstr: Returns hyperparameters of the model.
  
  Returns
  -------
  hyperparam_dict : dict
      Dictionary of hyperparameters that were used for training.
docstr_tok: ["Returns","hyperparameters","of","the","model",".","Returns","--","--","--","-","hyperparam_dict",":","dict","Dictionary","of","hyperparameters","that","were","used","for","training","."]
code: def get_hyperparameter_dict(self):
      """Returns hyperparameters of the model.
  
      Returns
      -------
      hyperparam_dict : dict
          Dictionary of hyperparameters that were used for training.
      """
      ent_idx = np.arange(self.model.data_indexer.get_entities_count())
      rel_idx = np.arange(self.model.data_indexer.get_relations_count())
      ent_values_raw = self.model.data_indexer.get_indexes(
          ent_idx, "e", "ind2raw"
      )
      rel_values_raw = self.model.data_indexer.get_indexes(
          rel_idx, "r", "ind2raw"
      )
      return dict(zip(ent_values_raw, ent_idx)), dict(
          zip(rel_values_raw, rel_idx)
      )
  
imports: import numpy
  import tensorflow
  from ampligraph.latent_features.loss_functions import get
  from ampligraph.latent_features.models import ScoringBasedEmbeddingModel
  from ampligraph.latent_features.optimizers import get
  from ampligraph.latent_features.regularizers import get
label_desc: Use image node_id 9 for calling the ScoringModelBase obj's underlying member method code with example usage: obj.get_hyperparameter_dict() and returns: dict,  dict