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: ``concept_type='e'``.
  
  Returns
  -------
  count: int
      Count of the entities or relations.
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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: ``concept_type='e'``.
  
      Returns
      -------
      count: int
          Count of the entities or relations.
      """
      if concept_type == "entity" or concept_type == "e":
          return self.model.get_count("e")
      elif concept_type == "relation" or concept_type == "r":
          return self.model.get_count("r")
      else:
          raise ValueError("Invalid value for concept_type!")
  
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 7 for calling the ScoringModelBase obj's underlying member method code with example usage: obj.get_count(concept_type) and returns: self, self