docstr: Get the embeddings of entities or relations.
  
  .. Note ::
  
      Use :meth:`ampligraph.utils.create_tensorboard_visualizations` to
      visualize the embeddings with TensorBoard.
  
  Parameters
  ----------
  entities : array-like, dtype=int, shape=[n]
      The entities (or relations) of interest. Elements of the vector
      must be the original string literals, and
      not internal IDs.
  embedding_type : str
      If `'e'` or `'entities'`, ``entities`` argument will be
      considered as a list of knowledge graph entities (i.e. nodes).
      If set to `'r'` or `'relation'`, they will be treated as relation
      types instead (i.e. predicates).
  
  Returns
  -------
  embeddings : ndarray, shape [n, k]
      An array of k-dimensional embeddings.
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code: def get_embeddings(self, entities, embedding_type="entity"):
      """Get the embeddings of entities or relations.
  
      .. Note ::
  
          Use :meth:`ampligraph.utils.create_tensorboard_visualizations` to
          visualize the embeddings with TensorBoard.
  
      Parameters
      ----------
      entities : array-like, dtype=int, shape=[n]
          The entities (or relations) of interest. Elements of the vector
          must be the original string literals, and
          not internal IDs.
      embedding_type : str
          If `'e'` or `'entities'`, ``entities`` argument will be
          considered as a list of knowledge graph entities (i.e. nodes).
          If set to `'r'` or `'relation'`, they will be treated as relation
          types instead (i.e. predicates).
  
      Returns
      -------
      embeddings : ndarray, shape [n, k]
          An array of k-dimensional embeddings.
      """
      if embedding_type == "entity" or embedding_type == "e":
          return self.model.get_embeddings(entities, "e")
      elif embedding_type == "relation" or embedding_type == "r":
          return self.model.get_embeddings(entities, "r")
      else:
          raise ValueError("Invalid value for embedding_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 8 for calling the ScoringModelBase obj's underlying member method code with example usage: obj.get_embeddings(entities, embedding_type) and returns: self, self