class_docstr: Random scoring layer.
docstr: Compute subject corruption scores.
  
  Evaluate the inputs against subject corruptions and scores of the corruptions.
  
  Parameters
  ----------
  triples: array-like, shape (n, k)
      Batch of input embeddings.
  ent_matrix: array-like, shape (m, k)
      Slice of embedding matrix (corruptions).
  
  Returns
  -------
  scores: tf.Tensor, shape (n, 1)
      Scores of subject corruptions (corruptions defined by `ent_embs` matrix).
code: def _get_subject_corruption_scores(self, triples, ent_matrix):
      """Compute subject corruption scores.
  
      Evaluate the inputs against subject corruptions and scores of the corruptions.
  
      Parameters
      ----------
      triples: array-like, shape (n, k)
          Batch of input embeddings.
      ent_matrix: array-like, shape (m, k)
          Slice of embedding matrix (corruptions).
  
      Returns
      -------
      scores: tf.Tensor, shape (n, 1)
          Scores of subject corruptions (corruptions defined by `ent_embs` matrix).
      """
      scores = tf.random.uniform(
          shape=[tf.shape(triples[0])[0], tf.shape(ent_matrix)[0]],
          seed=0,
      )
      return scores
  
file_path: AmpliGraph/ampligraph/latent_features/layers/scoring/Random.py
imports: import tensorflow
  from .AbstractScoringLayer import register_layer, AbstractScoringLayer
label_desc: Use image node_id 4 for calling the Random obj's underlying member method code with example usage: obj._get_subject_corruption_scores(triples, ent_matrix) and returns: scores