(self, embeddings)
return tf.stack([c.log_prob(embeddings) for c in self.components], axis=-1)
Compute log probabilities of embeddings under each class-conditional.

    Args:
      embeddings: A [num_examples, embedding_dim] Tensor of embeddings.

    Returns:
      A [num_examples, num_components] Tensor of log_probabalities.
Compute log probabilities of embeddings under each class-conditional.
Compute log probabilities of embeddings under each class - conditional .
def __call__(self, embeddings):
    """Compute log probabilities of embeddings under each class-conditional.

    Args:
      embeddings: A [num_examples, embedding_dim] Tensor of embeddings.

    Returns:
      A [num_examples, num_components] Tensor of log_probabalities.
    """
    return tf.stack([c.log_prob(embeddings) for c in self.components], axis=-1)
def __call__ ( self , embeddings ) : return tf . stack ( [ c . log_prob ( embeddings ) for c in self . components ] , axis = - 1 )