research/kg_hyp_emb/models/euclidean.py [142:177]:
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    self.sim = 'dist'

    # reflection
    self.ref = tf.keras.layers.Embedding(
        input_dim=sizes[1],
        output_dim=self.rank,
        embeddings_initializer=self.initializer,
        embeddings_regularizer=self.rel_regularizer,
        name='reflection_weights')

    # rotation
    self.rot = tf.keras.layers.Embedding(
        input_dim=sizes[1],
        output_dim=self.rank,
        embeddings_initializer=self.initializer,
        embeddings_regularizer=self.rel_regularizer,
        name='rotation_weights')

    # attention
    self.context_vec = tf.keras.layers.Embedding(
        input_dim=sizes[1],
        output_dim=self.rank,
        embeddings_initializer=self.initializer,
        embeddings_regularizer=self.rel_regularizer,
        name='context_embeddings')
    self.scale = tf.keras.backend.ones(1) / np.sqrt(self.rank)

  def get_reflection_queries(self, entity, ref):
    queries = euc_utils.givens_reflection(ref, entity)
    return tf.reshape(queries, (-1, 1, self.rank))

  def get_rotation_queries(self, entity, rot):
    queries = euc_utils.givens_rotations(rot, entity)
    return tf.reshape(queries, (-1, 1, self.rank))

  def get_queries(self, input_tensor):
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research/kg_hyp_emb/models/hyperbolic.py [121:156]:
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    self.sim = 'dist'

    # reflection
    self.ref = tf.keras.layers.Embedding(
        input_dim=sizes[1],
        output_dim=self.rank,
        embeddings_initializer=self.initializer,
        embeddings_regularizer=self.rel_regularizer,
        name='reflection_weights')

    # rotation
    self.rot = tf.keras.layers.Embedding(
        input_dim=sizes[1],
        output_dim=self.rank,
        embeddings_initializer=self.initializer,
        embeddings_regularizer=self.rel_regularizer,
        name='rotation_weights')

    # attention
    self.context_vec = tf.keras.layers.Embedding(
        input_dim=sizes[1],
        output_dim=self.rank,
        embeddings_initializer=self.initializer,
        embeddings_regularizer=self.rel_regularizer,
        name='context_embeddings')
    self.scale = tf.keras.backend.ones(1) / np.sqrt(self.rank)

  def get_reflection_queries(self, entity, ref):
    queries = euc_utils.givens_reflection(ref, entity)
    return tf.reshape(queries, (-1, 1, self.rank))

  def get_rotation_queries(self, entity, rot):
    queries = euc_utils.givens_rotations(rot, entity)
    return tf.reshape(queries, (-1, 1, self.rank))

  def get_queries(self, input_tensor):
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