def _top_k():
    (values, _) = tf.nn.top_k(logits, k=k)
    min_values = values[:, (- 1), tf.newaxis]
    return tf.where((logits < min_values), (tf.ones_like(logits, dtype=logits.dtype) * (- 10000000000.0)), logits)
def _top_k():
    (values, _) = tf.nn.top_k(logits, k=k)
    min_values = values[:, (- 1), tf.newaxis]
    return tf.where((logits < min_values), (tf.ones_like(logits, dtype=logits.dtype) * (- 10000000000.0)), logits)