import tensorflow as tf

        self.feature_size = data_set.feature_size
        if self.hparams.ranker_learning_rate < 0:
            self.ranker_learning_rate = tf.Variable(float(self.hparams.learning_rate), trainable=False)
        else:
            self.ranker_learning_rate = tf.Variable(float(self.hparams.ranker_learning_rate), trainable=False)
        self.learning_rate = self.ranker_learning_rate
#         self.weighs_propen=
        # Feeds for inputs.
        self.is_training = tf.placeholder(tf.bool, name="is_train")
        self.docid_inputs = [] # a list of top documents
        self.letor_features = tf.placeholder(tf.float32, shape=[None, self.feature_size], 
                                name="letor_features") # the letor features for the documents
        self.labels = []  # the labels for the documents (e.g., clicks)
        self.types=[]
        for i in range(self.max_candidate_num):
            self.docid_inputs.append(tf.placeholder(tf.int64, shape=[None],
