Make yapf happy

        b2 = tf.Variable(tf.constant([0.01]), name="b2", dtype=tf.float32)

      label_placeholders = self.add_label_placeholders(graph, name_scopes)

      weight_placeholders = self.add_example_weight_placeholders(graph, name_scopes)

      weight_placeholders = self.add_example_weight_placeholders(graph,

                                                                 name_scopes)

      if training:

        graph.queue = tf.FIFOQueue(capacity=5, dtypes=[tf.float32]*(len(label_placeholders)+len(weight_placeholders)+1))

        graph.enqueue = graph.queue.enqueue([mol_features]+label_placeholders+weight_placeholders)

        graph.queue = tf.FIFOQueue(

            capacity=5,

            dtypes=[tf.float32] *

            (len(label_placeholders) + len(weight_placeholders) + 1))

        graph.enqueue = graph.queue.enqueue([mol_features] + label_placeholders

                                            + weight_placeholders)

        queue_outputs = graph.queue.dequeue()

        labels = queue_outputs[1:len(label_placeholders) + 1]

        weights = queue_outputs[len(label_placeholders) + 1:]

      for count in range(self.n_tasks):

        similarity = features[:, 2 * K * count:(2 * K * count + K)]

        ys = tf.to_int32(

            features[:, (2 * K * count + K):2 * K * (count + 1)])

        ys = tf.to_int32(features[:, (2 * K * count + K):2 * K * (count + 1)])

        R = b + W[0] * similarity + W[1] * tf.constant(

            np.arange(K) + 1, dtype=tf.float32)

        R = tf.sigmoid(R)