def batch_norm_layer(self, x, train_phase, scope_bn):
    bn_train = batch_norm(x, decay=self.batch_norm_decay, center=True, scale=True, updates_collections=None, is_training=True, reuse=None, trainable=True, scope=scope_bn)
    bn_inference = batch_norm(x, decay=self.batch_norm_decay, center=True, scale=True, updates_collections=None, is_training=False, reuse=True, trainable=True, scope=scope_bn)
    z = tf.cond(train_phase, (lambda : bn_train), (lambda : bn_inference))
    return z
def batch_norm_layer(self, x, train_phase, scope_bn):
    bn_train = batch_norm(x, decay=self.batch_norm_decay, center=True, scale=True, updates_collections=None, is_training=True, reuse=None, trainable=True, scope=scope_bn)
    bn_inference = batch_norm(x, decay=self.batch_norm_decay, center=True, scale=True, updates_collections=None, is_training=False, reuse=True, trainable=True, scope=scope_bn)
    z = tf.cond(train_phase, (lambda : bn_train), (lambda : bn_inference))
    return z