import tensorflow as tf

                    updates_collections=None,
                    scope=scope,
                    reuse=self.reuse,
                    is_training=self.train)
        _, l3_h2 = self.hgru_ops(
            i0=i0,
            x=processed_l2_h2,
            h2=l3_h2,
            layer='h3',
            layer_idx=1)
        if self.batch_norm:
            with tf.variable_scope(
                    'l3_h2_bn',
                    reuse=self.scope_reuse) as scope:
                l3_h2 = tf.contrib.layers.batch_norm(
                    inputs=l3_h2,
                    scale=True,
                    center=True,
                    fused=True,
                    renorm=False,
                    param_initializers=self.param_initializer,
                    updates_collections=None,
                    scope=scope,
                    reuse=self.reuse,
                    is_training=self.train)

        # l3-l2 feedback (FEEDBACK KERNEL is 2x channels)
        _, temp_l2_h2 = self.hgru_ops(
