How to use the babi.model.VariableEmbedder function in babi

with tf.name_scope("placeholders"):
            x = tf.placeholder('int32', shape=[N, M, J], name='x')
            x_mask = tf.placeholder('bool', shape=[N, M, J], name='x_mask')
            q = tf.placeholder('int32', shape=[N, J], name='q')
            q_mask = tf.placeholder('bool', shape=[N, J], name='q_mask')
            y = tf.placeholder('int32', shape=[N], name='y')
            is_train = tf.placeholder('bool', shape=[], name='is_train')
            placeholders['x'] = x
            placeholders['x_mask'] = x_mask
            placeholders['q'] = q
            placeholders['q_mask'] = q_mask
            placeholders['y'] = y
            placeholders['is_train'] = is_train

        with tf.variable_scope("embedding"):
            A = VariableEmbedder(params, wd=wd, initializer=initializer, name='A')
            Aq = A(q, name='Aq')  # [N, S, J, d]
            Ax = A(x, name='Ax')  # [N, S, J, d]

        with tf.name_scope("encoding"):
            encoder = PositionEncoder(J, d)
            u = encoder(Aq, q_mask)  # [N, d]
            m = encoder(Ax, x_mask)  # [N, M, d]

        with tf.variable_scope("networks"):
            m_mask = tf.reduce_max(tf.cast(x_mask, 'int64'), 2, name='m_mask')  # [N, M]
            gate_mask = tf.expand_dims(m_mask, -1)
            m_length = tf.reduce_sum(m_mask, 1, name='m_length')  # [N]
            prev_u = tf.tile(tf.expand_dims(u, 1), [1, M, 1])  # [N, M, d]
            reg_layer = VectorReductionLayer(N, M, d) if use_vector_gate else ReductionLayer(N, M, d)
            gate_size = d if use_vector_gate else 1
            h = None  # [N, M, d]