import tensorflow as tf

            padding = padding.upper()

            if isinstance(k_size, list):
                filter_shape = [k_size[0], k_size[1]] + [in_channel, out_dims]
            else:
                filter_shape = [k_size, k_size] + [in_channel, out_dims]

            if w_init is None:
                w_init = tf.contrib.layers.variance_scaling_initializer()
            if b_init is None:
                b_init = tf.constant_initializer()

            w = tf.get_variable('W', filter_shape, initializer=w_init)
            b = None

            if use_bias:
