import tensorflow as tf

            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:
                b = tf.get_variable('b', [out_channel], initializer=b_init)

            if split == 1:
                conv = tf.nn.conv2d(inputdata, w, strides, padding, data_format=data_format)
            else:
                inputs = tf.split(inputdata, split, channel_axis)
                kernels = tf.split(w, split, 3)
                outputs = [tf.nn.conv2d(i, k, strides, padding, data_format=data_format)
                           for i, k in zip(inputs, kernels)]
                conv = tf.concat(outputs, channel_axis)

            ret = tf.identity(tf.nn.bias_add(conv, b, data_format=data_format)
                              if use_bias else conv, name=name)
