import tensorflow as tf

            padding = padding.upper()

            if isinstance(kernel_size, list):
                filter_shape = [kernel_size[0], kernel_size[1]] + [in_channel / split, out_channel]
            else:
                filter_shape = [kernel_size, kernel_size] + [in_channel / split, out_channel]

            if isinstance(stride, list):
                strides = [1, stride[0], stride[1], 1] if data_format == 'NHWC' \
                    else [1, 1, stride[0], stride[1]]
            else:
                strides = [1, stride, stride, 1] if data_format == 'NHWC' \
                    else [1, 1, stride, stride]

            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:
                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)
