import tensorflow as tf

    """
    with tf.variable_scope(scope) as sc:
        kernel_d, kernel_h, kernel_w = kernel_size
        num_in_channels = inputs.get_shape()[-1].value
        kernel_shape = [kernel_d, kernel_h, kernel_w,
                        num_in_channels, num_output_channels]
        kernel = _variable_with_weight_decay('weights',
                                             shape=kernel_shape,
                                             use_xavier=use_xavier,
                                             stddev=stddev,
                                             wd=weight_decay)
        stride_d, stride_h, stride_w = stride
        outputs = tf.nn.conv3d(inputs, kernel,
                               [1, stride_d, stride_h, stride_w, 1],
                               padding=padding)
        biases = _variable_on_cpu('biases', [num_output_channels],
                                  tf.constant_initializer(0.0))
        outputs = tf.nn.bias_add(outputs, biases)

        if bn:
            outputs = batch_norm_for_conv3d(outputs, is_training,
                                            bn_decay=bn_decay, scope='bn')

        if activation_fn is not None:
