import tensorflow as tf

                                dilations=(1, dilation, dilation, 1))
                x = x * mask_ratio
                if use_bias:
                    bias = tf.get_variable("bias" + id, [channels], initializer=tf.constant_initializer(0.0))
                    x = tf.nn.bias_add(x, bias)
                return x * update_mask

        x = tf.nn.conv2d(input, filters, strides=[1, stride, stride, 1], padding=padding, name='zero-conv_' + id,
