import tensorflow as tf

                    strides = [1] + strides + [1]

                    filter_ = get_variable('filter_{}'.format(k),
                                           [filter_height, filter_width, in_channels, out_channels])
                    encoder_inputs_ = tf.nn.conv2d(encoder_inputs_, filter_, strides, padding='SAME')

                    if encoder.batch_norm:
                        encoder_inputs_ = tf.layers.batch_normalization(encoder_inputs_, training=training,
