import tensorflow as tf

                        activation = tf.tanh

                    if encoder.batch_norm:
                        encoder_inputs_ = tf.layers.batch_normalization(encoder_inputs_, training=training,
                                                                        name='input_batch_norm_{}'.format(j + 1))

                    encoder_inputs_ = dense(encoder_inputs_, layer_size, activation=activation, use_bias=True,
                                            name='layer_{}'.format(j))
                    if encoder.use_dropout:
                        encoder_inputs_ = tf.nn.dropout(encoder_inputs_, keep_prob=encoder.input_layer_keep_prob)

            if encoder.conv_filters:
                encoder_inputs_ = tf.expand_dims(encoder_inputs_, axis=3)

                for k, out_channels in enumerate(encoder.conv_filters, 1):
                    in_channels = encoder_inputs_.get_shape()[-1].value
                    filter_height, filter_width = encoder.conv_size

