import tensorflow as tf

        # Set shape to remove ambiguity for dense layer.
        height, width =  params["generator_projection_dims"][0:2]
        valid_kernel_size = (
            params["discriminator_base_conv_blocks"][0][-1][0]
        )
        block_conv.set_shape(
            [
                block_conv.get_shape()[0],
                height - valid_kernel_size + 1,
                width - valid_kernel_size + 1,
                block_conv.get_shape()[-1]]
        )
        print_obj("use_discriminator_logits_layer", "block_conv", block_conv)

        with tf.variable_scope(name_or_scope=self.name, reuse=tf.AUTO_REUSE):
            # Flatten final block conv tensor.
            block_conv_flat = self.flatten_layer(inputs=block_conv)
            print_obj(
                "use_discriminator_logits_layer",
                "block_conv_flat",
                block_conv_flat
            )

            # Final linear layer for logits.
            logits = self.logits_layer(inputs=block_conv_flat)
            print_obj("use_discriminator_logits_layer", "logits", logits)

        return logits
