def _compute_auxiliary_loss_fn(model, num_steps, x_in, decomposition, x_hat,
                               auxiliary_models, auxiliary_optimizers):
    """Example of an implementation of the compute auxiliary loss function.
    This implementation does nothing but describes the paramaters.

    Args:
        model: The learned basis decomposition model.
        num_steps: The number of training steps past this epoch.
        x_in: The batch of input data. May be a tuple,
            if multiple input streams are given. Waveform data
            is the first.
        decomposition: The decomposition of x_in[0] through the
            encoder.
        x_hat: The reconstruction of the input signals, through
            decoder(encoder(x_in[0])).
        auxiliary_models: The list of auxiliary models.
        auxiliary_optimizers: The list of optimizers for the auxiliary
            models.

    Returns:
        auxiliary_loss: The auxiliary loss computed by the function.
            A scalar value.
        update_enc_dec: True if the encoder/decoder weights should be
            updated, otherwise false. Allows for an uneven update rule,
            e.g. WGAN.

    """

    return 0, True