#' @param impute.val [\code{numeric}]\cr
#' If something goes wrong during optimization (e.g. the learner crashes),
#' this value is fed back to the tuner, so the tuning algorithm does not abort.
#' It is not stored in the optimization path, an NA and a corresponding error message are
#' logged instead.
#' Note that this value is later multiplied by -1 for maximization measures internally, so you
#' need to enter a larger positive value for maximization here as well.
#' Default is the worst obtainable value of the performance measure you optimize for when
#' you aggregate by mean value, or \code{Inf} instead.
#' For multi-criteria optimization pass a vector of imputation values, one for each of your measures,
#' in the same order as your measures.
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