title: How to predict new datasets after the `tune_nested`?
body: <pre><code># retrieve task
  task = tsk(&quot;pima&quot;)
  
  # load learner and set search space
  learner = lrn(&quot;classif.rpart&quot;, cp = to_tune(1e-04, 1e-1, logscale = TRUE))
  
  # nested resampling
  rr = tune_nested(
    method = &quot;random_search&quot;,
    task =  task,
    learner = learner,
    inner_resampling = rsmp(&quot;holdout&quot;),
    outer_resampling = rsmp(&quot;cv&quot;, folds = 3),
    measure = msr(&quot;classif.ce&quot;),
    term_evals = 10,
    batch_size = 5
  )
  </code></pre>
  <p>So if now I define a new dataset:</p>
  <pre><code>new_data = as.data.table(task)[1:10,]
  </code></pre>
  <p>How can I predict the result of the new_data?</p>