title: Unable to train dataset by mlr3proba after encoding and scaling it with mlr3pipeline
body: <p>when I run the code below for training a model in mlr3proba after encoding and scaling my dataset with mlr3pipeline:</p>
  <pre><code>task =tsk(&quot;sonar&quot;)
  learner = lrn(&quot;classif.rpart&quot;)
  measure = msr(&quot;classif.ce&quot;)
  inner.rsmp &lt;- rsm(&quot;cv&quot;, folds = 5)
  train_set = sample(task$nrow, 0.8 * task$nrow)
  test_set = setdiff(seq_len(task$nrow), train_set)
  
  learner &lt;- po(&quot;encode&quot;) %&gt;&gt;% po(&quot;scale&quot;) %&gt;&gt;% po(&quot;learner&quot;, learner)
  learner$train(task, row_ids = train_set)
  </code></pre>
  <p>R code shows an error as below:</p>
  <pre><code>Error in learner$train(task, row_ids = train_set) : 
    unused argument (row_ids = train_set)
  </code></pre>
  <p>I tried this in another dataset but it shows the same issues.</p>
  <p>but if I don't encode and scale my dataset, everything works.</p>
  <p>Also, For <code>resample()</code> function, it is ok (despite encoding and scaling):</p>
  <pre><code>rr &lt;- resample(task, learner, inner.rsmp)
  rr$aggregate(measure)
  
  #Results:
  
  INFO  [08:46:55.411] [mlr3]  Applying learner 'encode.scale.classif.rpart' on task 'sonar' (iter 4/5) 
  INFO  [08:46:55.539] [mlr3]  Applying learner 'encode.scale.classif.rpart' on task 'sonar' (iter 1/5) 
  INFO  [08:46:55.644] [mlr3]  Applying learner 'encode.scale.classif.rpart' on task 'sonar' (iter 2/5) 
  INFO  [08:46:55.773] [mlr3]  Applying learner 'encode.scale.classif.rpart' on task 'sonar' (iter 5/5) 
  INFO  [08:46:55.876] [mlr3]  Applying learner 'encode.scale.classif.rpart' on task 'sonar' (iter 3/5)
  
  rr$score(measure)
  
                  task task_id            learner                 learner_id         resampling
  1: &lt;TaskClassif[46]&gt;   sonar &lt;GraphLearner[33]&gt; encode.scale.classif.rpart &lt;ResamplingCV[19]&gt;
  2: &lt;TaskClassif[46]&gt;   sonar &lt;GraphLearner[33]&gt; encode.scale.classif.rpart &lt;ResamplingCV[19]&gt;
  3: &lt;TaskClassif[46]&gt;   sonar &lt;GraphLearner[33]&gt; encode.scale.classif.rpart &lt;ResamplingCV[19]&gt;
  4: &lt;TaskClassif[46]&gt;   sonar &lt;GraphLearner[33]&gt; encode.scale.classif.rpart &lt;ResamplingCV[19]&gt;
  5: &lt;TaskClassif[46]&gt;   sonar &lt;GraphLearner[33]&gt; encode.scale.classif.rpart &lt;ResamplingCV[19]&gt;
     resampling_id iteration              prediction classif.ce
  1:            cv         1 &lt;PredictionClassif[19]&gt;  0.3333333
  2:            cv         2 &lt;PredictionClassif[19]&gt;  0.2142857
  3:            cv         3 &lt;PredictionClassif[19]&gt;  0.2380952
  4:            cv         4 &lt;PredictionClassif[19]&gt;  0.3658537
  5:            cv         5 &lt;PredictionClassif[19]&gt;  0.2439024
  </code></pre>
  <p>So where is the problem?</p>