class_docstr: Performs a ``FeatureUnion`` of steps, and then send the joined result to the above judge step.
  
  Usage example:
  
  .. code-block:: python
  
      model_stacking = Pipeline([
          ModelStacking([
              SKLearnWrapper(
                  GradientBoostingRegressor(),
                  HyperparameterSpace({
                      "n_estimators": RandInt(50, 600), "max_depth": RandInt(1, 10),
                      "learning_rate": LogUniform(0.07, 0.7)
                  })
              ),
              SKLearnWrapper(
                  KMeans(),
                  HyperparameterSpace({
                      "n_clusters": RandInt(5, 10)
                  })
              ),
          ],
              joiner=NumpyTranspose(),
              judge=SKLearnWrapper(
                  Ridge(),
                  HyperparameterSpace({
                      "alpha": LogUniform(0.7, 1.4),
                      "fit_intercept": Boolean()
                  })
              ),
          )
      ])
  
  .. seealso::
      :class:`FeatureUnion`,
      :class:`AddFeatures`,
      :class:`~neuraxle.base.ForceHandleOnlyMixin`,
      :class:`~neuraxle.base.TruncableSteps`,
      :class:`~neuraxle.base.BaseStep`
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docstr: Transform the data with the unions. It will make use of some parallel processing.
  Then, use the judge to refine the transformations.
  :param data_container: data container to transform
  :param context: execution context
docstr_tok: ["Transform","the","data","with","the","unions",".","It","will","make","use","of","some","parallel","processing",".","Then",",","use","the","judge","to","refine","the","transformations",".",":","param","data_container",":","data","container","to","transform",":","param","context",":","execution","context"]
code: def _did_transform(self, data_container, context) -> DACT:
      """
      Transform the data with the unions. It will make use of some parallel processing.
      Then, use the judge to refine the transformations.
      :param data_container: data container to transform
      :param context: execution context
      """
      data_container = super()._did_transform(data_container, context)
  
      results = self.judge.handle_transform(data_container, context)
      data_container.set_data_inputs(results.data_inputs)
  
      return data_container
  
imports: from typing import Tuple
  from joblib import Parallel, delayed
  from neuraxle.base import CX, DACT, BaseStep, BaseTransformer, ForceHandleOnlyMixin, Identity, NamedStepsList, NonFittableMixin, TruncableSteps, _TruncableServiceWithBodyMixin
  from neuraxle.data_container import ZipDataContainer
  from neuraxle.steps.numpy import NumpyConcatenateInnerFeatures
label_desc: Use image node_id 4 for calling the ModelStacking obj's underlying member method code with example usage: obj._did_transform(data_container, context) and returns: data_container