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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_outputs(self, merge_multi_context=True):
 """Gets outputs of the previous forward computation. If ``merge_multi_context`` is ``True``, it is like ``[out1, out2]``. Otherwise, it is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output elements are `NDArray`. When `merge_multi_context` is `False`, those `NDArray` might live on different devices. Parameters merge_multi_context : bool Default is ``True``. In the case when data-parallelism is used, the outputs will be collected from multiple devices. A ``True`` value indicate that we should merge the collected results so that they look like from a single executor. Returns ------- list of NDArray or list of list of NDArray Output. """

        assert self.binded and self.params_initialized
        return self._exec_group.get_outputs(merge_multi_context=merge_multi_context)