def get_outputs(self, merge_multi_context=True):
        """Gets outputs from a previous forward computation.

        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
            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 numpy arrays.
        """
        assert self.binded and self.params_initialized
        return self._modules[-1].get_outputs(merge_multi_context=merge_multi_context)
def get_outputs ( self , merge_multi_context = True ) : assert self . binded and self . params_initialized return self . _modules [ - 1 ] . get_outputs ( merge_multi_context = merge_multi_context )
Gets outputs from a previous forward computation.

        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
            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 numpy arrays.