code: def forward(self, data_batch, is_train=None):
          
          super(SVRGModule, self).forward(data_batch, is_train)
  
          if is_train:
              self._mod_aux.forward(data_batch, is_train)
docs: Forward computation for both two modules. It supports data batches with different shapes, such as
          different batch sizes or different image sizes.
          If reshaping of data batch relates to modification of symbol or module, such as
          changing image layout ordering or switching from training to predicting, module
          rebinding is required.
  
          See Also
          ----------
          :meth:`BaseModule.forward`.
  
          Parameters
          ----------
          data_batch : DataBatch
              Could be anything with similar API implemented.
          is_train : bool
              Default is ``None``, which means ``is_train`` takes the value of ``self.for_training``.