Binds the symbols to construct executors for both two modules. This is necessary before one
        can perform computation with the SVRGModule.

        Parameters
        ----------
        data_shapes : list of (str, tuple)
            Typically is ``data_iter.provide_data``.
        label_shapes : list of (str, tuple)
            Typically is ``data_iter.provide_label``.
        for_training : bool
            Default is ``True``. Whether the executors should be bound for training.
        inputs_need_grad : bool
            Default is ``False``. Whether the gradients to the input data need to be computed.
            Typically this is not needed. But this might be needed when implementing composition
            of modules.
        force_rebind : bool
            Default is ``False``. This function does nothing if the executors are already
            bound. But with this ``True``, the executors will be forced to rebind.
        shared_module : Module
            Default is ``None``. This is used in bucketing. When not ``None``, the shared module
            essentially corresponds to a different bucket -- a module with different symbol
            but with the same sets of parameters (e.g. unrolled RNNs with different lengths).

def bind(self, data_shapes, label_shapes=None, for_training=True,
             inputs_need_grad=False, force_rebind=False, shared_module=None, grad_req='write'):
        """Binds the symbols to construct executors for both two modules. This is necessary before one
        can perform computation with the SVRGModule.

        Parameters
        ----------
        data_shapes : list of (str, tuple)
            Typically is ``data_iter.provide_data``.
        label_shapes : list of (str, tuple)
            Typically is ``data_iter.provide_label``.
        for_training : bool
            Default is ``True``. Whether the executors should be bound for training.
        inputs_need_grad : bool
            Default is ``False``. Whether the gradients to the input data need to be computed.
            Typically this is not needed. But this might be needed when implementing composition
            of modules.
        force_rebind : bool
            Default is ``False``. This function does nothing if the executors are already
            bound. But with this ``True``, the executors will be forced to rebind.
        shared_module : Module
            Default is ``None``. This is used in bucketing. When not ``None``, the shared module
            essentially corresponds to a different bucket -- a module with different symbol
            but with the same sets of parameters (e.g. unrolled RNNs with different lengths).
        """
        # force rebinding is typically used when one want to switch from
        # training to prediction phase.
        super(SVRGModule, self).bind(data_shapes, label_shapes, for_training, inputs_need_grad, force_rebind,
                                     shared_module, grad_req)

        if for_training:
            self._mod_aux.bind(data_shapes, label_shapes, for_training, inputs_need_grad, force_rebind, shared_module,
                               grad_req)