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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 prepare(self, data_batch, sparse_row_id_fn=None):
 """Prepares two modules for processing a data batch. Usually involves switching bucket and reshaping. For modules that contain `row_sparse` parameters in KVStore, it prepares the `row_sparse` parameters based on the sparse_row_id_fn. When KVStore is used to update parameters for multi-device or multi-machine training, a copy of the parameters are stored in KVStore. Note that for `row_sparse` parameters, the `update()` updates the copy of parameters in KVStore, but doesn't broadcast the updated parameters to all devices / machines. The `prepare` function is used to broadcast `row_sparse` parameters with the next batch of data. Parameters data_batch : DataBatch The current batch of data for forward computation. sparse_row_id_fn : A callback function The function takes `data_batch` as an input and returns a dict of str -> NDArray. The resulting dict is used for pulling row_sparse parameters from the kvstore, where the str key is the name of the param, and the value is the row id of the param to pull. """

        super(SVRGModule, self).prepare(data_batch, sparse_row_id_fn=sparse_row_id_fn)
        self._mod_aux.prepare(data_batch, sparse_row_id_fn=sparse_row_id_fn)