def METHOD_NAME(self):
    dense_inst = []
    headers = ['x' + str(i) for i in range(20)]
    anonymous_header = ["guest_9999_x" + str(i) for i in range(20)]
    for i in range(100):
        inst = Instance(features=(i % 16 * np.ones(20)))
        dense_inst.append((i, inst))
    self.dense_table = session.parallelize(dense_inst, include_key=True, partition=2)
    self.dense_table.schema = {'header': headers,
                               "anonymous_header": anonymous_header}
    self.sparse_inst = []
    for i in range(100):
        dict = {}
        indices = []
        data = []
        for j in range(20):
            idx = random.randint(0, 29)
            if idx in dict:
                continue
            dict[idx] = 1
            val = random.random()
            indices.append(idx)
            data.append(val)
        sparse_vec = SparseVector(indices, data, 30)
        self.sparse_inst.append((i, Instance(features=sparse_vec)))
    self.sparse_table = session.parallelize(self.sparse_inst, include_key=True, partition=48)
    self.sparse_table.schema = {"header": ["fid" + str(i) for i in range(30)]}
    # self.sparse_table = eggroll.parallelize(sparse_inst, include_key=True, partition=1)