def METHOD_NAME(self, batch_data, config):
    dense_feature_dim = config.get('hyper_parameters.dense_input_dim')
    sparse_tensor = []
    for b in batch_data[:-1]:
        sparse_tensor.append(
            paddle.to_tensor(b.numpy().astype('int64').reshape(-1, 1)))
    dense_tensor = paddle.to_tensor(batch_data[-1].numpy().astype(
        'float32').reshape(-1, dense_feature_dim))
    label = sparse_tensor[0]
    return label, sparse_tensor[1:], dense_tensor