RV1266是否支持文本分类的LSTM+ATTR模型

1.我进行onnx转rknn出现单batch报错，多batch可以正常转换的情况。单batch转换出现ValueError: Try match layer: MatMul_/MatMulut0 failed!
E Try match layer: MatMul_/MatMulut0 failed!
The OP of this layer may not yet support, please check document <RKNN_OP_Support.md>!
E Catch exception when loading onnx model: LSTM_FULL_attr_1000_bs1.onnx!
E Traceback (most recent call last):
E   File "rknn/base/RKNNlib/converter/convert_onnx.py", line 1566, in rknn.base.RKNNlib.converter.convert_onnx.convert_onnx._onnx_build_acu_layer
E   File "rknn/base/RKNNlib/converter/convert_onnx.py", line 1535, in rknn.base.RKNNlib.converter.convert_onnx.convert_onnx._onnx_acu_param_assign
E   File "rknn/base/RKNNlib/converter/convert_onnx.py", line 1427, in rknn.base.RKNNlib.converter.convert_onnx.convert_onnx._onnx_parse_param
E   File "rknn/base/RKNNlib/converter/convert_onnx.py", line 1434, in rknn.base.RKNNlib.converter.convert_onnx.convert_onnx._onnx_parase_execute
E   File "<string>", line 1, in <module>
E IndexError: list index out of range
E During handling of the above exception, another exception occurred:
E Traceback (most recent call last):
E   File "rknn/api/rknn_base.py", line 376, in rknn.api.rknn_base.RKNNBase.load_onnx
E   File "rknn/base/RKNNlib/RK_nn.py", line 151, in rknn.base.RKNNlib.RK_nn.RKnn.load_onnx
E   File "rknn/base/RKNNlib/app/importer/import_onnx.py", line 131, in rknn.base.RKNNlib.app.importer.import_onnx.Importonnx.run
E   File "rknn/base/RKNNlib/converter/convert_onnx.py", line 1871, in rknn.base.RKNNlib.converter.convert_onnx.convert_onnx.match_paragraph_and_param
E   File "rknn/api/rknn_log.py", line 323, in rknn.api.rknn_log.RKNNLog.e
E ValueError: Try match layer: MatMul_/MatMulut0 failed!
E The OP of this layer may not yet support, please check document <RKNN_OP_Support.md>!
E Please feedback the detailed log file <log_feedback_to_the_rknn_toolkit_dev_team.log> to the RKNN Toolkit development team.
E You can also check github issues: https://github.com/rockchip-linux/rknn-toolkit/issues

2.多batch
但是使用多batch进行推理，出现精度问题，和onnx推理的结果差距很大