diff --git a/.jenkins/check/config/filter_pylint.txt b/.jenkins/check/config/filter_pylint.txt
index ac7405289b13bf690f582cc1ec6938c2c6078423..cfe1b1b50b637bb2f82516b87d187595bf097327 100644
--- a/.jenkins/check/config/filter_pylint.txt
+++ b/.jenkins/check/config/filter_pylint.txt
@@ -18,6 +18,7 @@
 "mindspore-lite/mindspore-lite/tools/kernel_builder/ascend/akg/custom.py"                                     "consider-using-in"
 "mindspore-lite/mindspore-lite/tools/kernel_builder/ascend/akg/custom.py"                                     "unused-argument"
 "mindspore-lite/mindspore-lite/python/api/lite_infer.py"                                                      "arguments-differ"
+"mindspore-lite/mindspore-lite/test/st/python/test_inference_cloud.py"                                        "unused-argument"
 
 # ascend samples
 "mindspore-lite/mindspore-lite/tools/kernel_builder/ascend/tbe_dsl/sample/"                              "wrong-import-order"
diff --git a/mindspore-lite/examples/cloud_infer/ascend_ge_distributed_java/src/main/java/com/mindspore/lite/demo/Main.java b/mindspore-lite/examples/cloud_infer/ascend_ge_distributed_java/src/main/java/com/mindspore/lite/demo/Main.java
index d50c55edfe49c406928ed68c56af4dea9e6feee0..89afe93f5e6d5fa7283a5f551923fa29547cc796 100644
--- a/mindspore-lite/examples/cloud_infer/ascend_ge_distributed_java/src/main/java/com/mindspore/lite/demo/Main.java
+++ b/mindspore-lite/examples/cloud_infer/ascend_ge_distributed_java/src/main/java/com/mindspore/lite/demo/Main.java
@@ -19,7 +19,6 @@ package com.mindspore.lite.demo;
 import com.mindspore.MSTensor;
 import com.mindspore.Model;
 import com.mindspore.config.DataType;
-import com.mindspore.config.DeviceType;
 import com.mindspore.config.MSContext;
 import com.mindspore.config.ModelType;
 import com.mindspore.config.Version;
@@ -27,7 +26,6 @@ import com.mindspore.config.AscendDeviceInfo;
 
 import java.nio.ByteBuffer;
 import java.nio.ByteOrder;
-import java.sql.DataTruncation;
 import java.util.*;
 
 public class Main {
@@ -46,7 +44,7 @@ public class Main {
         Random rand = new Random();
         byte[] arr = new byte[len];
         for (int i = 0; i < arr.length; i++) {
-            arr[i] = (byte)0;
+            arr[i] = (byte) 0;
         }
         return arr;
     }
diff --git a/mindspore-lite/examples/cloud_infer/quick_start_parallel_java/src/main/java/com/mindspore/lite/demo/Main.java b/mindspore-lite/examples/cloud_infer/quick_start_parallel_java/src/main/java/com/mindspore/lite/demo/Main.java
index 96e1fa2ba07baf2c24dbe0255b416a45d4746787..184f21d44d9acd4ae79670f50321654b718af638 100644
--- a/mindspore-lite/examples/cloud_infer/quick_start_parallel_java/src/main/java/com/mindspore/lite/demo/Main.java
+++ b/mindspore-lite/examples/cloud_infer/quick_start_parallel_java/src/main/java/com/mindspore/lite/demo/Main.java
@@ -21,8 +21,6 @@ import com.mindspore.config.MSContext;
 import com.mindspore.config.RunnerConfig;
 import com.mindspore.ModelParallelRunner;
 import com.mindspore.MSTensor;
-import com.mindspore.Model;
-import com.mindspore.config.ModelType;
 import com.mindspore.config.Version;
 
 import java.nio.ByteBuffer;
@@ -32,9 +30,7 @@ import java.util.Random;
 
 import java.nio.ByteBuffer;
 import java.nio.FloatBuffer;
-import java.nio.IntBuffer;
 import java.util.ArrayList;
-import java.util.Arrays;
 import java.util.List;
 
 public class Main {
@@ -62,7 +58,7 @@ public class Main {
         return buffer;
     }
 
-    private static void freeTensor(){
+    private static void freeTensor() {
         for (int i = 0; i < inputs.size(); i++) {
             inputs.get(i).free();
         }
@@ -81,7 +77,7 @@ public class Main {
 
         // use default param init context
         MSContext context = new MSContext();
-        context.init(1,0);
+        context.init(1, 0);
         boolean ret = context.addDeviceInfo(DeviceType.DT_CPU, false, 0);
         if (!ret) {
             System.err.println("init context failed");
@@ -129,7 +125,7 @@ public class Main {
         List<MSTensor> outputs = new ArrayList<>();
 
         // runner do predict
-        ret = runner.predict(inputs,outputs);
+        ret = runner.predict(inputs, outputs);
         if (!ret) {
             System.err.println("MindSpore Lite predict failed.");
             freeTensor();
diff --git a/mindspore-lite/examples/export_models/models/densenet_train_export.py b/mindspore-lite/examples/export_models/models/densenet_train_export.py
index f4f59f69a63dadff898e8eb7d611c026792869da..0996091351c700da43700766dc0afb15f2ff317d 100644
--- a/mindspore-lite/examples/export_models/models/densenet_train_export.py
+++ b/mindspore-lite/examples/export_models/models/densenet_train_export.py
@@ -18,10 +18,10 @@ import sys
 import os
 import numpy as np
 from train_utils import save_inout, train_wrap
+from src.network.densenet import DenseNet121
 import mindspore.common.dtype as mstype
 from mindspore import context, Tensor, nn
 from mindspore.train.serialization import export
-from src.network.densenet import DenseNet121
 #pylint: disable=wrong-import-position
 sys.path.append(os.environ['CLOUD_MODEL_ZOO'] + 'official/cv/densenet121/')
 
diff --git a/mindspore-lite/examples/export_models/models/tinybert_train_export.py b/mindspore-lite/examples/export_models/models/tinybert_train_export.py
index 125f2f8cbb0a6665d9c1ecc84d5dca4ab858e7e1..5f6944f8f6d20b65f089078eb45ca765b6d8b4cd 100644
--- a/mindspore-lite/examples/export_models/models/tinybert_train_export.py
+++ b/mindspore-lite/examples/export_models/models/tinybert_train_export.py
@@ -18,6 +18,9 @@
 import os
 import sys
 import numpy as np
+from official.nlp.tinybert.src.tinybert_model import TinyBertModel  # noqa: 402
+from official.nlp.tinybert.src.model_utils.config import bert_student_net_cfg  # noqa: 402
+from train_utils import save_t  # noqa: 402
 import mindspore as M
 from mindspore.ops import operations as P
 from mindspore.ops import composite as C
@@ -32,9 +35,6 @@ else:
     path = ''
 sys.path.append(os.environ['CLOUD_MODEL_ZOO'] + 'official/nlp/tinybert')
 
-from official.nlp.tinybert.src.tinybert_model import TinyBertModel  # noqa: 402
-from official.nlp.tinybert.src.model_utils.config import bert_student_net_cfg  # noqa: 402
-from train_utils import save_t  # noqa: 402
 
 
 class BertNetworkWithLossGenDistill(M.nn.Cell):
diff --git a/mindspore-lite/examples/quick_start_java/src/main/java/com/mindspore/lite/demo/Main.java b/mindspore-lite/examples/quick_start_java/src/main/java/com/mindspore/lite/demo/Main.java
index fd124635a1a86b07a7c2b71577f3eea5c852942f..2cf7e4158f34494de69280a2ddb36fe635a4bb41 100644
--- a/mindspore-lite/examples/quick_start_java/src/main/java/com/mindspore/lite/demo/Main.java
+++ b/mindspore-lite/examples/quick_start_java/src/main/java/com/mindspore/lite/demo/Main.java
@@ -25,8 +25,6 @@ import com.mindspore.config.ModelType;
 import com.mindspore.config.Version;
 
 import java.io.File;
-import java.nio.file.Path;
-import java.nio.file.Paths;
 import java.nio.ByteBuffer;
 import java.nio.ByteOrder;
 import java.nio.FloatBuffer;
diff --git a/mindspore-lite/examples/train_lenet_cpp/model/lenet_export.py b/mindspore-lite/examples/train_lenet_cpp/model/lenet_export.py
index cb03e8e6c71153502b3d3920b3cbc04315d89f47..900a0e5ff96231e52d3daf419fa09441110d60ed 100644
--- a/mindspore-lite/examples/train_lenet_cpp/model/lenet_export.py
+++ b/mindspore-lite/examples/train_lenet_cpp/model/lenet_export.py
@@ -17,11 +17,11 @@
 import sys
 import os
 import numpy as np
+from lenet import LeNet5
+from train_utils import train_wrap
 from mindspore import context, Tensor
 import mindspore.common.dtype as mstype
 from mindspore.train.serialization import export
-from lenet import LeNet5
-from train_utils import train_wrap
 
 
 n = LeNet5()
diff --git a/mindspore-lite/examples/train_lenet_java/model/lenet_export.py b/mindspore-lite/examples/train_lenet_java/model/lenet_export.py
index 6429f9e1882a6c5d01d5c1d099f389780e231282..c8e9f67a1899ea440d537e14269cf7d4fa63f8b7 100644
--- a/mindspore-lite/examples/train_lenet_java/model/lenet_export.py
+++ b/mindspore-lite/examples/train_lenet_java/model/lenet_export.py
@@ -16,11 +16,11 @@
 
 import os
 import numpy as np
+from lenet import LeNet5
+from train_utils import train_wrap
 from mindspore import context, Tensor
 import mindspore.common.dtype as mstype
 from mindspore.train.serialization import export
-from lenet import LeNet5
-from train_utils import train_wrap
 
 n = LeNet5()
 n.set_train()
diff --git a/mindspore-lite/examples/train_lenet_java/src/main/java/com/mindspore/lite/train_lenet/DataSet.java b/mindspore-lite/examples/train_lenet_java/src/main/java/com/mindspore/lite/train_lenet/DataSet.java
index f27c1de19c00e1df8915ad651e9efa7ee6063f41..31cfd7164fdcbf663a0a0a9862359b1a21b306db 100644
--- a/mindspore-lite/examples/train_lenet_java/src/main/java/com/mindspore/lite/train_lenet/DataSet.java
+++ b/mindspore-lite/examples/train_lenet_java/src/main/java/com/mindspore/lite/train_lenet/DataSet.java
@@ -35,8 +35,8 @@ public class DataSet {
         numOfClasses = 10;
         trainData = new Vector<DataLabelTuple>();
         testData = new Vector<DataLabelTuple>();
-        readMNISTFile(dpath + "/train/train-images-idx3-ubyte", dpath+"/train/train-labels-idx1-ubyte", trainData);
-        readMNISTFile(dpath + "/test/t10k-images-idx3-ubyte", dpath+"/test/t10k-labels-idx1-ubyte", testData);
+        readMNISTFile(dpath + "/train/train-images-idx3-ubyte", dpath + "/train/train-labels-idx1-ubyte", trainData);
+        readMNISTFile(dpath + "/test/t10k-images-idx3-ubyte", dpath + "/test/t10k-labels-idx1-ubyte", testData);
 
         System.out.println("train data cnt: " + trainData.size());
         System.out.println("test data cnt: " + testData.size());
diff --git a/mindspore-lite/examples/transfer_learning/model/transfer_learning_export.py b/mindspore-lite/examples/transfer_learning/model/transfer_learning_export.py
index 19082f4ea862562f3e7e93016370ec40789188cf..870d29cfd75669bfe821bab7c851cf897f80e685 100755
--- a/mindspore-lite/examples/transfer_learning/model/transfer_learning_export.py
+++ b/mindspore-lite/examples/transfer_learning/model/transfer_learning_export.py
@@ -16,11 +16,11 @@
 
 import os
 import numpy as np
+from effnet import effnet
+from train_utils import train_wrap
 import mindspore as M
 from mindspore.nn import Cell
 from mindspore.train.serialization import load_checkpoint, export
-from effnet import effnet
-from train_utils import train_wrap
 
 
 class TransferNet(Cell):
diff --git a/mindspore-lite/test/st/java/src/main/java/Benchmark.java b/mindspore-lite/test/st/java/src/main/java/Benchmark.java
index 683d749d219392258dde515077fbd29cdd3f17df..4fd4bc73fd239da34b4415cd9fba16e3d0422880 100644
--- a/mindspore-lite/test/st/java/src/main/java/Benchmark.java
+++ b/mindspore-lite/test/st/java/src/main/java/Benchmark.java
@@ -88,8 +88,8 @@ public class Benchmark {
                             } else {
                                 meanError += absoluteError / (Math.abs(benchmarkData[i]) + Float.MIN_VALUE);
                                 errorCount++;
-                            }                            
-                        } 
+                            }
+                        }
                     }
 
                     if (meanError > 0.0f) {
@@ -109,7 +109,7 @@ public class Benchmark {
         }
         return meanError < accuracy;
     }
-    
+
     public static void main(String[] args) {
         if (args.length < 4) {
             System.err.println("We must pass parameters such as modelPath, inDataFile, benchmarkDataFile and accuracy.");
@@ -123,7 +123,7 @@ public class Benchmark {
         if (args.length == 5 && args[4].equals("Runner")) {
             // use default param init context
             MSContext context = new MSContext();
-            context.init(1,0);
+            context.init(1, 0);
             boolean ret = context.addDeviceInfo(DeviceType.DT_CPU, false, 0);
             if (!ret) {
                 System.err.println("init context failed");
@@ -158,7 +158,7 @@ public class Benchmark {
             List<MSTensor> outputs = new ArrayList<>();
 
             // runner do predict
-            ret = runner.predict(inputs,outputs);
+            ret = runner.predict(inputs, outputs);
             if (!ret) {
                 System.err.println("MindSpore Lite predict failed.");
                 runner.free();
diff --git a/mindspore-lite/test/st/ops/frame/configs.py b/mindspore-lite/test/st/ops/frame/configs.py
index 2c174185325bdc14e433791e326fefcbb5147fb9..1eb04c0d48e967e9152cf466566f5cecc3a6722c 100644
--- a/mindspore-lite/test/st/ops/frame/configs.py
+++ b/mindspore-lite/test/st/ops/frame/configs.py
@@ -146,8 +146,6 @@ class OnnxModelConfig:
             for graph_input in each_model["graph_param"]["inputs"]:
                 each_input_name.append(graph_input["name"])
             self.graph_input_names[each_model["model_name"]] = each_input_name
-        # for graph_input in self.op_configs_["op_configs"][0]["graph_param"]["inputs"]:
-        #     self.graph_input_names.append(graph_input["name"])
 
     def modelconfigs(self):
         return self.op_configs_
diff --git a/mindspore-lite/test/st/ops/frame/optest.py b/mindspore-lite/test/st/ops/frame/optest.py
index a563318dda673e1a51f5db1d52a8187b5ae623e9..57d891fa6d75e7809e04e17b1f90f56613098ed2 100644
--- a/mindspore-lite/test/st/ops/frame/optest.py
+++ b/mindspore-lite/test/st/ops/frame/optest.py
@@ -51,8 +51,9 @@ class OpTest:
             os.mkdir(self.output_path)
         conf.logger.info("use mslite benchmark: %s", self.mslite_benchmark_path)
 
-## 因为ms默认输入格式为NHWC，将onnx模型的NCHW格式输入转为NHWC格式
-# 将所有标杆数据 gold_name:shapes 转换为 X1:dim1, dim2;X2:dim1, dim2...的格式
+
+# Because the default input format of MS is NHWC, convert the NCHW format input of the ONNX model to NHWC format
+# Convert all benchmark data gold_name:shapes to the format of X1:dim1, dim2;X2:dim1, dim2...
     def get_run_input_shapes(self, run_config):
         run_input_shapes_ = {}
         input_shapes = self.golden_confis.gold_input_name_dict[run_config["gold_in"]]
@@ -124,8 +125,6 @@ class OpTest:
                 initializer=initializers,
             )
             model = helper.make_model(graph, producer_name="test", opset_imports=[helper.make_opsetid("", 18)])
-            # for opset in model.opset_import:
-            #     print(f"Domain: {opset.domain}, Version: {opset.version}")
             onnx.checker.check_model(model)
             model.ir_version = 8
 
@@ -148,7 +147,6 @@ class OpTest:
             )
             onnx.checker.check_model(model)
 
-            # conf.logger.debug(onnx.helper.printable_graph(model.graph))
             # dtypes默认fp32
             input_tensors = []
             input_feeds = {}
@@ -290,8 +288,6 @@ class OpTest:
     def run_models(self):
         run_configs = self.run_configs.run_configs_
 
-        # run_input_shapes = self.get_run_input_shapes()
-
         for run_config in run_configs:
             in_model_path = self.output_path + "/ms_models/" + run_config["in_model"]
 
@@ -304,7 +300,6 @@ class OpTest:
                 )
                 continue
 
-            # dtypes = type_map[run_config["dtypes"]]
             run_input_shapes = self.get_run_input_shapes(run_config)
 
             input_shapes = run_input_shapes[run_config["gold_in"]]
@@ -326,10 +321,6 @@ class OpTest:
                 f"--benchmarkDataFile={gold_out_param}",
                 f"--inputShape={input_shapes}",
             ]
-            # if input_shapes != "None":
-            #     args.append(
-            #         f"--inputShape={input_shapes}",
-            #     )
             run_command = ""
             for arg in args:
                 run_command += arg + " "
diff --git a/mindspore-lite/test/st/python/import_ms_and_mslite/test_api_import_mslite_and_ms.py b/mindspore-lite/test/st/python/import_ms_and_mslite/test_api_import_mslite_and_ms.py
index d52ddd901324146c6e412a59333006f18d082f62..6eb2ed5fb415f7be0b2a20427ebe81e121ca4885 100644
--- a/mindspore-lite/test/st/python/import_ms_and_mslite/test_api_import_mslite_and_ms.py
+++ b/mindspore-lite/test/st/python/import_ms_and_mslite/test_api_import_mslite_and_ms.py
@@ -20,7 +20,6 @@ import numpy as np
 import mindspore_lite as mslite
 import mindspore
 
-
 # ============================ ut testcases ============================
 # ============================ Context ============================
 def test_context_construct():
diff --git a/mindspore-lite/test/st/python/import_ms_and_mslite/test_predict_backend_lite_resnet50.py b/mindspore-lite/test/st/python/import_ms_and_mslite/test_predict_backend_lite_resnet50.py
index 419c000829d50f50b85799fe5d0d74ffd54d5f91..a3a0c9c84e3ce3406af8f235b569d9ae4b0e2da1 100644
--- a/mindspore-lite/test/st/python/import_ms_and_mslite/test_predict_backend_lite_resnet50.py
+++ b/mindspore-lite/test/st/python/import_ms_and_mslite/test_predict_backend_lite_resnet50.py
@@ -23,10 +23,10 @@ import os
 
 import numpy as np
 
-import mindspore as ms
-from mindspore import context
 from lite_infer_predict_utils import predict_backend_lite, _get_max_index_from_res
 from resnet import resnet50
+import mindspore as ms
+from mindspore import context
 
 
 # pylint: disable=I1101
diff --git a/mindspore-lite/test/st/python/optimize_pass/test_concat_op_pass.py b/mindspore-lite/test/st/python/optimize_pass/test_concat_op_pass.py
index 50160abea8fca7361018bfcb7547cf7e46337769..b8229dea0686e0c09729115267e26e641b0badad 100644
--- a/mindspore-lite/test/st/python/optimize_pass/test_concat_op_pass.py
+++ b/mindspore-lite/test/st/python/optimize_pass/test_concat_op_pass.py
@@ -40,12 +40,14 @@ class ConcatOpPassNet(nn.Cell):
         key_paddings = self.concat((Tensor([0, 0, 0, 0, 0], mstype.int64), pad_length, Tensor([0, 0], mstype.int64)))
         return key_paddings
 
+
 def dummy_tensor(shape, dtype):
     """create dummy tensor"""
     if None in shape:
         return Tensor(shape=shape, dtype=dtype)
     return Tensor(np.ones(shape=tuple(shape)), dtype=dtype)
 
+
 def export_model():
     """
     export model
diff --git a/mindspore-lite/test/st/python/python_api/test_lite_llm_engine_api.py b/mindspore-lite/test/st/python/python_api/test_lite_llm_engine_api.py
index ae55e47ea6e73903dc2cc98ff500a66eb75b160f..93f378f0ecaaf89052fb668dd29e2d797eca910f 100644
--- a/mindspore-lite/test/st/python/python_api/test_lite_llm_engine_api.py
+++ b/mindspore-lite/test/st/python/python_api/test_lite_llm_engine_api.py
@@ -42,6 +42,7 @@ def test_lite_llm_engine_cluster_info_cluster_id_type_check():
         llm_cluster.remote_cluster_id = "0"
     assert "remote_cluster_id must be int, but got" in str(raise_info.value)
 
+
 # ============================ LLMEngine ============================
 def test_lite_llm_engine_llm_engine_role_type_check():
     with pytest.raises(TypeError) as raise_info:
@@ -168,6 +169,7 @@ def test_lite_llm_engine_llm_engine_fetch_status_check():
         llm_engine.fetch_status()
     assert "LLMEngine is not inited or init failed" in str(raise_info.value)
 
+
 # ============================ LLMModel ============================
 def test_lite_llm_engine_llm_model_predict_check():
     cluster_id = 0
diff --git a/mindspore-lite/test/st/python/test_inference_cloud.py b/mindspore-lite/test/st/python/test_inference_cloud.py
index da2006792f9aba3ce34cea2ec78899f16515bbfd..ee3c65c36e09dd35dd18f331549ed327fffeca6a 100644
--- a/mindspore-lite/test/st/python/test_inference_cloud.py
+++ b/mindspore-lite/test/st/python/test_inference_cloud.py
@@ -168,6 +168,7 @@ def test_model_group_for_ascend(model_path, in_data_path, input_shapes):
     # use model two for inference
     test_model_inference_ascend(model_file, in_data_file_list, shapes)
 
+
 def test_input_shape_for_ascend(model_path, input_shape_str):
     context = mslite.Context()
     context.target = ["ascend"]
@@ -179,6 +180,7 @@ def test_input_shape_for_ascend(model_path, input_shape_str):
     model.build_from_file(model_path, mslite.ModelType.MINDIR, context)
     input_shape_config = model.get_model_info("input_shape")
 
+
 def test_model_group_weight_workspace_for_ascend(model_path, in_data_path, input_shapes):
     # init model group context
     model_group_context = mslite.Context()
diff --git a/mindspore-lite/test/ut/python/test_inference_api.py b/mindspore-lite/test/ut/python/test_inference_api.py
index 69cf3d9d023ff73a1346f6356ad9f033656f1ad9..8bf9dd64e04872968da203fcf5c22130cb90e7e7 100644
--- a/mindspore-lite/test/ut/python/test_inference_api.py
+++ b/mindspore-lite/test/ut/python/test_inference_api.py
@@ -434,12 +434,14 @@ def test_model_predict_inputs_element_type_error():
         outputs = model.predict(["input"])
     assert "inputs element must be Tensor" in str(raise_info.value)
 
+
 def test_model_get_model_info_type_error():
     with pytest.raises(TypeError) as raise_info:
         model = get_model()
         inputs = model.get_model_info()
     assert "key must be str" in str(raise_info.value)
 
+
 def test_model_predict_runtime_error():
     with pytest.raises(RuntimeError) as raise_info:
         model = get_model()
@@ -622,6 +624,7 @@ def test_model_group_add_model_invalid_model_obj_type_error():
         model_group.add_model("model_path")
     assert "models must be list/tuple, but got" in str(raise_info.value)
 
+
 # If declare a model and don't build it may cause core dump.
 def test_update_weights():
     with pytest.raises(RuntimeError) as raise_info:
diff --git a/mindspore-lite/tools/optimizer/fusion/flash_attention_antiquant_fusion.cc b/mindspore-lite/tools/optimizer/fusion/flash_attention_antiquant_fusion.cc
index 27fbb58b516c259d3321ac057c22a9d2c809ab34..8aac6a4b931c2ef18176b1d0c586389be40809a4 100644
--- a/mindspore-lite/tools/optimizer/fusion/flash_attention_antiquant_fusion.cc
+++ b/mindspore-lite/tools/optimizer/fusion/flash_attention_antiquant_fusion.cc
@@ -155,8 +155,9 @@ ParameterPtr FlashAttentionAntiquantFusion::ConcatParameter(const FuncGraphPtr &
     concat_parameter = opt::BuildFloatVecParameterNode(func_graph, concat_data, name);
   }
 
+  MS_CHECK_TRUE_RET(concat_parameter != nullptr, nullptr);
   auto abstract = concat_parameter->abstract();
-  MS_EXCEPTION_IF_NULL(abstract);
+  MS_CHECK_TRUE_RET(abstract != nullptr, nullptr);
   ShapeVector shape_vector = {2};
   shape_vector.insert(shape_vector.end(), ori_shape.begin() + 1, ori_shape.end());
   abstract->set_shape(std::make_shared<abstract::Shape>(shape_vector));