TEST_EXECUTOR

TEST_EXECUTOR是一个用于执行测试的工具。它可以帮助开发人员在软件开发过程中进行单元测试、集成测试和功能测试等各种类型的测试。通过使用TEST_EXECUTOR，开发人员可以编写测试用例并运行这些测试用例，以验证代码的正确性和功能是否符合预期。

TEST_EXECUTOR的主要功能包括：
1. 测试用例管理：开发人员可以使用TEST_EXECUTOR来创建、编辑和管理测试用例。测试用例可以包含输入数据、预期输出和断言条件等信息。
2. 测试执行：TEST_EXECUTOR提供了一个执行环境，可以运行测试用例并记录执行结果。开发人员可以查看每个测试用例的执行状态和结果。
3. 测试报告生成：TEST_EXECUTOR可以生成详细的测试报告，包括测试用例的执行情况、通过率、失败原因等信息。这些报告可以帮助开发人员快速定位和修复问题。
4. 自动化测试支持：TEST_EXECUTOR支持自动化测试，可以通过脚本或命令行接口来批量执行测试用例，并生成相应的测试报告。

相关问题

2023-06-17 23:47:22.786162: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found 2023-06-17 23:47:22.786281: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. 2023-06-17 23:47:24.419330: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found 2023-06-17 23:47:24.419809: W tensorflow/stream_executor/cuda/cuda_driver.cc:263] failed call to cuInit: UNKNOWN ERROR (303) 2023-06-17 23:47:24.426229: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: ����� 2023-06-17 23:47:24.426345: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: ����� 2023-06-17 23:47:24.430552: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. Traceback (most recent call last): File "C:\Users\10290\Desktop\test\writer.py", line 20, in <module> write_reward_tb(summary_writer, rewards[i], i) File "C:\Users\10290\Desktop\test\writer.py", line 9, in write_reward_tb summary = tf.Summary(value=[tf.Summary.Value(tag='reward', simple_value=reward)]) AttributeError: module 'tensorflow' has no attribute 'Summary'分析错误原因

这个错误是由于TensorFlow 2.x版本中`tf.Summary`已被弃用所致。相反，你应该使用`tf.summary.Summary`。你需要将代码更新为：

summary = tf.summary.Summary(value=[tf.summary.Summary.Value(tag='reward', simple_value=reward)])

这样就可以避免这个错误了。另外，还需要注意，TensorFlow 2.x版本中`tf.summary.FileWriter`已经被弃用，应该使用`tf.summary.create_file_writer`来创建一个`tf.summary.FileWriter`的实例。

D:\python\python.exe D:\tokamaka\实验集\Python\SVM低数据兼测试版本\DisruptionPredictor\Test3.py Traceback (most recent call last): File "D:\tokamaka\实验集\Python\SVM低数据兼测试版本\DisruptionPredictor\Test3.py", line 50, in <module> train_data, train_labels = Parallel(n_jobs=-1)(delayed(load_data_parallel)('train', list(eval(shot)), directory) for shot in shots['train']) File "D:\python\lib\site-packages\joblib\parallel.py", line 1008, in __call__ n_jobs = self._initialize_backend() File "D:\python\lib\site-packages\joblib\parallel.py", line 775, in _initialize_backend n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self, File "D:\python\lib\site-packages\joblib\_parallel_backends.py", line 506, in configure self._workers = get_memmapping_executor( File "D:\python\lib\site-packages\joblib\executor.py", line 20, in get_memmapping_executor return MemmappingExecutor.get_memmapping_executor(n_jobs, **kwargs) File "D:\python\lib\site-packages\joblib\executor.py", line 42, in get_memmapping_executor manager = TemporaryResourcesManager(temp_folder) File "D:\python\lib\site-packages\joblib\_memmapping_reducer.py", line 535, in __init__ self.set_current_context(context_id) File "D:\python\lib\site-packages\joblib\_memmapping_reducer.py", line 539, in set_current_context self.register_new_context(context_id) File "D:\python\lib\site-packages\joblib\_memmapping_reducer.py", line 564, in register_new_context self.register_folder_finalizer(new_folder_path, context_id) File "D:\python\lib\site-packages\joblib\_memmapping_reducer.py", line 594, in register_folder_finalizer resource_tracker.register(pool_subfolder, "folder") File "D:\python\lib\site-packages\joblib\externals\loky\backend\resource_tracker.py", line 179, in register self._send('REGISTER', name, rtype) File "D:\python\lib\site-packages\joblib\externals\loky\backend\resource_tracker.py", line 196, in _send msg = f'{cmd}:{name}:{rtype}\n'.encode('ascii') UnicodeEncodeError: 'ascii' codec can't encode characters in position 18-20: ordinal not in range(128) 进程已结束,退出代码1

这是一个Python程序运行时发生的错误，具体错误信息为UnicodeEncodeError，错误原因是程序试图将一个非ASCII字符编码为ASCII字符，但是该字符不在ASCII编码范围内，导致编码错误。解决这个问题的方法是将程序中涉及到的非ASCII字符转为Unicode编码或者使用适当的编码方式，例如utf-8编码。可以通过在程序开头添加以下代码来指定编码方式：

# -*- coding: utf-8 -*-

另外，可能还需要检查一下程序中的文件路径是否正确，以及相关的Python包是否已经正确安装。