include_directories( endpoint mainControl message message/sdMessage resourcesMgr serviceInstance serviceInstance/serviceDiscovery serviceInstance/someip utils utils/config utils/e2e utils/log utils/workerPool ../utils ../utils/APF ../utils/asio ../utils/kt_someip ../utils/kt_someip/log ../utils/kt_someip/log/include/ara/log ../utils/kt_someip/someipMessage )

这是一个 CMake 的代码片段，用于向编译器添加多个头文件所在的目录，以便在编译时可以找到这些头文件。include_directories() 命令可以用来指定头文件目录，括号内列出的是多个目录名，每个目录名之间用空格分隔。

在这个代码片段中，它会向编译器添加以下目录的头文件：

- endpoint
- mainControl
- message
- message/sdMessage
- resourcesMgr
- serviceInstance
- serviceInstance/serviceDiscovery
- serviceInstance/someip
- utils
- utils/config
- utils/e2e
- utils/log
- utils/workerPool
- ../utils
- ../utils/APF
- ../utils/asio
- ../utils/kt_someip
- ../utils/kt_someip/log
- ../utils/kt_someip/log/include/ara/log
- ../utils/kt_someip/someipMessage

这些目录包含了该项目所需的头文件，当编译器需要用到这些头文件时，就会在这些目录中查找。

相关问题

module 'tensorflow.keras.utils' has no attribute 'image_dataset_from_directory'

This error message suggests that the attribute 'image_dataset_from_directory' is not available in the 'utils' module of the TensorFlow Keras library.

This could be due to a few different reasons, such as using an outdated version of TensorFlow Keras or not importing the appropriate modules.

One possible solution is to update TensorFlow Keras to the latest version using the following command:

!pip install --upgrade tensorflow

If the issue persists, try importing the 'ImageDataGenerator' module instead and using it to generate image datasets from directories:

from tensorflow.keras.preprocessing.image import ImageDataGenerator

datagen = ImageDataGenerator(
    # Specify image augmentation parameters
)

train_dataset = datagen.flow_from_directory(
    # Specify training directory
)

test_dataset = datagen.flow_from_directory(
    # Specify testing directory
)