semvec/pypi-packages huggingface dataset:
ImgToolsGG 0.3.45
test how to make a package
License: MIT

# 个人图片工具

# 项目地址:  https://github.com/buxuele/ImgToolsGG



### Installation

> pip install ImgToolsGG

### Get started
How to use this package:

```
from src import convert_image_to_jpg, print_exif_data


# test 1
p = r"C:\Users\Administrator\Desktop\imgs\meme"
convert_image_to_jpg(p)


# test 2
print_exif_data("./imgs/3.jpg")

```


# 添加一些图片


![图片描述](./imgs/2.jpg)
![图片描述](./imgs/3.jpg)
![图片描述](./imgs/4.jpg)
![图片描述](./imgs/5.jpg)
![图片描述](./imgs/6.jpg)
ncsu_courses 0.3.1
A Python library for retrieving data about NCSU's course offerings and sections
License: MIT
Requires Python: <4.0,>=3.10

# NCSU Courses

A Python library for getting data about NCSU's course offerings. Contains functionality for retrieving historical and current courses and their sections, including instructor, meeting information, seats, and more.

This library uses the same API that [this](https://webappprd.acs.ncsu.edu/php/coursecat/) website does.

## Courses

Use `ncsu_courses.courses.get_courses(subject, term)` to get all courses for a given subject during the given term. A course has data such as a curriculum and code, a description, title, etc.

## Sections

A section of a course is a specific instance of the course being offered during a certain term. A section contains information such as instructor, meeting days and times, meeting location, seats, etc. Use `ncsu_courses.courses.get_sections(subject, term)` to get all sections for all classes of the given subject during the given term.

## Subjects

Subjects represent broad areas of study. Each course's name begins with the letters of the subject that it belongs to (ex. CSC 111 is a Computer Science course because CSC represents the Computer Science subject). The `ncsu_courses.subjects.get_all_subjects(term)` function returns a list of all subjects that exist during the current term.

## Terms

A term is identified by a year and a session (Fall, Spring, Summer 1, or Summer 2). Use `ncsu_courses.term.Term` to represent a specific term. Terms are passed to the API as integers representing a combination of year and session, and the `Term` object handles this internally.
audio-xlstm 0.0.1
Paper - Pytorch
License: MIT
Requires Python: <4.0,>=3.10
Keywords: artificial intelligence, deep learning, optimizers, Prompt Engineering

[![Multi-Modality](agorabanner.png)](https://discord.com/servers/agora-999382051935506503)

# Audio xLSTMs: Learning Self-supervised audio representations with xLSTMs

[![Join our Discord](https://img.shields.io/badge/Discord-Join%20our%20server-5865F2?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/agora-999382051935506503) [![Subscribe on YouTube](https://img.shields.io/badge/YouTube-Subscribe-red?style=for-the-badge&logo=youtube&logoColor=white)](https://www.youtube.com/@kyegomez3242) [![Connect on LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/kye-g-38759a207/) [![Follow on X.com](https://img.shields.io/badge/X.com-Follow-1DA1F2?style=for-the-badge&logo=x&logoColor=white)](https://x.com/kyegomezb)

This is a community based approach to an implementation mostly for practice. I will implement the model architecture as defined in the paper but will leave someone else to implement the training script! So please create a training script if you have the time and energy




# License
MIT


# Citation
```bibtex
@article{xlstm,
  title={xLSTM: Extended Long Short-Term Memory},
  author={Beck, Maximilian and P{\"o}ppel, Korbinian and Spanring, Markus and Auer, Andreas and Prudnikova, Oleksandra and Kopp, Michael and Klambauer, G{\"u}nter and Brandstetter, Johannes and Hochreiter, Sepp},
  journal={arXiv preprint arXiv:2405.04517},
  year={2024}
}

```