semvec/pypi-packages huggingface dataset:
kunna 0.1.0
A CLI tool to display Q&A content
License: MIT
Requires Python: >=3.6

# Kunna

A simple CLI tool to display Q&A content.

## Installation

```bash
pip install kunna
```

## Usage

Display a specific question:

```bash
kunna show q1
kunna show Q1
kunna show q15
```

## Available Questions

Questions Q1 through Q15 are available.

## License

MIT
ryseble 1.9.0
Python library for RYSE BLE Smart Shade devices (used by Home Assistant integration).
License: MIT
Requires Python: >=3.9


# ryseble

A small Python library to interact with RYSE BLE Smart Shade devices. Designed to be
used by Home Assistant integrations but reusable in other projects.

## Features
- Pair/connect/disconnect with device
- Read/write raw packets
- Helpers to build position/get-position packets

## Installation

```bash
pip install ryseble
```

## Usage (example)

```python
from ryseble.device import RyseBLEDevice
from ryseble.packets import build_position_packet, build_get_position_packet

device = RyseBLEDevice(address="AA:BB:CC:DD:EE:FF", rx_uuid="...", tx_uuid="...")
await device.pair()
await device.write_data(build_position_packet(50))
```
spectroapi 0.1.7
A discord bot IOS status
License: MIT
Requires Python: >=3.8

# spectro-api

> This is spectros api for IOS / mobile status
> You must have status presence online(green)# spectroapi

## Install

```bash
pip install spectro-api
```

```
from discord.ext import commands
from spectro_api import api as pfps

bot = commands.Bot(command_prefix=";")

bot.add_cog(pfps(bot))
bot.run("token")
```

Use:
- ;pfps random
- ;pfps anime
- ;pfps egirl

# Categories
- all
- anime
- egirl

This will will be free (no rate limits)
jet-for-pytorch 0.0.1
Taylor-mode automatic differentiation for PyTorch
License: MIT
Requires Python: >=3.12

# Taylor Mode Autodiff in PyTorch

This library provides a PyTorch implementation of Taylor mode automatic differentiation, a generalization of forward mode to higher-order derivatives.
It is similar to JAX's Taylor mode ([`jax.experimental.jet`](https://docs.jax.dev/en/latest/jax.experimental.jet.html)).

The repository also hosts the Python functionality+experiments and LaTeX source for our NeurIPS 2025 paper ["Collapsing Taylor Mode Automatic Differentiation"](https://openreview.net/forum?id=XgQVL1uP34), which allows to further accelerate Taylor mode for many practical differential operators.

> 🔪 **Warning: expect rough edges!** 🔪
>
> This is a research prototype with various limitations (e.g. operator coverage).
> We highly recommend double-checking your results with PyTorch's autodiff.
> Please help us improve the package by providing feedback, filing issues, and opening pull requests.


## Getting Started

### Installation

```bash
pip install jet-for-pytorch
```

### Examples

See the [documentation](https://torch-jet.readthedocs.io/en/latest/generated/gallery/).

## Citing

If you find the `jet` package useful for your research, consider citing

```bibtex

@inproceedings{dangel2025collapsing,
  title =        {Collapsing Taylor Mode Automatic Differentiation},
  author =       {Felix Dangel and Tim Siebert and Marius Zeinhofer and Andrea
                  Walther},
  year =         2025,
  booktitle =    {Advances in Neural Information Processing Systems (NeurIPS)},
}

```