gccgo-14-multilib-x86-64-linux-gnu

GNU Go compiler (multilib support) (cross compiler for amd64 architecture)

This is the GNU Go compiler, which compiles Go on platforms supported by the gcc compiler.

python3-djangorestframework-flex-fields

Dynamic fields and nested resources for DRF (Python3 version)

FlexFields (DRF-FF) for Django REST Framework is a package designed to provide a common baseline of functionality for dynamically setting fields and nested models within DRF serializers. To remove unneeded fields, you can dynamically set fields, including nested fields, via URL parameters (?fields=name,address.zip) or when configuring serializers. Additionally, you can dynamically expand fields from simple values to complex nested models, or treat fields as "deferred", and expand them on an as-needed basis.

libgl4es0

GL4ES - OpenGL for GLES Hardware -- runtime library

This is a library provide OpenGL 2.x functionality for GLES2.0 accelerated Hardware (and of course also support OpenGL 1.5 function, so metimes better than when using GLES 1.1 backend) There is also support for GLES 1.1 Hardware, emulating OpenGL 1.5, and some OpenGL 2.x + extensions.

gir1.2-nma4-1.0

GObject introspection data for libnma-gtk4

The libnma-gtk4 library provides GTK 4 dialogs for NetworkManager.

libmlt++7

MLT multimedia framework C++ wrapper (runtime)

MLT is an open source multimedia framework, designed and developed for television broadcasting. It provides a toolkit for broadcasters, video editors, media players, transcoders, web streamers and many more types of applications. The functionality of the system is provided via an assortment of ready to use tools, xml authoring components, and an extendible plug-in based API.

pyomop

OHSDI OMOP Common Data Model tools

The OHSDI OMOP Common Data Model allows for the systematic analysis of healthcare observational databases. This is a Python library to use the CDM v6 compliant databases using SQLAlchemy as the ORM. pyomop also supports converting query results to a pandas dataframe (see below) for use in machine learning pipelines.