Learning can be a difficult and time-consuming process, especially when new models are released in programming languages and frameworks that are unfamiliar to you. It can be frustrating when a new model is not available in your current tool set, forcing you to either wait or learn a new one. To address this issue, a group of engineers has created Ivy, a solution that unifies all the unique tools available in different frameworks of the Python language. With Ivy, developers can focus more on high-level experimentation without having to worry about the low-level details.

Ivy had two main parts: the Backend Handler and the Backend Functional APIs. The Backend Handler made sure everything was working smoothly behind the scenes, while the Backend Functional APIs let you use specific tools for specific jobs. Ivy also had two types of tools you could use: the Ivy functional API and the Ivy stateful API. The Ivy functional API let you use any tool you wanted, no matter where it came from. The Ivy stateful API helped you create new tools that worked well with other tools you might use.

Ivy allows using different tools from different frameworks without any problems. We can test Ivy by creating a machine learning program that used PyTorch as the backend, but Ivy could easily be changed to work with other frameworks like TensorFlow or JAX. We can also use Ivy to create a model with trainable modules and layers, which they could use alone or as part of any other framework code.