Torch is a popular open-source machine learning library for Python developed by Facebook’s AI Research lab (FAIR). It is widely used for various tasks such as deep learning, natural language processing, computer vision, and more. Torch provides powerful tools to work with tensors, which are multi-dimensional arrays, and offers efficient implementations of many machine learning algorithms PyTorch introduced PyTorch Lightning, a lightweight PyTorch wrapper that simplifies the process of organizing and structuring PyTorch code, making it easier to write, scale, and maintain. PyTorch Lightning provides a high-level interface for PyTorch and automates many boilerplate tasks, making the code cleaner and more organized.

Here is a simple example of how to use PyTorch for creating a basic neural network:

This is a basic example to illustrate the main components of working with PyTorch. In real-world scenarios, you would typically work with larger datasets, more complex models, and utilize various techniques to improve training and performance, such as data augmentation, learning rate scheduling, and model checkpoints.

Make sure you have PyTorch and other required libraries installed before running the code. You can install PyTorch using pip:

Keep in mind that my knowledge is up until September 2021, and there might have been updates or changes to PyTorch since then. Always refer to the official PyTorch documentation and release notes for the most up-to-date information.