how to do the supervised learning in pytorch

Supervised learning in PyTorch involves training a model on a labeled dataset. The process involves defining a model architecture, selecting a loss function and optimizer, splitting the dataset into training and validation sets, and finally training the model using the training data. During training, the model uses backpropagation to update its parameters and minimize the loss function. The validation set is used to evaluate the model's performance and prevent overfitting.