with torch.no_grad(): params -= learning_rate * params.grad

This code snippet updates the parameters (weights and biases) of a neural network using gradient descent with a learning rate. The `torch.no_grad()` context manager is used to disable gradient calculations, as we only want to update the parameters based on the gradients calculated during the training process, not update the gradients themselves.

`params` refers to the tensor containing the current parameters of the neural network, and `params.grad` refers to the tensor containing the gradients of the parameters with respect to the loss function. The learning rate is a hyperparameter that determines the step size of the parameter update.

The code subtracts the product of the learning rate and the gradients from the parameters, effectively moving the parameters in the direction that decreases the loss function. This process is repeated for each batch of data during training, until the loss function converges to a minimum.