net, loss = lambda X:d2l.linreg(X, w, b), d2l.squared_loss

The code snippet above defines a loss function for linear regression. The loss function is a combination of two functions: a linear regression function and a squared loss function.

The linear regression function is defined by the lambda expression "lambda X:d2l.linreg(X, w, b)", which takes in the input data X, and parameters w and b, and returns the predicted output of the linear regression model.

The squared loss function is defined by the function "d2l.squared_loss", which computes the difference between the predicted output of the linear regression model and the true output, and squares the difference. This function is commonly used as a loss function in linear regression models.

The overall loss function is then computed as the composition of these two functions using the "lambda" expression and is used to optimize the parameters w and b during the training process.