loss.backward(retain_graph=False)

This function call computes the gradients of the loss with respect to all the trainable parameters in the model, and propagates them backwards through the computation graph, accumulating the gradients in the leaves of the graph.

The `retain_graph` argument determines whether or not to keep the computation graph after the backward pass has completed. If `retain_graph=True`, the graph is retained and can be used for multiple backward passes. If `retain_graph=False`, the graph is released after the backward pass, and cannot be used for further computations.

In general, `retain_graph=True` is only necessary if you need to perform multiple backward passes through the same graph (e.g. for computing second-order gradients). Otherwise, it is usually more efficient to set `retain_graph=False` to save memory.