The memory is from the output out1 and intermediate activations needed to compute the gradient. The first increase is from computing out1. The second increase is from computing net(data1) while out1 is still alive. The reason is that in:
out1 = net(data1)

The right-hand side net(data1) is evaluated before the assignment. Memory usage, as reported by the system, doesn’t generally decrease. If it had, then it would decrease back to 2872Mi after the assignment operation.
You can rewrite your program to avoid keeping two versions of out1 alive at once:
def eval(network, input):
  out1 = network(input)
  # maybe use out1 here

for i in range(10):
  eval(net, data1)

As long as you don’t return out1 from eval, out1 will be freed before the next call, so you’ll only use 2872Mi.