If you visited the link I provided you could see the definition of optim.LBFGS which is:
torch.optim.LBFGS(params, lr=1, max_iter=20, max_eval=None, tolerance_grad=1e-07, tolerance_change=1e-09, history_size=100, line_search_fn=None)

Where max_iter is same as sklearn and max_fun is max_eval.
lr (float) – learning rate (default: 1)
max_iter (int) – maximal number of iterations per optimization step (default: 20)
max_eval (int) – maximal number of function evaluations per optimization step (default: max_iter * 1.25).
tolerance_grad (float) – termination tolerance on first order optimality (default: 1e-5).
tolerance_change (float) – termination tolerance on function value/parameter changes (default: 1e-9).
history_size (int) – update history size (default: 100).
line_search_fn (str) – either ‘strong_wolfe’ or None (default: None).