def __init__(self, manager, task_id, sub_ids=None, dry_run=False, resubmit=False):
		self.__manager = manager
		self.__task	= self.__manager.load_task(task_id)
		self.__sub_ids = sub_ids
		self.__dry_run = dry_run
		self.__resubmit = resubmit

		self.__logger = logging.getLogger('JSUB')
		if self.__sub_ids==None:
			self.__sub_ids=range(len(self.__task.data['jobvar']))

		self.__initialize_manager()
def nonlinear_1d(module, grad_input, grad_output):
    delta_out = module.y[: int(module.y.shape[0] / 2)] - module.y[int(module.y.shape[0] / 2):]

    delta_in = module.x[: int(module.x.shape[0] / 2)] - module.x[int(module.x.shape[0] / 2):]
    dup0 = [2] + [1 for i in delta_in.shape[1:]]
    # handles numerical instabilities where delta_in is very small by
    # just taking the gradient in those cases
    grads = [None for _ in grad_input]
    grads[0] = torch.where(torch.abs(delta_in.repeat(dup0)) < 1e-6, grad_input[0],
                           grad_output[0] * (delta_out / delta_in).repeat(dup0))
    return tuple(grads)