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 gradient(self, idx, inputs):
        self.model.zero_grad()
        X = [x.requires_grad_() for x in inputs]
        outputs = self.model(*X)
        selected = [val for val in outputs[:, idx]]
        grads = []
        if self.interim:
            interim_inputs = self.layer.target_input
            for idx, input in enumerate(interim_inputs):
                grad = torch.autograd.grad(selected, input,
                                           retain_graph=True if idx + 1 < len(interim_inputs) else None,
                                           allow_unused=True)[0]
                if grad is not None:
                    grad = grad.cpu().numpy()
                else:
                    grad = torch.zeros_like(X[idx]).cpu().numpy()
                grads.append(grad)
            del self.layer.target_input
            return grads, [i.detach().cpu().numpy() for i in interim_inputs]
        else:
            for idx, x in enumerate(X):
                grad = torch.autograd.grad(selected, x,
                                           retain_graph=True if idx + 1 < len(X) else None,
                                           allow_unused=True)[0]
                if grad is not None:
                    grad = grad.cpu().numpy()
                else:
                    grad = torch.zeros_like(X[idx]).cpu().numpy()
                grads.append(grad)
            return grads