def handle(self):
		run_root = self.__backend_mgr.get_run_root(self.__task.data['backend'], self.__task.data['id'])

		main_root = os.path.join(run_root, 'main')

		safe_rmdir(main_root)
		safe_mkdir(main_root)

		self.__create_input(main_root)
		self.__create_context(main_root)
		self.__create_action(main_root)
		self.__create_navigator(main_root)
		self.__create_bootstrap(main_root)

		launcher_param = self.__create_launcher(run_root)

		self.__submit(launcher_param)
def deeplift_grad(module, grad_input, grad_output):
    """The backward hook which computes the deeplift
    gradient for an nn.Module
    """
    # first, get the module type
    module_type = module.__class__.__name__
    # first, check the module is supported
    if module_type in op_handler:
        if op_handler[module_type].__name__ not in ['passthrough', 'linear_1d']:
            return op_handler[module_type](module, grad_input, grad_output)
    else:
        print('Warning: unrecognized nn.Module: {}'.format(module_type))
        return grad_input