376058931d26b5351e9aed2dcf9dd2932b0cadab,foolbox/ext/native/attacks/basic_iterative_method.py,LinfinityBasicIterativeAttack,__call__,#LinfinityBasicIterativeAttack#Any#Any#Any#,72

Before Change

        if self.random_start:
            x = x + ep.uniform(x, x.shape, -self.epsilon, self.epsilon)
            x = ep.clip(x, *model.bounds())

        for _ in range(self.steps):
            _, gradients = ep.value_and_grad(loss_fn, x)

After Change

        self, model: Model, inputs: T, criterion: Union[Misclassification, T]
    ) -> T:
        x0, restore_type = ep.astensor_(inputs)
        criterion_ = get_criterion(criterion)
        del inputs, criterion

        if not isinstance(criterion_, Misclassification):
            raise ValueError("unsupported criterion")

        labels = criterion_.labels

        def loss_fn(inputs: ep.Tensor) -> ep.Tensor:
            logits = model(inputs)

Instances