def adversarial(x, model, loss_func, c=1e-4, kappa=0, num_iter=100, lr=0.01): """ Create adversarial examples using CW algorithm Args: - x: input image - model: the neural network model - loss_func: the loss function to use - c: the weight for the L2 regularization term (default=1e-4) - kappa: the confidence parameter (default=0) - num_iter: number of iterations for the algorithm (default=100) - lr: learning rate for the optimization (default=0.01) Returns: - x_adv: adversarial example """ x_adv = x.clone().detach().requires_grad_(True) for i in range(num_iter): output = model(x_adv) loss = loss_func(output, torch.tensor([kappa]), x, x_adv, c) model.zero_grad() loss.backward() with torch.no_grad(): x_adv += lr * x_adv.grad x_adv = torch.max(torch.min(x_adv, x + 0.35), x - 0.35) x_adv = torch.clamp(x_adv, 0, 1) x_adv.requires_grad_() return x_adv.detach()上述代码出现TypeError: ce_loss() takes 2 positional arguments but 5 were given错误，请改正

根据代码，`loss_func`应该是一个函数，且它的输入参数应该是模型的输出 `output`和目标标签 `target`。因此，需要修改 `loss_func` 函数的输入参数，将 `x`、`x_adv` 和 `c` 作为函数的参数，而不是在函数调用时传入。修改后的代码如下：

def adversarial(x, model, loss_func, c=1e-4, kappa=0, num_iter=100, lr=0.01):
    """Create adversarial examples using CW algorithm
    Args:
    - x: input image
    - model: the neural network model
    - loss_func: the loss function to use
    - c: the weight for the L2 regularization term (default=1e-4)
    - kappa: the confidence parameter (default=0)
    - num_iter: number of iterations for the algorithm (default=100)
    - lr: learning rate for the optimization (default=0.01)
    Returns:
    - x_adv: adversarial example
    """
    x_adv = x.clone().detach().requires_grad_(True)
    for i in range(num_iter):
        output = model(x_adv)
        loss = loss_func(output, kappa, target=x, adversary=x_adv, c=c)
        model.zero_grad()
        loss.backward()
        with torch.no_grad():
            x_adv += lr * x_adv.grad
            x_adv = torch.max(torch.min(x_adv, x + 0.35), x - 0.35)
            x_adv = torch.clamp(x_adv, 0, 1)
            x_adv.requires_grad_()
    return x_adv.detach()