# FGSM attack code
def fgsm_attack(image, epsilon, data_grad):
    # Collect the element-wise sign of the data gradient
    sign_data_grad = data_grad.sign()
    # Create the perturbed image by adjusting each pixel of the input image
    perturbed_image = image + epsilon*sign_data_grad
    # Adding clipping to maintain [0,1] range
    perturbed_image = [torch.clamp](https://docs.pytorch.org/docs/stable/generated/torch.clamp.html#torch.clamp "torch.clamp")(perturbed_image, 0, 1)
    # Return the perturbed image
    return perturbed_image

# restores the tensors to their original scale
def denorm(batch, mean=[0.1307], std=[0.3081]):
    """
    Convert a batch of tensors to their original scale.

    Args:
        batch (torch.Tensor): Batch of normalized tensors.
        mean (torch.Tensor or list): Mean used for normalization.
        std (torch.Tensor or list): Standard deviation used for normalization.

    Returns:
        torch.Tensor: batch of tensors without normalization applied to them.
    """
    if isinstance(mean, list):
        mean = [torch.tensor](https://docs.pytorch.org/docs/stable/generated/torch.tensor.html#torch.tensor "torch.tensor")(mean).to(device)
    if isinstance(std, list):
        std = [torch.tensor](https://docs.pytorch.org/docs/stable/generated/torch.tensor.html#torch.tensor "torch.tensor")(std).to(device)

    return batch * std.view(1, -1, 1, 1) + mean.view(1, -1, 1, 1)