How to use the saliency.smoothgrad.SmoothGrad function in saliency

# Use pretrained ResNet-18 provided by PyTorch
model = models.resnet18(pretrained=True)
model = model.to(device)

# Initialize saliency methods
saliency_methods = {
# FullGrad-based methods
'fullgrad': FullGrad(model),
'simple_fullgrad': SimpleFullGrad(model),
'smooth_fullgrad': SmoothFullGrad(model),

# Other saliency methods from literature
'gradcam': GradCAM(model),
'inputgrad': InputGradient(model),
'smoothgrad': SmoothGrad(model)
}

def compute_saliency_and_save():
    for batch_idx, (data, _) in enumerate(sample_loader):
        data = data.to(device).requires_grad_()

        # Compute saliency maps for the input data
        for s in saliency_methods:
            saliency_map = saliency_methods[s].saliency(data)

            # Save saliency maps
            for i in range(data.size(0)):
                filename = save_path + str( (batch_idx+1) * (i+1))
                image = unnormalize(data[i].cpu())
                save_saliency_map(image, saliency_map[i], filename + '_' + s + '.jpg')