How to use the lime.lime_image.LimeImageExplainer function in lime

pytorch_model = load_orig_imagenet_model(arch_name='resnet50')

        # load the class label
        label_map = load_imagenet_label_map()

    elif args.dataset == 'places365':
        pytorch_model = load_orig_places365_model(arch_name='resnet50')

        # load the class label
        label_map = load_class_label()

    else:
        print('Invalid datasest!!')
        exit(0)

    pytorch_explainer = lime_image.LimeImageExplainer(random_state=args.lime_explainer_seed)
    slic_parameters = {'n_segments': args.lime_superpixel_num, 'compactness': 30, 'sigma': 3}
    segmenter = SegmentationAlgorithm('slic', **slic_parameters)
    pill_transf = get_pil_transform()

    #########################################################
    # Function to compute probabilities
    # Pytorch
    pytorch_preprocess_transform = get_pytorch_preprocess_transform()

    def pytorch_batch_predict(images):
        batch = torch.stack(tuple(pytorch_preprocess_transform(i) for i in images), dim=0)
        batch = batch.to('cuda')

        if args.if_pre == 1:
            logits = pytorch_model(batch)
            probs = F.softmax(logits, dim=1)

def explain_raw(model, img, topLabels, numSamples, numFeatures, hideRest, hideColor, positiveOnly):
    img, oldImg = transform_img_fn(img)
    img = img*(1./255)
    prediction = model.predict(img)
    explainer = lime_image.LimeImageExplainer()
    img = np.squeeze(img)
    explanation = explainer.explain_instance(img, model.predict, top_labels=topLabels, hide_color=hideColor, num_samples=numSamples)
    temp, mask = explanation.get_image_and_mask(getTopPrediction(prediction[0]), positive_only=positiveOnly, num_features=numFeatures, hide_rest=hideRest)
    imgExplained = mark_boundaries(temp, mask)
    img = Image.fromarray(np.uint8(imgExplained*255))
    imgByteArr = io.BytesIO()
    img.save(imgByteArr, format='JPEG')
    imgByteArr = imgByteArr.getvalue()

    return imgByteArr