How to use the lpips.base_model.BaseModel.initialize function in lpips

def initialize(self, model='net-lin', net='squeeze', colorspace='Lab', use_gpu=True, printNet=False):
        '''
        INPUTS
            model - ['net-lin'] for linearly calibrated network
                    ['net'] for off-the-shelf network
                    ['L2'] for L2 distance in Lab colorspace
                    ['SSIM'] for ssim in RGB colorspace
            net - ['squeeze','alex','vgg']
            colorspace - ['Lab','RGB'] colorspace to use for L2 and SSIM
            use_gpu - bool - whether or not to use a GPU
            printNet - bool - whether or not to print network architecture out
        '''
        BaseModel.initialize(self, use_gpu=use_gpu)
        self.model = model
        self.net = net
        self.use_gpu = use_gpu
        if self.use_gpu:
            self.map_location = None
        else:
           self.map_location = lambda storage, loc: storage
        self.model_name = '%s [%s]'%(model,net)
        if(self.model == 'net-lin'): # pretrained net + linear layer
            self.net = networks.PNetLin(use_gpu=use_gpu,pnet_type=net,use_dropout=True)

            weight_path =  os.path.join(os.path.dirname(__file__), 'weights', '%s.pth' % net)

            self.net.load_state_dict(torch.load(weight_path, 
                                     map_location=self.map_location))
        elif(self.model=='net'): # pretrained network