How to use the redisai.load_model function in redisai

def testTFGraph(self):
        _ = get_tf_graph()
        init = tf.global_variables_initializer()
        sess = tf.Session()
        sess.run(init)
        path = f'{time.time()}.pb'
        save_model(sess, path, output=['output'])
        model = load_model(path)
        os.remove(path)
        con = self.get_client()
        con.modelset(
            'tfmodel', Backend.tf, Device.cpu, model,
            input=['input'], output=['output'])
        con.tensorset('a', Tensor.scalar(DType.float, 2))
        con.modelrun('tfmodel', ['a'], 'c')
        tensor = con.tensorget('c')
        self.assertEqual([13], tensor.value)

def testSKLearnGraph(self):
        sklearn_model, prototype = get_sklearn_model_and_prototype()
        path = f'{time.time()}.onnx'
        self.assertRaises(TypeError, save_model, sklearn_model, path)
        save_model(sklearn_model, path, prototype=prototype)
        model = load_model(path)
        os.remove(path)
        con = self.get_client()
        con.modelset('onnx_skl_model', Backend.onnx, Device.cpu, model)
        con.tensorset('a', Tensor.scalar(DType.float, *([1] * 13)))
        con.modelrun('onnx_skl_model', ['a'], ['outfromonnxskl'])
        tensor = con.tensorget('outfromonnxskl')
        self.assertEqual(len(tensor.value), 1)

def testONNXGraph(self):
        onnx_model = get_onnx_model()
        path = f'{time.time()}.onnx'
        save_model(onnx_model, path)
        model = load_model(path)
        os.remove(path)
        con = self.get_client()
        con.modelset('onnxmodel', Backend.onnx, Device.cpu, model)
        con.tensorset('a', Tensor.scalar(DType.float, 2, -1))
        con.modelrun('onnxmodel', ['a'], ['c'])
        tensor = con.tensorget('c')
        self.assertEqual([2.0, 0.0], tensor.value)

def testPyTorchGraph(self):
        torch_graph = MyModule()
        path = f'{time.time()}.pt'
        save_model(torch_graph, path)
        model = load_model(path)
        os.remove(path)
        con = self.get_client()
        con.modelset('ptmodel', Backend.torch, Device.cpu, model)
        con.tensorset('a', Tensor.scalar(DType.float, 2, 5))
        con.tensorset('b', Tensor.scalar(DType.float, 3, 7))
        con.modelrun('ptmodel', ['a', 'b'], 'c')
        tensor = con.tensorget('c')
        self.assertEqual([5, 12], tensor.value)

def testScriptLoad(self):
        con = self.get_client()
        dirname = os.path.dirname(__file__)
        path = f'{dirname}/testdata/script.txt'
        script = load_model(path)
        con.scriptset('script', Device.cpu, script)
        con.tensorset('a', Tensor.scalar(DType.float, 2, 5))
        con.tensorset('b', Tensor.scalar(DType.float, 3, 7))
        con.scriptrun('script', 'bar', ['a', 'b'], 'c')
        tensor = con.tensorget('c')
        self.assertEqual([5, 12], tensor.value)

import redisai as rai

con = rai.Client(host='159.65.150.75', port=6379, db=0)

pt_model_path = '../models/imagenet/pytorch/resnet50.pt'
script_path = '../models/imagenet/pytorch/data_processing_script.txt'

pt_model = rai.load_model(pt_model_path)
script = rai.load_script(script_path)

out1 = con.modelset('imagenet_model', rai.Backend.torch, rai.Device.cpu, pt_model)
out2 = con.scriptset('imagenet_script', rai.Device.cpu, script)