How to use the redisai.DType.float function in redisai

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)

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)

import redisai as rai
from ml2rt import load_model

model = load_model("../models/ONNX/boston.onnx")
con = rai.Client()
con.modelset("onnx_model", rai.Backend.onnx, rai.Device.cpu, model)

# dummydata taken from sklearn.datasets.load_boston().data[0]
dummydata = [
    0.00632, 18.0, 2.31, 0.0, 0.538, 6.575, 65.2, 4.09, 1.0, 296.0, 15.3, 396.9, 4.98]
tensor = rai.Tensor.scalar(rai.DType.float, *dummydata)
con.tensorset("input", tensor)

con.modelrun("onnx_model", ["input"], ["output"])
outtensor = con.tensorget("output", as_type=rai.BlobTensor)
print(f"House cost predicted by model is ${outtensor.to_numpy().item() * 1000}")

import numpy as np
from redisai import Client, DType, Device, Backend
import ml2rt

client = Client()
client.tensorset('x', [2, 3], dtype=DType.float)
t = client.tensorget('x')
print(t.value)

model = ml2rt.load_model('test/testdata/graph.pb')
tensor1 = np.array([2, 3], dtype=np.float)
client.tensorset('a', tensor1)
client.tensorset('b', (12, 10), dtype=np.float)
client.modelset('m', Backend.tf,
                Device.cpu,
                inputs=['a', 'b'],
                outputs='mul',
                data=model)
client.modelrun('m', ['a', 'b'], ['mul'])
print(client.tensorget('mul'))

# Try with a script