Uncaught (in promise) Error: Tensorflow Op is not supported: _FusedConv2D

<!-- Load TensorFlow.js. This is required to use MobileNet. -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@4.17.0"> </script>
<!-- Load the MobileNet model. -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/mobilenet@2.1.1"> </script>

<!-- Replace this with your image. Make sure CORS settings allow reading the image! -->
<img id="img" src="cat.jpg"></img>

<!-- Place your code in the script tag below. You can also use an external .js file -->
<script>
  // Notice there is no 'import' statement. 'mobilenet' and 'tf' is
  // available on the index-page because of the script tag above.

  const img = document.getElementById('img');

  // Load the model.
  mobilenet.load().then(model => {
    // Classify the image.
    model.classify(img).then(predictions => {
      console.log('Predictions: ');
      console.log(predictions);
    });
  });
</script>

import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import PIL.Image
tf.get_logger().setLevel("ERROR")
def preprocess_image(image_path):
    image = PIL.Image.open(image_path).convert("RGB").resize((150,150))
    image = np.array(image) / 255
    image = np.expand_dims(image, 0)

    return image

def test_model(imgurl):
    image_path = imgurl
    model_file_name='human_or.h5'
    labels = ["human","dogs"]
    model = tf.keras.models.load_model(model_file_name, custom_objects={"KerasLayer": hub.KerasLayer})
    predictions = model.predict(preprocess_image(image_path))
    print("検証 %s 人の顔である確率 %3d%%" %(image_path,int(predictions[0][0]*100)) )

test_model("ai_image_test\\test1.jpg")
test_model("ai_image_test\\test2.jpg")
test_model("ai_image_test\\test3.jpg")
test_model("ai_image_test\\test4.jpg")
test_model("ai_image_test\\test5.jpg")
test_model("ai_image_test\\test6.jpg")