I am going round in circles with this error. Could anyone offer a pointer? I'm new to python and pytorch.

import os
import cv2
import numpy as np
import skops.io as sio  # to load the saved model
from PIL import Image
from skimage.filters import laplace, sobel, roberts
from torchvision import transforms

# Load the saved SVM model
svm_model = sio.load("svm_model_trained.skops", trusted=True)

# Define the path to the directory containing the test images
test_images_dir = '/Users/test/project_dataset/archive'

# Define the label names for the predicted classes
label_names = ['Sharp', 'Defocussed', 'Motion_blurred']

# Define the transformation to be applied to each image
transform = transforms.Compose({
    transforms.PILToTensor(),
    transforms.Resize((224, 224)),
    transforms.Normalize(mean=(0, 0, 0), std=(1, 1, 1)),
    transforms.ToTensor(),

})

# Loop over each image in the test directory and predict its class
for img_name in os.listdir(test_images_dir):
if not img_name.startswith('.'):
    img_path = os.path.join(test_images_dir, img_name)
    img = Image.open(img_path)
    img = transform(img)
    img = np.array([img.numpy()])

    features = []

    image_gray = cv2.cvtColor(img[0], cv2.COLOR_RGB2GRAY)
    lap_feat = laplace(image_gray)
    sob_feat = sobel(image_gray)
    rob_feat = roberts(image_gray)
    features.extend([lap_feat.mean(), lap_feat.var(), np.amax(lap_feat),
                     sob_feat.mean(), sob_feat.var(), np.max(sob_feat),
                     rob_feat.mean(), rob_feat.var(), np.max(rob_feat)])
    features = np.array([features])
    pred = svm_model.predict(features)
    print(f"Image {img_name} is classified as {label_names[pred[0]]}")