text: from tvm.contrib.download import download_testdata
  from PIL import Image
  import numpy as np
  
  img_url = "https://s3.amazonaws.com/model-server/inputs/kitten.jpg"
  img_path = download_testdata(img_url, "imagenet_cat.png", module="data")
  
  # Resize it to 224x224
  resized_image = Image.open(img_path).resize((224, 224))
  img_data = np.asarray(resized_image).astype("float32")
  
  # ONNX expects NCHW input, so convert the array
  img_data = np.transpose(img_data, (2, 0, 1))
  
  # Normalize according to ImageNet
  imagenet_mean = np.array([0.485, 0.456, 0.406])
  imagenet_stddev = np.array([0.229, 0.224, 0.225])
  norm_img_data = np.zeros(img_data.shape).astype("float32")
  for i in range(img_data.shape[0]):
      norm_img_data[i, :, :] = (img_data[i, :, :] / 255 - imagenet_mean[i]) / imagenet_stddev[i]
  
  # Add batch dimension
  img_data = np.expand_dims(norm_img_data, axis=0)
  
  # Save to .npz (outputs imagenet_cat.npz)
  np.savez("imagenet_cat", data=img_data)