suggestion

# Download test image.
from PIL import Image
from io import BytesIO
from IPython.display import Image as IPImage, display
from huggingface_hub import from_pretrained_keras
import tensorflow as tf

# Download sample image
!wget -nc -q https://storage.googleapis.com/dx-scin-public-data/dataset/images/3445096909671059178.png

# Load the image
img = Image.open("3445096909671059178.png")
buf = BytesIO()
img.convert('RGB').save(buf, 'PNG')
image_bytes = buf.getvalue()

# Format input
input_tensor= tf.train.Example(features=tf.train.Features(
        feature={'image/encoded': tf.train.Feature(
            bytes_list=tf.train.BytesList(value=[image_bytes]))
        })).SerializeToString()

# Load the model directly from Hugging Face Hub
loaded_model = from_pretrained_keras("google/derm-foundation")

# Call inference
infer = loaded_model.signatures["serving_default"]
output = infer(inputs=tf.constant([input_tensor]))

# Extract the embedding vector
embedding_vector = output['embedding'].numpy().flatten()