# Path to trained model file
model_path = 'path/to/property_model.qatkpt'

# Set up the PropertyPredictor on a GPU with float32 precision
predictor = PropertyPredictor(model_path, device='cuda', dtype='float32')

# Get information about what property the model predicts
property_name = predictor.propertyName()
property_unit = predictor.propertyUnit()

print(f"Model predicts: {property_name}")
if property_unit:
    print(f"Unit: {property_unit}")

configuration = ...  # Load or define your atomic configuration here

# Predict property for a given atomic configuration
result = predictor.predict(configuration)
print("Predicted property value:", result)