import mlflow
import numpy as np

model = mlflow.pyfunc.load_model('/mlruns/1/e435ee5c0c5a468c99eb43c13df4a94b/artifacts/s444421')

with open('input_example.json') as f:
    input = json.load(f)
    y_predicted = model.predict(np.array([data['inputs']]).reshape(-1, 2))
    print(y_predicted[:5])