code: def test_serve_and_score_1x_models():
      X, _ = load_iris(return_X_y=True, as_frame=True)
      X_pred = X.sample(frac=0.1, random_state=0)
      loaded_model = mlflow.pyfunc.load_model(TEST_MLFLOW_1X_MODEL_DIR)
      y_pred = loaded_model.predict(X_pred)
  
      scores = serve_and_score(TEST_MLFLOW_1X_MODEL_DIR, X_pred)
      np.testing.assert_array_almost_equal(scores, y_pred)
  
imports: import os
  import sys
  from collections import namedtuple
  from io import BytesIO
  from pathlib import Path
  from stat import S_IRGRP, S_IROTH, S_IRUSR, S_IXGRP, S_IXOTH, S_IXUSR
  import numpy
  import pandas
  import pytest
  import sklearn
  from sklearn.datasets import load_iris
  from sklearn.linear_model import LogisticRegression
  import mlflow
  from mlflow.environment_variables import MLFLOW_ENV_ROOT
  from mlflow.pyfunc.scoring_server import CONTENT_TYPE_JSON
  from mlflow.utils.environment import _PYTHON_ENV_FILE_NAME, _REQUIREMENTS_FILE_NAME
  from mlflow.utils.virtualenv import _is_pyenv_available, _is_virtualenv_available
  from tests.helper_functions import pyfunc_serve_and_score_model
label_desc: Use image node_id 6 for calling a global function with example usage: test_serve_and_score_1x_models() without return types