code: def serve_and_score(model_uri, data, extra_args=None):
      resp = pyfunc_serve_and_score_model(
          model_uri,
          data=data,
          content_type=CONTENT_TYPE_JSON,
          extra_args=["--env-manager=virtualenv"] + (extra_args or []),
      )
      return pd.read_json(
          BytesIO(resp.content), orient="records"
      ).values.squeeze()
  
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 2 for calling a global function with example usage: serve_and_score(model_uri, data, extra_args) and returns: pd