code: def iris_data():
      iris = sklearn.datasets.load_iris()
      x = iris.data[:, :2]
      y = iris.target
      return x, y
  
imports: import json
  import os
  import re
  import shutil
  import subprocess
  import sys
  import warnings
  from pathlib import Path
  from unittest import mock
  import numpy
  import pandas
  import pytest
  import sklearn
  import sklearn.datasets
  import sklearn.neighbors
  from click.testing import CliRunner
  import mlflow
  import mlflow.models.cli
  import mlflow.sklearn
  from mlflow.environment_variables import MLFLOW_DISABLE_ENV_MANAGER_CONDA_WARNING
  from mlflow.exceptions import MlflowException
  from mlflow.models.flavor_backend_registry import get_flavor_backend
  from mlflow.protos.databricks_pb2 import BAD_REQUEST, ErrorCode
  from mlflow.pyfunc.backend import PyFuncBackend
  from mlflow.pyfunc.scoring_server import CONTENT_TYPE_CSV, CONTENT_TYPE_JSON
  from mlflow.utils import PYTHON_VERSION
  from mlflow.utils import env_manager
  from mlflow.utils.conda import _get_conda_env_name
  from mlflow.utils.environment import _mlflow_conda_env
  from mlflow.utils.file_utils import TempDir
  from mlflow.utils.process import ShellCommandException
  from tests.helper_functions import PROTOBUF_REQUIREMENT, RestEndpoint, get_safe_port, pyfunc_build_image, pyfunc_generate_dockerfile, pyfunc_serve_and_score_model, pyfunc_serve_from_docker_image, pyfunc_serve_from_docker_image_with_env_override
label_desc: Use image node_id 2 for calling a global function with example usage: iris_data() and returns: x,  y