code: def sklearn_knn_model():
      iris = datasets.load_iris()
      X = iris.data[:, :2]  # we only take the first two features.
      y = iris.target
      knn_model = knn.KNeighborsClassifier()
      knn_model.fit(X, y)
      return ModelWithData(model=knn_model, inference_data=X)
  
imports: import json
  import os
  import random
  import re
  from collections import namedtuple
  import numpy
  import pandas
  import pytest
  import sklearn.neighbors
  import yaml
  from sklearn import datasets
  import mlflow
  import mlflow.pyfunc.scoring_server
  from mlflow.exceptions import MlflowException
  from mlflow.models.wheeled_model import _WHEELS_FOLDER_NAME, WheeledModel
  from mlflow.pyfunc.model import MLMODEL_FILE_NAME, Model
  from mlflow.store.artifact.utils.models import _improper_model_uri_msg
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.environment import _CONDA_ENV_FILE_NAME, _REQUIREMENTS_FILE_NAME, _is_pip_deps, _mlflow_conda_env
  from tests.helper_functions import _is_available_on_pypi, _mlflow_major_version_string, pyfunc_serve_and_score_model
label_desc: Use image node_id 1 for calling a global function with example usage: sklearn_knn_model() and returns: ModelWithData