code: def test_restore_environment_from_conda_yaml_containing_conda_packages(
      sklearn_model,
  ):
      conda_env = {
          "name": "mlflow-env",
          "channels": ["conda-forge"],
          "dependencies": [
              "python=" + ".".join(map(str, sys.version_info[:3])),
              "conda-package=1.2.3",  # conda package
              "pip",
              {
                  "pip": [
                      "mlflow",
                      f"scikit-learn=={sklearn.__version__}",
                  ]
              },
          ],
      }
      with mlflow.start_run():
          model_info = mlflow.sklearn.log_model(
              sklearn_model.model,
              artifact_path="model",
              conda_env=conda_env,
          )
          model_artifact_path = Path(
              mlflow.get_artifact_uri("model").replace("file://", "")
          )
      model_artifact_path.joinpath(_PYTHON_ENV_FILE_NAME).unlink()
      serve_and_score(model_info.model_uri, sklearn_model.X_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 13 for calling a global function with example usage: test_restore_environment_from_conda_yaml_containing_conda_packages(sklearn_model) without return types