code: def model_evaluation_step(**context):
      from deepchecks.tabular.suites import model_evaluation
      from deepchecks.tabular.datasets.classification.adult import (
          _CAT_FEATURES,
          _target,
      )
      from deepchecks.tabular import Dataset
  
      adult_model = joblib.load(
          context.get("ti").xcom_pull(key="adult_model")
      )
      adult_train = pd.read_csv(
          context.get("ti").xcom_pull(key="train_path")
      )
      adult_test = pd.read_csv(
          context.get("ti").xcom_pull(key="test_path")
      )
      ds_train = Dataset(
          adult_train, label=_target, cat_features=_CAT_FEATURES
      )
      ds_test = Dataset(
          adult_test, label=_target, cat_features=_CAT_FEATURES
      )
  
      evaluation_results = model_evaluation().run(
          ds_train, ds_test, adult_model
      )
  
      run_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
      evaluation_results.save_as_html(
          os.path.join(dir_path, f"model_evaluation_{run_time}.html")
      )
  
file_path: deepchecks/examples/integrations/airflow/deepchecks_airflow_tutorial.py
imports: from datetime import datetime, timedelta
  import os
  from airflow import DAG
  from airflow.operators.python import PythonOperator
  import joblib
  import pandas
  from deepchecks.tabular.datasets.classification import adult
label_desc: Use image node_id 4 for calling a global function with example usage: model_evaluation_step() without return types