code: def test_loading_rai_insights_without_model_file(self):
      (
          X_train,
          X_test,
          y_train,
          y_test,
          feature_names,
          classes,
      ) = create_iris_data()
      model = create_lightgbm_classifier(X_train, y_train)
      X_train["target"] = y_train
      X_test["target"] = y_test
  
      rai_insights = RAIInsights(
          model=model,
          train=X_train,
          test=X_test,
          target_column="target",
          task_type="classification",
      )
  
      with TemporaryDirectory() as tmpdir:
          assert rai_insights.model is not None
          save_path = Path(tmpdir) / "rai_insights"
          rai_insights.save(save_path)
  
          # Remove the model.pkl file to cause an exception to occur
          # while loading the model.
          model_pkl_path = (
              Path(tmpdir)
              / "rai_insights"
              / SerializationAttributes.MODEL_PKL
          )
          os.remove(model_pkl_path)
          with pytest.raises(Exception):
              RAIInsights.load(save_path)
  
imports: import os
  from pathlib import Path
  from tempfile import TemporaryDirectory
  import numpy
  import pandas
  import pytest
  from tests.common_utils import ADULT_CATEGORICAL_FEATURES_AFTER_DROP, ADULT_DROPPED_FEATURES, create_iris_data
  from rai_test_utils.datasets.tabular import create_binary_classification_dataset
  from rai_test_utils.models.lightgbm import create_lightgbm_classifier
  from rai_test_utils.models.sklearn import create_complex_classification_pipeline
  from raiutils.models import ModelTask
  from responsibleai import RAIInsights
  from responsibleai._internal.constants import ManagerNames, SerializationAttributes
  from responsibleai.feature_metadata import FeatureMetadata
label_desc: Use image node_id 4 for calling the TestRAIInsightsSaveAndLoadScenarios obj's underlying member method code with example usage: obj.test_loading_rai_insights_without_model_file() without return types