code: def test_load_missing_dirs(self, target_dir, adult_data):
      # This test is about the case where an object has been saved to Azure
      # Directories only exist implicitly, so in a downloaded instance
      # if a manager had no outputs, then its subdirectory won't exist
      # The exception is the Explainer, which always creates a file
      # in its subdirectory
      (
          data_train,
          data_test,
          y_train,
          y_test,
          categorical_features,
          continuous_features,
          target_name,
          classes,
          feature_columns,
          feature_range_keys,
      ) = adult_data
      X_train = data_train.drop([target_name], axis=1)
  
      model = create_complex_classification_pipeline(
          X_train, y_train, continuous_features, categorical_features
      )
      rai_insights = RAIInsights(
          model,
          data_train,
          data_test,
          target_name,
          categorical_features=categorical_features,
          task_type=ModelTask.CLASSIFICATION,
      )
  
      with TemporaryDirectory() as tmpdir:
          save_1 = Path(tmpdir) / "first_save"
  
          # Save it
          rai_insights.save(save_1)
  
          # Remove the target directory
          # First make sure it's empty
          dir_to_remove = save_1 / target_dir
          assert len(list(dir_to_remove.iterdir())) == 0
          os.rmdir(dir_to_remove)
          assert not dir_to_remove.exists()
  
          # Load
          rai_2 = RAIInsights.load(save_1)
          assert rai_2 is not None
  
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 3 for calling the TestRAIInsightsSaveAndLoadScenarios obj's underlying member method code with example usage: obj.test_load_missing_dirs(target_dir, adult_data) without return types