code: def test_explainer_manager_dropped_categorical_features(self):
      (
          data_train,
          data_test,
          _,
          _,
          categorical_features,
          continuous_features,
          target_name,
          classes,
          _,
          _,
      ) = create_adult_income_dataset()
  
      dropped_features = ADULT_DROPPED_FEATURES
      categorical_features_after_drop = (
          ADULT_CATEGORICAL_FEATURES_AFTER_DROP
      )
  
      X = data_train.drop([target_name] + dropped_features, axis=1)
      y = data_train[target_name]
  
      model = create_complex_classification_pipeline(
          X, y, continuous_features, categorical_features_after_drop
      )
  
      # create feature metadata
      feature_metadata = FeatureMetadata(
          dropped_features=dropped_features
      )
  
      rai_insights = RAIInsights(
          model=model,
          train=data_train,
          test=data_test,
          task_type="classification",
          target_column=target_name,
          categorical_features=categorical_features,
          classes=classes,
          feature_metadata=feature_metadata,
      )
  
      rai_insights.explainer.add()
      rai_insights.compute()
  
file_path: responsible-ai-toolbox/responsibleai/tests/explainer/test_explainer_manager.py
imports: import pandas
  import pytest
  from rai_test_utils.models.lightgbm import create_lightgbm_classifier
  from rai_test_utils.models.sklearn import create_complex_classification_pipeline
  from raiutils.exceptions import UserConfigValidationException
  from responsibleai._interfaces import ModelExplanationData
  from responsibleai.feature_metadata import FeatureMetadata
  from responsibleai.rai_insights import RAIInsights
  from ..common_utils import ADULT_CATEGORICAL_FEATURES_AFTER_DROP, ADULT_DROPPED_FEATURES, create_adult_income_dataset, create_iris_data
label_desc: Use image node_id 4 for calling the TestExplainerManager obj's underlying member method code with example usage: obj.test_explainer_manager_dropped_categorical_features() without return types