code: def test_misidentified_categorical_features(self):
      X_train = pd.DataFrame(
          data=[["1", 1], ["2", 3]], columns=["c1", "c2"]
      )
      y_train = np.array([1, 0])
      X_test = pd.DataFrame(
          data=[["1", 2], ["2", 3]], columns=["c1", "c2"]
      )
      y_test = np.array([1, 0])
  
      model = LGBMClassifier(
          boosting_type="gbdt",
          learning_rate=0.1,
          max_depth=5,
          n_estimators=200,
          n_jobs=1,
          random_state=777,
      )
  
      X_train[TARGET] = y_train
      X_test[TARGET] = y_test
  
      with pytest.raises(UserConfigValidationException) as ucve:
          RAIInsights(
              model=model,
              train=X_train,
              test=X_test,
              target_column=TARGET,
              task_type="classification",
          )
  
      assert (
          "The following string features were not "
          + "identified as categorical features: {'c1'}"
          in str(ucve.value)
      )
  
imports: import logging
  import random
  from unittest.mock import MagicMock
  import numpy
  import pandas
  import pytest
  from lightgbm import LGBMClassifier
  from tests.common_utils import RandomForecastingModel, RandomForecastingModelWithQuantiles, create_iris_data, create_tiny_forecasting_dataset
  from rai_test_utils.datasets.tabular import create_binary_classification_dataset, create_cancer_data, create_housing_data
  from rai_test_utils.models.lightgbm import create_lightgbm_classifier
  from rai_test_utils.models.sklearn import create_sklearn_random_forest_regressor
  from raiutils.exceptions import UserConfigValidationException
  from responsibleai import RAIInsights
  from responsibleai.feature_metadata import FeatureMetadata
  from responsibleai.rai_insights.rai_insights import _MODEL_METHOD_EXCEPTION_MESSAGE
label_desc: Use image node_id 20 for calling the TestRAIInsightsValidations obj's underlying member method code with example usage: obj.test_misidentified_categorical_features() without return types