code: def test_validate_test_data_size(self):
      X_train, X_test, y_train, y_test, _, _ = create_iris_data()
  
      model = create_lightgbm_classifier(X_train, y_train)
      X_train[TARGET] = y_train
      X_test[TARGET] = y_test
      length = len(y_test)
      with pytest.warns(
          UserWarning,
          match=f"The size of the test set {length} is greater than "
          f"the supported limit of {length - 1}. Computing "
          f"insights for the first {length - 1} samples of the "
          "test set",
      ):
          RAIInsights(
              model=model,
              train=X_train,
              test=X_test,
              target_column=TARGET,
              task_type="classification",
              maximum_rows_for_test=len(y_test) - 1,
          )
  
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 2 for calling the TestRAIInsightsValidations obj's underlying member method code with example usage: obj.test_validate_test_data_size() without return types