code: def test_large_data_importances(self, error_correlation_method):
      # mutual information can be very costly for large number of rows
      # hence, assert we downsample to compute importances for large data
      (
          X_train,
          y_train,
          X_test,
          y_test,
          _,
      ) = create_binary_classification_dataset(NUM_SAMPLE_ROWS)
      feature_names = list(X_train.columns)
      model = create_sklearn_random_forest_regressor(X_train, y_train)
      X_test, y_test = replicate_dataset(X_test, y_test)
      assert X_test.shape[0] > 1000000
      t0 = time.time()
      categorical_features = []
      model_analyzer = ModelAnalyzer(
          model, X_test, y_test, feature_names, categorical_features
      )
      model_analyzer.compute_importances(error_correlation_method)
      t1 = time.time()
      execution_time = t1 - t0
      print(execution_time)
      # assert we don't take too long and downsample the dataset
      # note execution time is in seconds
      # note GBM_SHAP is slower than the other methods
      if error_correlation_method == GBM_SHAP:
          assert execution_time < 35
      else:
          assert execution_time < 25
  
imports: import time
  import numpy
  import pytest
  from common_utils import replicate_dataset
  from sklearn.base import BaseEstimator
  from erroranalysis._internal.constants import ErrorCorrelationMethods, ModelTask
  from erroranalysis._internal.error_analyzer import ModelAnalyzer
  from rai_test_utils.datasets.tabular import create_binary_classification_dataset, create_cancer_data, create_housing_data, create_iris_data, create_simple_titanic_data
  from rai_test_utils.models.lightgbm import create_lightgbm_classifier
  from rai_test_utils.models.model_utils import create_models_classification, create_models_regression
  from rai_test_utils.models.sklearn import create_sklearn_random_forest_classifier, create_sklearn_random_forest_regressor, create_titanic_pipeline
label_desc: Use image node_id 6 for calling the TestImportances obj's underlying member method code with example usage: obj.test_large_data_importances(error_correlation_method) without return types