code: def test_small_data_importances(
      self, num_rows, error_correlation_method
  ):
      # validate we can run on very few rows
      (
          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_classifier(X_train, y_train)
      X_test = X_test[:num_rows]
      y_test = y_test[:num_rows]
      categorical_features = []
      model_analyzer = ModelAnalyzer(
          model, X_test, y_test, feature_names, categorical_features
      )
      scores = model_analyzer.compute_importances(
          error_correlation_method
      )
      assert len(scores) == DEFAULT_SAMPLE_COLS
  
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 7 for calling the TestImportances obj's underlying member method code with example usage: obj.test_small_data_importances(num_rows, error_correlation_method) without return types