code: def run_error_analyzer(
      validation_data,
      model,
      X_test,
      y_test,
      feature_names,
      categorical_features,
      model_task,
      filters=None,
      composite_filters=None,
      is_empty_validation_data=False,
      classes=None,
  ):
      error_analyzer = ModelAnalyzer(
          model,
          X_test,
          y_test,
          feature_names,
          categorical_features,
          model_task=model_task,
          classes=classes,
      )
      filtered_data = filter_from_cohort(
          error_analyzer, filters, composite_filters
      )
  
      # validate there is some data selected for each of the filters
      if is_empty_validation_data:
          assert validation_data.shape[0] == 0
      else:
          assert validation_data.shape[0] > 0
      assert validation_data.equals(filtered_data)
  
imports: import numpy
  import pandas
  import pytest
  from erroranalysis._internal.cohort_filter import filter_from_cohort
  from erroranalysis._internal.constants import ARG, COLUMN, METHOD, PRED_Y, ROW_INDEX, TRUE_Y, ImageColumns, ModelTask
  from erroranalysis._internal.error_analyzer import ModelAnalyzer
  from rai_test_utils.datasets.tabular import create_diabetes_data, create_iris_data, create_simple_titanic_data
  from rai_test_utils.datasets.vision import load_fridge_object_detection_dataset
  from rai_test_utils.models.sklearn import create_sklearn_random_forest_regressor, create_sklearn_svm_classifier, create_titanic_pipeline
  from rai_test_utils.models.torch import get_object_detection_fridge_model
  from raiutils.cohort import CohortFilterMethods
label_desc: Use image node_id 3 for calling a global function with example usage: run_error_analyzer(validation_data, model, X_test, y_test, feature_names, categorical_features, model_task, filters, composite_filters, is_empty_validation_data, classes) without return types