code: def run_error_analyzer_on_models(
      X_train,
      y_train,
      X_test,
      y_test,
      feature_names,
      model_task,
      filters=None,
      composite_filters=None,
      matrix_features=None,
      quantile_binning=False,
      num_bins=BIN_THRESHOLD,
      metric=None,
  ):
      if model_task == ModelTask.CLASSIFICATION:
          models = create_models_classification(X_train, y_train)
      else:
          models = create_models_regression(X_train, y_train)
  
      for model in models:
          categorical_features = []
          run_error_analyzer(
              model,
              X_test,
              y_test,
              feature_names,
              categorical_features,
              model_task=model_task,
              filters=filters,
              composite_filters=composite_filters,
              matrix_features=matrix_features,
              quantile_binning=quantile_binning,
              num_bins=num_bins,
              metric=metric,
          )
  
imports: import numpy
  import pandas
  import pytest
  from erroranalysis._internal.cohort_filter import filter_from_cohort
  from erroranalysis._internal.constants import ARG, COLUMN, METHOD, ROW_INDEX, TRUE_Y, MatrixParams, Metrics, ModelTask, f1_metrics, metric_to_display_name, precision_metrics, recall_metrics
  from erroranalysis._internal.error_analyzer import ModelAnalyzer
  from erroranalysis._internal.matrix_filter import CATEGORY1, CATEGORY2, COUNT, FALSE_COUNT, FN, FP, INTERVAL_MAX, INTERVAL_MIN, MATRIX, METRIC_NAME, METRIC_VALUE, TN, TP, VALUES, bin_data
  from erroranalysis._internal.metrics import get_ordered_classes, is_multi_agg_metric, metric_to_func
  from rai_test_utils.datasets.tabular import create_adult_census_data, create_binary_classification_dataset, create_cancer_data, create_diabetes_data, create_housing_data, create_iris_data, create_simple_titanic_data, create_wine_data
  from rai_test_utils.models.model_utils import create_models_classification, create_models_regression
  from rai_test_utils.models.sklearn import create_kneighbors_classifier, create_titanic_pipeline
  from raiutils.exceptions import UserConfigValidationException
label_desc: Use image node_id 1 for calling a global function with example usage: run_error_analyzer_on_models(X_train, y_train, X_test, y_test, feature_names, model_task, filters, composite_filters, matrix_features, quantile_binning, num_bins, metric) without return types