code: def get_expected_metric_error(
      error_analyzer, metric, model, validation_data, y_test
  ):
      if metric == Metrics.ERROR_RATE:
          return sum(model.predict(validation_data) != y_test)
      elif (
          metric == Metrics.MEAN_SQUARED_ERROR
          or metric == Metrics.MEAN_ABSOLUTE_ERROR
          or metric in precision_metrics
          or metric in recall_metrics
          or metric in f1_metrics
          or metric == Metrics.ACCURACY_SCORE
      ):
          func = metric_to_func[metric]
          pred_y = model.predict(validation_data)
          can_be_binary = (
              error_analyzer.model_task == ModelTask.CLASSIFICATION
          )
          if can_be_binary and metric != Metrics.ACCURACY_SCORE:
              ordered_labels = get_ordered_classes(
                  error_analyzer.classes, y_test, pred_y
              )
              if len(ordered_labels) == 2:
                  return func(
                      y_test, pred_y, pos_label=ordered_labels[1]
                  )
          return func(y_test, pred_y)
      else:
          raise NotImplementedError(
              "Metric {} validation not supported yet".format(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 3 for calling a global function with example usage: get_expected_metric_error(error_analyzer, metric, model, validation_data, y_test) and returns: sum, func, func