code: def create_validation_data(X_test, y_test, pred_y=None):
      validation_data = X_test.copy()
      validation_data[TRUE_Y] = y_test
      validation_data[ROW_INDEX] = np.arange(0, len(y_test))
      if pred_y is not None:
          validation_data[PRED_Y] = pred_y
      return validation_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 2 for calling a global function with example usage: create_validation_data(X_test, y_test, pred_y) and returns: validation_data