code: def validation_dataset(mnist_dataset, num_classes):
      (
          train_images,
          train_labels,
          test_images,
          test_labels,
      ) = mnist_dataset
      TARGET_CLASS = np.random.choice(num_classes, 1)[0]
      ONE_HOT_TARGET_CLASS = np.array(
          [int(el == TARGET_CLASS) for el in range(num_classes)]
      )
  
      validation_target_class = (
          np.array(
              [
                  el
                  for el, label in zip(test_images, test_labels)
                  if np.all(label == ONE_HOT_TARGET_CLASS)
              ]
          ),
          None,
      )
  
      return validation_target_class, TARGET_CLASS
  
imports: import numpy
  import pytest
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: validation_dataset(mnist_dataset, num_classes) and returns: validation_target_class,  TARGET_CLASS