code: def get_data() -> Dict[Text, Any]:
      """Return 320 examples from Cats vs Dogs dataset.
  
      Returns:
        Dictionary containing examples and labels for 'cat', 'dog', and 'combined'.
      """
      validation_ds = tfds.load(
          "cats_vs_dogs",
          split="train[:5%]",
          as_supervised=True,  # Include labels
      )
      validation_ds_resized = validation_ds.map(
          lambda x, y: (tf.image.resize(x, (IMAGE_SIZE, IMAGE_SIZE)), y)
      )
      validation_ds_performant = (
          validation_ds_resized.cache()
          .batch(BATCH_SIZE)
          .prefetch(buffer_size=10)
      )
      validation_numpy = iter(tfds.as_numpy(validation_ds_performant))
  
      # Each batch is 32 examples.
      # We create a validation set of 320 examples by taking the first ten batches.
      validation_data = {
          "combined": {"examples": [], "labels": []},
          "cat": {"examples": [], "labels": []},
          "dog": {"examples": [], "labels": []},
      }
      for _ in range(NUM_BATCHES):
          examples, labels = next(validation_numpy)
          for example, label in zip(examples, labels):
              validation_data["combined"]["examples"].append(example)
              validation_data["combined"]["labels"].append(label)
              if label == 1:
                  validation_data["cat"]["examples"].append(example)
                  validation_data["cat"]["labels"].append(label)
              elif label == 0:
                  validation_data["dog"]["examples"].append(example)
                  validation_data["dog"]["labels"].append(label)
  
      return validation_data
  
imports: from typing import Any, Dict, Text
  import tensorflow
  import tensorflow_datasets
label_desc: Use image node_id 1 for calling a global function with example usage: get_data() and returns: validation_data