code: def preprocess(data):
      image = data["image"]
      if len(image.shape) != 3:
          raise ValueError("Input must be of size [height, width, C>0]")
      result = preprocess_image_tensor(
          tf.convert_to_tensor(image), image_size=IMAGE_SIZE
      )
      if isinstance(image, np.ndarray):
          return tf.keras.backend.get_value(result)
      return result, data["label"]
  
imports: import functools
  import os
  import sys
  import tempfile
  import larq
  import numpy
  import pytest
  import tensorflow
  import tensorflow_datasets
  from larq_compute_engine.mlir.python.converter import convert_keras_model, convert_saved_model
  from larq_compute_engine.tflite.python.interpreter import Interpreter
  from preprocess import preprocess_image_tensor, IMAGE_SIZE
label_desc: Use image node_id 6 for calling a global function with example usage: preprocess(data) and returns: result,  data, tf