code: def test_int8_input_output(
      conversion_function,
      model_cls,
      inference_input_type,
      inference_output_type,
  ):
      model_lce = conversion_function(
          model_cls(),
          inference_input_type=inference_input_type,
          inference_output_type=inference_output_type,
          experimental_default_int8_range=(-6.0, 6.0)
          if model_cls == toy_model
          else None,
      )
      interpreter = tf.lite.Interpreter(model_content=model_lce)
      input_details = interpreter.get_input_details()
      assert len(input_details) == 1
      assert (
          input_details[0]["dtype"]
          == inference_input_type.as_numpy_dtype
      )
      output_details = interpreter.get_output_details()
      assert len(output_details) == 1
      assert (
          output_details[0]["dtype"]
          == inference_output_type.as_numpy_dtype
      )
  
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 11 for calling a global function with example usage: test_int8_input_output(conversion_function, model_cls, inference_input_type, inference_output_type) without return types