code: def toy_model_int8(**kwargs):
      img = tf.keras.layers.Input(shape=(224, 224, 3))
      x = quant(img)
      x = lq.layers.QuantConv2D(
          12,
          3,
          strides=2,
          input_quantizer="ste_sign",
          kernel_quantizer="ste_sign",
      )(x)
      x = tf.keras.layers.BatchNormalization(momentum=0.7)(x)
      x = quant(x)
      x = lq.layers.QuantConv2D(
          12,
          3,
          strides=2,
          input_quantizer="ste_sign",
          kernel_quantizer="ste_sign",
      )(x)
      x = tf.keras.layers.BatchNormalization(momentum=0.7)(x)
      x = quant(x)
      x = tf.keras.layers.GlobalAveragePooling2D()(x)
      x = quant(x)
      # We do not use a Dense layer or softmax here, because this introduces error,
      # and in this test we want to test the int8 part of our bconvs
      return tf.keras.Model(img, x)
  
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 5 for calling a global function with example usage: toy_model_int8() and returns: tf