code: def toy_model_sequential(binary_quantizer="ste_sign", **kwargs):
      return tf.keras.models.Sequential(
          [
              tf.keras.layers.Input((224, 224, 3)),
              tf.keras.layers.Conv2D(
                  16, (3, 3), strides=(2, 2), padding="same"
              ),
              tf.keras.layers.BatchNormalization(momentum=0.7),
              # This will be converted to a float->bitpacked binary max pool.
              tf.keras.layers.MaxPooling2D((2, 2)),
              lq.layers.QuantConv2D(
                  32,
                  (3, 3),
                  input_quantizer=binary_quantizer,
                  kernel_quantizer=binary_quantizer,
                  padding="same",
                  pad_values=1.0,
                  use_bias=False,
              ),
              tf.keras.layers.BatchNormalization(
                  momentum=0.7,
                  # Use a Gamma initialiser with mean -0.1 to test the negative
                  # multipliers corner-case.
                  gamma_initializer=tf.keras.initializers.RandomNormal(
                      mean=-0.1
                  ),
              ),
              lq.layers.QuantConv2D(
                  32,
                  (3, 3),
                  input_quantizer=binary_quantizer,
                  kernel_quantizer=binary_quantizer,
                  strides=(2, 2),
                  padding="same",
                  pad_values=1.0,
                  use_bias=False,
              ),
              tf.keras.layers.BatchNormalization(
                  momentum=0.7,
                  # Use a Gamma initialiser with mean -0.1 to test the negative
                  # multipliers corner-case.
                  gamma_initializer=tf.keras.initializers.RandomNormal(
                      mean=-0.1
                  ),
              ),
              # This will be converted to a bitpacked->bitpacked binary max pool.
              # Test some funky filter/stride combination.
              tf.keras.layers.MaxPooling2D((3, 2), strides=(1, 2)),
              lq.layers.QuantConv2D(
                  32,
                  (3, 3),
                  input_quantizer=binary_quantizer,
                  kernel_quantizer=binary_quantizer,
                  padding="same",
                  pad_values=1.0,
                  use_bias=False,
              ),
              tf.keras.layers.BatchNormalization(momentum=0.7),
              tf.keras.layers.GlobalAvgPool2D(),
          ]
      )
  
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 3 for calling a global function with example usage: toy_model_sequential(binary_quantizer) and returns: tf