code: def toy_model(binary_quantizer="ste_sign", **kwargs):
      def block(padding, pad_values, activation):
          def dummy(x):
              shortcut = x
              x = lq.layers.QuantConv2D(
                  filters=32,
                  kernel_size=3,
                  padding=padding,
                  pad_values=pad_values,
                  input_quantizer=binary_quantizer,
                  kernel_quantizer=binary_quantizer,
                  use_bias=False,
                  activation=activation,
              )(x)
              x = tf.keras.layers.BatchNormalization(
                  momentum=0.7, beta_initializer="random_normal"
              )(x)
              return tf.keras.layers.add([x, shortcut])
  
          return dummy
  
      img_input = tf.keras.layers.Input(shape=(224, 224, 3))
      out = img_input
      out = tf.keras.layers.Conv2D(
          filters=32, kernel_size=3, strides=2, padding="same"
      )(out)
  
      # Test zero-padding
      out = block("same", 0.0, "relu")(out)
      # Test one-padding
      out = block("same", 1.0, "relu")(out)
      # Test no activation function
      out = block("same", 1.0, None)(out)
  
      out = tf.keras.layers.GlobalAvgPool2D()(out)
      return tf.keras.Model(inputs=img_input, outputs=out)
  
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 2 for calling a global function with example usage: toy_model(binary_quantizer) and returns: tf, dummy, tf