code: def test_quantizer(randX_1000_1, quantizer, backend, io_type):
      """
      Test a single quantizer as an Activation function.
      Checks the type inference through the conversion is correct without just
      using the same logic.
      """
      X = randX_1000_1
      X = (
          np.round(X * 2**10) * 2**-10
      )  # make it an exact ap_fixed<16,6>
      model = Sequential()
      model.add(
          QActivation(
              input_shape=(1,), activation=quantizer, name="quantizer"
          )
      )
      model.compile()
  
      config = hls4ml.utils.config_from_keras_model(
          model, granularity="name"
      )
      output_dir = str(
          test_root_path
          / "hls4mlprj_qkeras_quantizer_{}_{}_{}_{}_{}".format(
              quantizer.__class__.__name__,
              quantizer.bits,
              quantizer.integer,
              backend,
              io_type,
          )
      )
      hls_model = hls4ml.converters.convert_from_keras_model(
          model,
          hls_config=config,
          output_dir=output_dir,
          backend=backend,
          io_type=io_type,
      )
      hls_model.compile()
  
      y_qkeras = model.predict(X)
      y_hls4ml = hls_model.predict(X)
      # Goal is to get it passing with all equal
      np.testing.assert_array_equal(y_qkeras, y_hls4ml)
  
imports: import warnings
  from pathlib import Path
  import numpy
  import pytest
  from qkeras.qconv2d_batchnorm import QConv2DBatchnorm
  from qkeras.qconvolutional import QDepthwiseConv2D, QSeparableConv1D, QSeparableConv2D
  from qkeras.qlayers import QActivation, QDense
  from qkeras.quantizers import binary, quantized_bits, quantized_po2, quantized_relu, quantized_sigmoid, quantized_tanh, ternary
  from qkeras.utils import _add_supported_quantized_objects
  from sklearn.datasets import fetch_openml
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import LabelEncoder, StandardScaler
  from tensorflow.keras.layers import BatchNormalization, Input
  from tensorflow.keras.models import Model, Sequential, model_from_json
  from tensorflow.keras.utils import to_categorical
  import hls4ml
label_desc: Use image node_id 13 for calling a global function with example usage: test_quantizer(randX_1000_1, quantizer, backend, io_type) without return types