code: def test_qactivation_kwarg(
      randX_100_10, activation_quantizer, weight_quantizer
  ):
      if activation_quantizer in ["binary", "ternary"]:
          name = "bnbt_qdense_alpha"
      else:
          name = (
              f"qdense_{eval(activation_quantizer).__class__.__name__}"
          )
  
      inputs = Input(shape=(10,))
  
      outputs = QDense(
          10,
          activation=activation_quantizer,
          name="qdense",
          kernel_quantizer=weight_quantizer,
          bias_quantizer=weight_quantizer,
          kernel_initializer="lecun_uniform",
      )(inputs)
      model = Model(inputs, outputs)
  
      config = hls4ml.utils.config_from_keras_model(
          model, granularity="name"
      )
  
      out_dir = str(
          test_root_path
          / f"hls4mlprj_qactivation_kwarg_{activation_quantizer}"
      )
  
      hls_model = hls4ml.converters.convert_from_keras_model(
          model, hls_config=config, output_dir=out_dir
      )
      hls_model.compile()
  
      # Verify if activation in hls_model
      assert name in [layer.name for layer in hls_model.get_layers()]
  
      # Output tests
      X = randX_100_10
      X = np.round(X * 2**10) * 2**-10
      y_qkeras = model.predict(X)
      y_hls4ml = hls_model.predict(X)
      if hasattr(eval(activation_quantizer), "bits"):
          np.testing.assert_allclose(
              y_qkeras.ravel(),
              y_hls4ml.ravel(),
              atol=2 ** -(eval(activation_quantizer).bits - 1),
              rtol=1.0,
          )
      else:
          if activation_quantizer == "binary":
              y_hls4ml = np.where(y_hls4ml == 0, -1, 1)
          wrong = (y_hls4ml != y_qkeras).ravel()
          assert sum(wrong) / len(wrong) <= 0.005
  
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 14 for calling a global function with example usage: test_qactivation_kwarg(randX_100_10, activation_quantizer, weight_quantizer) without return types