code: def test_single_dense_activation_exact(
      randX_100_16, bits, alpha, backend, io_type
  ):
      """
      Test a single Dense -> Activation layer topology for
      bit exactness with number of bits parameter
      """
      X = randX_100_16
      model = Sequential()
      model.add(
          QDense(
              16,
              input_shape=(16,),
              name="fc1",
              kernel_quantizer=quantized_bits(bits, 0, alpha=alpha),
              bias_quantizer=quantized_bits(bits, 0, alpha=1),
              kernel_initializer="lecun_uniform",
          )
      )
      model.add(
          QActivation(activation=quantized_relu(bits, 0), name="relu1")
      )
      model.compile()
  
      config = hls4ml.utils.config_from_keras_model(
          model, granularity="name"
      )
      output_dir = str(
          test_root_path
          / f"hls4mlprj_qkeras_single_dense_activation_exact_{bits}_{alpha}_{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)
      # For now allow matching within 1 bit
      np.testing.assert_allclose(
          y_qkeras.ravel(), y_hls4ml.ravel(), atol=2**-bits, rtol=1.0
      )
  
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 7 for calling a global function with example usage: test_single_dense_activation_exact(randX_100_16, bits, alpha, backend, io_type) without return types