code: def make_btnn(
      test_no,
      N,
      kernel_quantizer,
      bias_quantizer,
      activation_quantizer,
      use_batchnorm,
      is_xnor,
  ):
      shape = (N,)
      model = Sequential()
      model.add(
          QDense(
              10,
              input_shape=shape,
              kernel_quantizer=kernel_quantizer,
              bias_quantizer=bias_quantizer,
              name="dense",
          )
      )
      if use_batchnorm:
          model.add(BatchNormalization(name="bn"))
      model.add(QActivation(activation=activation_quantizer))
      model.compile()
      return model, is_xnor, test_no
  
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 8 for calling a global function with example usage: make_btnn(test_no, N, kernel_quantizer, bias_quantizer, activation_quantizer, use_batchnorm, is_xnor) and returns: model,  is_xnor,  test_no