code: def load_jettagging_model():
      """
      Load the 3 hidden layer QKeras example model trained on the jet tagging dataset
      """
      model_path = example_model_path / "keras/qkeras_3layer.json"
      with model_path.open("r") as f:
          jsons = f.read()
      model = model_from_json(jsons, custom_objects=co)
      model.load_weights(
          example_model_path / "keras/qkeras_3layer_weights.h5"
      )
      return model
  
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 2 for calling a global function with example usage: load_jettagging_model() and returns: model