code: def convert(load_jettagging_model, strategy):
      """
      Convert a QKeras model trained on the jet tagging dataset
      """
      model = load_jettagging_model
  
      config = hls4ml.utils.config_from_keras_model(
          model, granularity="name"
      )
      config["Model"]["Strategy"] = strategy
      config["LayerName"]["softmax"]["exp_table_t"] = "ap_fixed<18,8>"
      config["LayerName"]["softmax"]["inv_table_t"] = "ap_fixed<18,4>"
      hls_model = hls4ml.converters.convert_from_keras_model(
          model,
          hls_config=config,
          output_dir=str(
              test_root_path / f"hls4mlprj_qkeras_accuracy_{strategy}"
          ),
          part="xcu250-figd2104-2L-e",
      )
      hls_model.compile()
      return hls_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 3 for calling a global function with example usage: convert(load_jettagging_model, strategy) and returns: hls_model