code: def get_jettagging_data():
      """
      Download the jet tagging dataset
      """
      print("Fetching data from openml")
      data = fetch_openml("hls4ml_lhc_jets_hlf")
      X, y = data["data"], data["target"]
      le = LabelEncoder()
      y = le.fit_transform(y)
      y = to_categorical(y, 5)
      X_train_val, X_test, y_train_val, y_test = train_test_split(
          X, y, test_size=0.2, random_state=42
      )
      scaler = StandardScaler()
      X_train_val = scaler.fit_transform(X_train_val)
      X_test = scaler.transform(X_test)
      return X_train_val, X_test, y_train_val, y_test
  
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 1 for calling a global function with example usage: get_jettagging_data() and returns: X_train_val,  X_test,  y_train_val,  y_test