code: def fetch_genome(
      return_X_y: bool = False, return_labels: bool = False
  ) -> Union[Bunch, tuple]:
      """
      Load genome data including their labels and whether they are outliers or not. More details about the data can be
      found in the readme on https://console.cloud.google.com/storage/browser/seldon-datasets/genome/.
      The original data can be found here: https://drive.google.com/drive/folders/1Ht9xmzyYPbDouUTl_KQdLTJQYX2CuclR.
  
      Parameters
      ----------
      return_X_y
          Bool, whether to only return the data and target values or a Bunch object.
      return_labels
          Whether to return the genome labels which are detailed in the `label_json` dict
          of the returned Bunch object.
  
      Returns
      -------
      Bunch
          Training, validation and test data, whether they are outliers and optionally including the
          genome labels which are specified in the `label_json` key as a dictionary.
      (data, outlier) or (data, outlier, target)
          Tuple for the train, validation and test set with either the data and whether they
          are outliers or the data, outlier flag and labels for the genomes if 'return_X_y' equals True.
      """
      data_train = load_genome_npz(
          "train_in", return_labels=return_labels
      )
      data_val_in = load_genome_npz(
          "val_in", return_labels=return_labels
      )
      data_val_ood = load_genome_npz(
          "val_ood", return_labels=return_labels
      )
      data_val = (
          np.concatenate([data_val_in[0], data_val_ood[0]]),
          np.concatenate([data_val_in[1], data_val_ood[1]]),
      )
      data_test_in = load_genome_npz(
          "test_in", return_labels=return_labels
      )
      data_test_ood = load_genome_npz(
          "test_ood", return_labels=return_labels
      )
      data_test = (
          np.concatenate([data_test_in[0], data_test_ood[0]]),
          np.concatenate([data_test_in[1], data_test_ood[1]]),
      )
      if return_labels:
          data_val += (np.concatenate([data_val_in[2], data_val_ood[2]]),)  # type: ignore
          data_test += (np.concatenate([data_test_in[2], data_test_ood[2]]),)  # type: ignore
      if return_X_y:
          return data_train, data_val, data_test
      try:
          resp = requests.get(
              "https://storage.googleapis.com/seldon-datasets/genome/label_dict.json",
              timeout=TIMEOUT,
          )
          resp.raise_for_status()
      except RequestException:
          logger.exception(
              "Could not connect, URL may be out of service"
          )
          raise
  
      label_dict = resp.json()
      bunch = Bunch(
          data_train=data_train[0],
          data_val=data_val[0],
          data_test=data_test[0],
          outlier_train=data_train[1],
          outlier_val=data_val[1],
          outlier_test=data_test[1],
          label_dict=label_dict,
      )
      if not return_labels:
          return bunch
      else:
          bunch["target_train"] = data_train[2]  # type: ignore
          bunch["target_val"] = data_val[2]  # type: ignore
          bunch["target_test"] = data_test[2]  # type: ignore
          return bunch
  
imports: import io
  import logging
  from io import BytesIO
  from typing import List, Tuple, Type, Union
  from xml.etree import ElementTree
  import dill
  import numpy
  import pandas
  import requests
  from alibi_detect.utils.data import Bunch
  from alibi_detect.utils.url import _join_url
  from requests import RequestException
  from urllib.error import URLError
  from scipy.io import arff
  from sklearn.datasets import fetch_kddcup99
label_desc: Use image node_id 11 for calling a global function with example usage: fetch_genome(return_X_y, return_labels) and returns: data_train,  data_val,  data_test, bunch, bunch