code: def _gen_data_higgs(datasets_root_dir):
      higgs_url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz"  # noqa
  
      local_url = os.path.join(
          datasets_root_dir, os.path.basename(higgs_url)
      )
  
      if not os.path.isfile(local_url):
          urlretrieve(higgs_url, local_url)
  
      col_names = ["label"] + [
          "col-{}".format(i) for i in range(2, 30)
      ]  # Assign column names
      dtypes_ls = [np.int32] + [
          np.float32 for _ in range(2, 30)
      ]  # Assign dtypes to each column
  
      df = pd.read_csv(
          local_url,
          names=col_names,
          dtype={k: v for k, v in zip(col_names, dtypes_ls)},
      )
  
      X = df[df.columns.difference(["label"])]
      y = df["label"]
  
      return X, y
  
imports: from cuml.internals.import_utils import has_scipy
  from cuml.internals.safe_imports import gpu_only_import_from
  from cuml.internals import input_utils
  from urllib.request import urlretrieve
  import sklearn.model_selection
  from sklearn.datasets import load_svmlight_file, fetch_covtype
  import cuml.datasets
  from cuml.internals.safe_imports import cpu_only_import
  import os
  import functools
  import gzip
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 11 for calling a global function with example usage: _gen_data_higgs(datasets_root_dir) and returns: X,  y