code: def _gen_data_airline_regression(datasets_root_dir):
      url = "http://kt.ijs.si/elena_ikonomovska/datasets/airline/airline_14col.data.bz2"
  
      local_url = os.path.join(datasets_root_dir, os.path.basename(url))
  
      cols = [
          "Year",
          "Month",
          "DayofMonth",
          "DayofWeek",
          "CRSDepTime",
          "CRSArrTime",
          "UniqueCarrier",
          "FlightNum",
          "ActualElapsedTime",
          "Origin",
          "Dest",
          "Distance",
          "Diverted",
          "ArrDelay",
      ]
      dtype = np.float64
      dtype_columns = {
          "Year": dtype,
          "Month": dtype,
          "DayofMonth": dtype,
          "DayofWeek": dtype,
          "CRSDepTime": dtype,
          "CRSArrTime": dtype,
          "FlightNum": dtype,
          "ActualElapsedTime": dtype,
          "Distance": dtype,
          "Diverted": dtype,
          "ArrDelay": dtype,
      }
  
      if not os.path.isfile(local_url):
          urlretrieve(url, local_url)
      df = pd.read_csv(local_url, names=cols, dtype=dtype_columns)
  
      # Encode categoricals as numeric
      for col in df.select_dtypes(["object"]).columns:
          df[col] = df[col].astype("category").cat.codes
  
      X = df[df.columns.difference(["ArrDelay"])]
      y = df["ArrDelay"]
  
      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 5 for calling a global function with example usage: _gen_data_airline_regression(datasets_root_dir) and returns: X,  y