code: def load_url_dataset_day(cache_path, days):
      """Loads url dataset from a tar file
  
      Parameters
      ----------
      cache_path : `str`
          Path to the tar file
  
      days : `list` or `range`
          Days to be loaded
  
      Returns
      -------
      X : `np.ndarray`
          A sparse matrix containing the features
  
      y : `np.ndarray`
          An array containing the labels
      """
      tar_file = tarfile.open(cache_path, "r:gz")
  
      X, y = None, None
  
      for day in days:
          data_filename = "url_svmlight/Day{}.svm".format(day)
          with tar_file.extractfile(data_filename) as data_file:
              X_day, y_day = load_svmlight_file(
                  data_file, n_features=_N_FEATURES
              )
  
          if X is None:
              X, y = X_day, y_day
          else:
              X = scipy.sparse.vstack((X, X_day))
              y = np.hstack((y, y_day))
  
      return X, y
  
file_path: tick/tick/dataset/fetch_url_dataset.py
imports: import os
  import tarfile
  import numpy
  import scipy
  from sklearn.datasets import load_svmlight_file
  from tick.dataset.download_helper import download_dataset, get_data_home
label_desc: Use image node_id 1 for calling a global function with example usage: load_url_dataset_day(cache_path, days) and returns: X,  y